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1
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Kendell AE, Yost S, Yang K, Mills M, Winkler N. Prevalence of Axillary Arch Variant Anatomy on Breast MRI and Impact on Axillary Lymph Node Assessment. JOURNAL OF BREAST IMAGING 2025:wbaf023. [PMID: 40393942 DOI: 10.1093/jbi/wbaf023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2024] [Indexed: 05/22/2025]
Abstract
OBJECTIVE To assess the prevalence of the axillary arch (AA) variant and its impact on the sensitivity of US vs MRI for detecting axillary nodal metastases in patients with breast cancer. METHODS The IRB waved informed consent for this retrospective study. Three hundred, eighty-two breast MRIs performed for the extent of disease of breast cancer between 2012 and 2023 were reviewed for the presence of AA. Pre-MRI axillary US was available in 322 of these cases. The presence of axillary adenopathy was documented and correlated with pathology. A paired sample comparison was used to calculate sensitivities of US and MRI for detection of the AA. RESULTS The AA was detected on breast MRI in 6.8% (26/382) of patients. Of these, 30.8% (8/26) were bilateral, 50% (13/26) were unilateral left, and 19.2% (5/26) were unilateral right. All had lymph nodes both superficial and deep to the AA. Of the 26 patients with AA, 19 patients underwent pre-MRI axillary US. Biopsy-proven malignant adenopathy was detected on MRI but missed on US in 10.5% (2/19) of patients with AA but only 2.5% (7/279) of patients without AA. Sensitivity for detection of lymphadenopathy on MRI and US was 69.7% (82/122)% and 67.5% (77/114) for all patients (P = .17), 72.2% (78/108) and 68.5% (74/108) for patients without AA (P = .21), and 66.2% (4/6) and 50.0% (3/6) for patients with AA. The small sample size of patients with AA precluded statistical comparison. CONCLUSION The AA is a common variant detectable on breast MRI. Axillary nodal metastases may reduce US sensitivity for identifying nodal metastases. Further investigation is required to establish statistical significance.
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Affiliation(s)
| | - Samantha Yost
- Radiology Department, University of Utah, Salt Lake City, UT, USA
| | - Kristie Yang
- Radiology Department, University of Utah, Salt Lake City, UT, USA
| | - Megan Mills
- Radiology Department, University of Utah, Salt Lake City, UT, USA
| | - Nicole Winkler
- Radiology Department, University of Utah, Salt Lake City, UT, USA
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Wu T, Long Q, Zeng L, Zhu J, Gao H, Deng Y, Han Y, Qu L, Yi W. Axillary lymph node metastasis in breast cancer: from historical axillary surgery to updated advances in the preoperative diagnosis and axillary management. BMC Surg 2025; 25:81. [PMID: 40016717 PMCID: PMC11869450 DOI: 10.1186/s12893-025-02802-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2024] [Accepted: 02/07/2025] [Indexed: 03/01/2025] Open
Abstract
Axillary lymph node status, which was routinely assessed by axillary lymph node dissection (ALND) until the 1990s, is a crucial factor in determining the stage, prognosis, and therapeutic strategy used for breast cancer patients. Axillary surgery for breast cancer patients has evolved from ALND to minimally invasive approaches. Over the decades, the application of noninvasive imaging techniques, machine learning approaches and emerging clinical prediction models for the detection of axillary lymph node metastasis greatly improves clinical diagnostic efficacy and provides optimal surgical selection. In this work, we summarize the historical axillary surgery and updated perspectives of axillary management for breast cancer patients.
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Affiliation(s)
- Tong Wu
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Clinical Research Center for Breast Disease in Hunan Province, Changsha, 410011, China
| | - Qian Long
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Clinical Research Center for Breast Disease in Hunan Province, Changsha, 410011, China
| | - Liyun Zeng
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Clinical Research Center for Breast Disease in Hunan Province, Changsha, 410011, China
| | - Jinfeng Zhu
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Clinical Research Center for Breast Disease in Hunan Province, Changsha, 410011, China
| | - Hongyu Gao
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Clinical Research Center for Breast Disease in Hunan Province, Changsha, 410011, China
| | - Yueqiong Deng
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Clinical Research Center for Breast Disease in Hunan Province, Changsha, 410011, China
| | - Yi Han
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Clinical Research Center for Breast Disease in Hunan Province, Changsha, 410011, China
| | - Limeng Qu
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
- Clinical Research Center for Breast Disease in Hunan Province, Changsha, 410011, China.
| | - Wenjun Yi
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
- Clinical Research Center for Breast Disease in Hunan Province, Changsha, 410011, China.
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Ling S, Yang H, Wu F, Yang X, Li T, Zhang Y, Jiang J, Li C, Wang Q. Rapid, Non-Invasive, Accurate Diagnosis and Efficient Clearance of Metastatic Lymph Nodes. Angew Chem Int Ed Engl 2025; 64:e202419988. [PMID: 39557612 DOI: 10.1002/anie.202419988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2024] [Revised: 11/18/2024] [Accepted: 11/18/2024] [Indexed: 11/20/2024]
Abstract
Sentinel lymph node (SLN) biopsy is currently the standard procedure for clinical cancer diagnosis and treatment, but still faces the risks of false negatives and tumor metastasis, as well as time-consuming pathological evaluation procedure. Herein, we proposed a near-infrared-II (NIR-II, 1000-1700 nm) theranostic nanosystem (FLAGC) for rapid, non-invasive, accurate diagnosis and efficient clearance of metastatic lymph nodes in breast cancer. Initialized by chlorin e6 (Ce6), a pH-sensitive amphiphilic amino acid fluorenylmethoxycarbonyl-L-histidine (Fmoc-His) was assembled with Gd3+, luminol, and AgAuSe quantum dots (AAS QDs) to form FLAGC. In FLAGC, luminol and AAS QDs form a NIR-II chemical resonance energy transfer (CRET) system (Luminol-AAS); Ce6 initiates the assembly and also serves as a photosensitizer. Upon subcutaneous injection, FLAGC is easily drained into SLNs, achieving their precise localization. Subsequently, the acidity of tumor microenvironment triggers the rapid disassembly of FLAGC, exposing Luminol-AAS. myeloperoxidase (MPO) secreted by tumor-associated macrophages and neutrophils in SLNs mediates the oxidation of luminol, lighting up AAS QDs through the CRET process for precise diagnosis of metastatic lymph nodes. Moreover, highly efficient clearance of positive lymph nodes is achieved through Ce6-mediated photodynamic therapy. Our strategy provides a new paradigm for identifying and eliminating clinically metastatic lymph nodes.
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Grants
- 21934007, 22127808, 22174158, 22271308 National Natural Science Foundation of China
- 2021YFF0701804 National Key Research and Development Program of China
- YJKYYQ20200036 Research instrument and equipment development project
- ZDBS-LYSLH021 Key Research Program of Frontier Sciences,CAS
- BE2022753,BK20222016,BK20232046,BE2022745,BK20210128,BK20200254 Natural Science Foundation of Jiangsu Province
- SJC2022001, SZS201904, SZS2023006, ZXT2022007 Science and Technology Foundation of Suzhou
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Affiliation(s)
- Sisi Ling
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Key Laboratory of Functional Molecular Imaging Technology, Division of Nanobiomedicine andi-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Hongchao Yang
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Key Laboratory of Functional Molecular Imaging Technology, Division of Nanobiomedicine andi-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Feng Wu
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Key Laboratory of Functional Molecular Imaging Technology, Division of Nanobiomedicine andi-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Xiaohu Yang
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Key Laboratory of Functional Molecular Imaging Technology, Division of Nanobiomedicine andi-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Tuanwei Li
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Key Laboratory of Functional Molecular Imaging Technology, Division of Nanobiomedicine andi-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Yejun Zhang
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Key Laboratory of Functional Molecular Imaging Technology, Division of Nanobiomedicine andi-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Jiang Jiang
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Key Laboratory of Functional Molecular Imaging Technology, Division of Nanobiomedicine andi-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Chunyan Li
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Key Laboratory of Functional Molecular Imaging Technology, Division of Nanobiomedicine andi-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Qiangbin Wang
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Key Laboratory of Functional Molecular Imaging Technology, Division of Nanobiomedicine andi-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China
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Pintican R, Fechete R, Radutiu DI, Lenghel M, Bene I, Solomon C, Ciortea C, Ciurea A. Predicting Axillary Metastasis of Breast Cancer Patients with MRI Relaxometry. Diagnostics (Basel) 2025; 15:188. [PMID: 39857072 PMCID: PMC11765026 DOI: 10.3390/diagnostics15020188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2024] [Revised: 01/04/2025] [Accepted: 01/13/2025] [Indexed: 01/27/2025] Open
Abstract
Background: Breast cancer is a leading cause of cancer-related mortality among women worldwide. Accurate staging, including the detection of axillary metastases, is vital for treatment planning. This study evaluates the efficacy of MRI relaxometry as a diagnostic tool for axillary lymph node metastases in breast cancer patients. Methods: A prospective study was conducted on 67 consecutive breast cancer patients. Relaxometry parameters, including T2Max, T2Min, and 1HAv, were assessed using 1.5 Tesla MRI. All axillary metastases were histologically confirmed using core-needle biopsy or surgical specimens. Statistical analyses included ROC curves, chi-square tests, and multivariate analysis to determine correlations between imaging findings and pathological results. Results: Significant associations were found between T2Min-ipsilateral (p = 0.018), 1HAv-ipsilateral (p = 0.003), and axillary metastases. ROC analysis demonstrated that T2Min-ipsilateral and 1HAv-ipsilateral have modest to acceptable discriminatory abilities (AUC = 0.681 and AUC = 0.740, respectively). Combined clinical and imaging models enhanced diagnostic accuracy (AUC = 0.749). Conclusions: MRI relaxometry improves the detection of axillary metastases in breast cancer, particularly when integrated with clinical and pathological evaluations.
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Affiliation(s)
- Roxana Pintican
- Department of Radiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania; (D.I.R.); (M.L.); (I.B.); (C.S.); (C.C.); (A.C.)
- Department of Radiology, Prof. Dr. Ion Chiricuta Oncology Institute, 400015 Cluj-Napoca, Romania
| | - Radu Fechete
- Institute for Interdisciplinary Research in Bio-Nano-Science, Babes-Bolyai University, INSPIRE Platform, 400347 Cluj-Napoca, Romania
- Faculty of Material and Environmental Engineering, Physics and Chemistry Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Delia Ioana Radutiu
- Department of Radiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania; (D.I.R.); (M.L.); (I.B.); (C.S.); (C.C.); (A.C.)
| | - Manuela Lenghel
- Department of Radiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania; (D.I.R.); (M.L.); (I.B.); (C.S.); (C.C.); (A.C.)
- Department of Radiology, County Emergency Hospital, 400347 Cluj-Napoca, Romania
| | - Ioana Bene
- Department of Radiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania; (D.I.R.); (M.L.); (I.B.); (C.S.); (C.C.); (A.C.)
- Department of Radiology, County Emergency Hospital, 400347 Cluj-Napoca, Romania
| | - Carolina Solomon
- Department of Radiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania; (D.I.R.); (M.L.); (I.B.); (C.S.); (C.C.); (A.C.)
- Department of Radiology, County Emergency Hospital, 400347 Cluj-Napoca, Romania
| | - Cristiana Ciortea
- Department of Radiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania; (D.I.R.); (M.L.); (I.B.); (C.S.); (C.C.); (A.C.)
- Department of Radiology, County Emergency Hospital, 400347 Cluj-Napoca, Romania
| | - Anca Ciurea
- Department of Radiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania; (D.I.R.); (M.L.); (I.B.); (C.S.); (C.C.); (A.C.)
- Department of Radiology, County Emergency Hospital, 400347 Cluj-Napoca, Romania
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Gong C, Wu Y, Zhang G, Liu X, Zhu X, Cai N, Li J. Computer-assisted diagnosis for axillary lymph node metastasis of early breast cancer based on transformer with dual-modal adaptive mid-term fusion using ultrasound elastography. Comput Med Imaging Graph 2025; 119:102472. [PMID: 39612691 DOI: 10.1016/j.compmedimag.2024.102472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 07/27/2024] [Accepted: 11/14/2024] [Indexed: 12/01/2024]
Abstract
Accurate preoperative qualitative assessment of axillary lymph node metastasis (ALNM) in early breast cancer patients is crucial for precise clinical staging and selection of axillary treatment strategies. Although previous studies have introduced artificial intelligence (AI) to enhance the assessment performance of ALNM, they all focus on the prediction performances of their AI models and neglect the clinical assistance to the radiologists, which brings some issues to the clinical practice. To this end, we propose a human-AI collaboration strategy for ALNM diagnosis of early breast cancer, in which a novel deep learning framework, termed DAMF-former, is designed to assist radiologists in evaluating ALNM. Specifically, the DAMF-former focuses on the axillary region rather than the primary tumor area in previous studies. To mimic the radiologists' alternative integration of the UE images of the target axillary lymph nodes for comprehensive analysis, adaptive mid-term fusion is proposed to alternatively extract and adaptively fuse the high-level features from the dual-modal UE images (i.e., B-mode ultrasound and Shear Wave Elastography). To further improve the diagnostic outcome of the DAMF-former, an adaptive Youden index scheme is proposed to deal with the fully fused dual-modal UE image features at the end of the framework, which can balance the diagnostic performance in terms of sensitivity and specificity. The clinical experiment indicates that the designed DAMF-former can assist and improve the diagnostic abilities of less-experienced radiologists for ALNM. Especially, the junior radiologists can significantly improve the diagnostic outcome from 0.807 AUC [95% CI: 0.781, 0.830] to 0.883 AUC [95% CI: 0.861, 0.902] (P-value <0.0001). Moreover, there are great agreements among radiologists of different levels when assisted by the DAMF-former (Kappa value ranging from 0.805 to 0.895; P-value <0.0001), suggesting that less-experienced radiologists can potentially achieve a diagnostic level similar to that of experienced radiologists through human-AI collaboration. This study explores a potential solution to human-AI collaboration for ALNM diagnosis based on UE images.
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Affiliation(s)
- Chihao Gong
- School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Yinglan Wu
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, PR China
| | - Guangyuan Zhang
- School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Xuan Liu
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, PR China
| | - Xiaoyao Zhu
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, PR China
| | - Nian Cai
- School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
| | - Jian Li
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, PR China.
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Zeng F, Cai W, Lin L, Chen C, Tang X, Yang Z, Chen Y, Chen L, Chen L, Li J, Chen S, Wang C, Xue Y. Development of a Preoperative Prediction Model Based on Spectral CT to Evaluate Axillary Lymph Node With Macrometastases in Clinical T1/2N0 Invasive Breast Cancer. Clin Breast Cancer 2025; 25:e10-e21.e1. [PMID: 39030158 DOI: 10.1016/j.clbc.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 06/07/2024] [Accepted: 06/13/2024] [Indexed: 07/21/2024]
Abstract
OBJECTIVES To develop a prediction model based on spectral computed tomography (CT) to evaluate axillary lymph node (ALN) with macrometastases in clinical T1/2N0 invasive breast cancer. METHODS A total of 217 clinical T1/2N0 invasive breast cancer patients who underwent spectral CT scans were retrospectively enrolled and categorized into a training cohort (n = 151) and validation cohort (n = 66). These patients were classified into ALN nonmacrometastases (stage pN0 or pN0 [i+] or pN1mi) and ALN macrometastases (stage pN1-3) subgroups. The morphologic criteria and quantitative spectral CT parameters of the most suspicious ALN were measured and compared. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the models. RESULTS The combined arterial-venous phase spectral CT model yielded the best diagnostic performance in discrimination of ALN nonmacrometastases and ALN macrometastases with the highest AUC (0.963 in the training cohort and 0.945 in validation cohorts). Among single phase spectral CT models, the venous phase spectral CT model showed the best performance (AUC = 0.960 in the training cohort and 0.940 in validation cohorts). There was no significant difference in AUCs among the 3 models (DeLong test, P > .05 for each comparison). CONCLUSION A Lasso-logistic model that combined morphologic features and quantitative spectral CT parameters based on contrast-enhanced spectral imaging potentially be used as a noninvasive tool for individual preoperative prediction of ALN status in clinical T1/2N0 invasive breast cancers.
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Affiliation(s)
- Fang Zeng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Weifeng Cai
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Lin Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Cong Chen
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Xiaoxue Tang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Zheting Yang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Yilin Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Lihong Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Lili Chen
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Jing Li
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Suping Chen
- GE Healthcare, Changsha, Hunan Province, China
| | - Chuang Wang
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province, China.
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors, Fujian Medical University, Fuzhou, Fujian Province, China.
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Bahl M, Chang JM, Mullen LA, Berg WA. Artificial Intelligence for Breast Ultrasound: AJR Expert Panel Narrative Review. AJR Am J Roentgenol 2024; 223:e2330645. [PMID: 38353449 DOI: 10.2214/ajr.23.30645] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2024]
Abstract
Breast ultrasound is used in a wide variety of clinical scenarios, including both diagnostic and screening applications. Limitations of ultrasound, however, include its low specificity and, for automated breast ultrasound screening, the time necessary to review whole-breast ultrasound images. As of this writing, four AI tools that are approved or cleared by the FDA address these limitations. Current tools, which are intended to provide decision support for lesion classification and/or detection, have been shown to increase specificity among nonspecialists and to decrease interpretation times. Potential future applications include triage of patients with palpable masses in low-resource settings, preoperative prediction of axillary lymph node metastasis, and preoperative prediction of neoadjuvant chemotherapy response. Challenges in the development and clinical deployment of AI for ultrasound include the limited availability of curated training datasets compared with mammography, the high variability in ultrasound image acquisition due to equipment- and operator-related factors (which may limit algorithm generalizability), and the lack of postimplementation evaluation studies. Furthermore, current AI tools for lesion classification were developed based on 2D data, but diagnostic accuracy could potentially be improved if multimodal ultrasound data were used, such as color Doppler, elastography, cine clips, and 3D imaging.
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Affiliation(s)
- Manisha Bahl
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA 02114
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Lisa A Mullen
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD
| | - Wendie A Berg
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA
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Grant K, Po XY, Tiong L. Is routine axillary staging still required in clinically node negative early breast cancer in women over 74 years? ANZ J Surg 2024; 94:2159-2164. [PMID: 39601442 DOI: 10.1111/ans.19313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 09/19/2024] [Accepted: 11/06/2024] [Indexed: 11/29/2024]
Abstract
BACKGROUND Investigate incidence and identify predictors of axillary lymph node metastases in early breast cancer in women >74 years Australia and New Zealand to inform decision making about sentinel lymph node (SLN) biopsy in this population. METHODS Retrospective review of invasive breast cancer in women in Australia and New Zealand between 2010 and 2022 using BreastSurgANZ Quality Audit Database. Data included patient demographics, tumour characteristics, surgery type, axillary nodal status and adjuvant therapy. Descriptive analysis of incidence of axillary nodal metastases and use of adjuvant therapy in various patient and tumour groups was performed, followed by statistical analysis using multivariate logistic regression to identify predictors of axillary nodal positivity and correlation between nodal status and prescription of adjuvant therapy. RESULTS Review of 127 436 cases of invasive breast cancer, 17 599 cases >74 years. Two thirds of the overall population and in those >74 years were node negative. In patients >74 years with grade 1-2, T1a-b cancers, ER+/HER2- 94% were node negative. Patient age, tumour size, grade and biomarker profile correlated with axillary nodal status and analysis of adjuvant therapy revealed significant correlation between nodal stage and adjuvant radiotherapy, chemotherapy and endocrine therapy. CONCLUSION A total of 94% of patients >74 years with T1a/b, ER positive HER2 negative breast cancer were node negative. Nodal status significantly influences adjuvant treatment in this patient group and therefore, we recommend clinicians consider tumour factors and patient fitness in their decision making about SLN biopsy in the elderly population with hormone receptor positive early breast cancer.
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Affiliation(s)
- Katherine Grant
- Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Xiang Yuen Po
- Department of Surgery, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Leong Tiong
- Department of Breast & Endocrine Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
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9
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Parisi S, Lucido FS, Mongardini FM, Ruggiero R, Fisone F, Tolone S, Santoriello A, Iovino F, Parmeggiani D, Vagni D, Cerbara L, Docimo L, Gambardella C. An Intraoperative Ultrasound Evaluation of Axillary Lymph Nodes: Cassandra Predictive Models in Patients with Breast Cancer-A Multicentric Study. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1806. [PMID: 39596991 PMCID: PMC11596888 DOI: 10.3390/medicina60111806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2024] [Revised: 10/20/2024] [Accepted: 11/01/2024] [Indexed: 11/29/2024]
Abstract
Background and Objectives: Axillary lymph node (ALN) staging is crucial for the management of invasive breast cancer (BC). Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US is limited by intra- and inter-operator variability. This study aims to evaluate US and Elastosonography Shear Wave (SW-ES) parameters for ALN staging to develop a predictive model, named the Cassandra score (CS), to improve the interpretation of findings and standardize staging. Materials and Methods: Sixty-three women diagnosed with BC and treated at two Italian hospitals were enrolled in the study. A total of 529 lymph nodes were surgically removed, underwent intraoperative US examination, and were individually sent for a final histological analysis. The study aimed to establish a direct correlation between eight US-SWES features (margins, vascularity, roundness index (RI), loss of hilum fat, cortical thickness, shear-wave elastography hardness (SWEH), peripheral infiltration (PI), and hypoechoic appearance) and the histological outcome (benign vs. malignant). Results: Several statistical models were compared. PI was strongly correlated with malignant ALNs. An ROC analysis for Model A revealed an impressive AUC of 0.978 (S.E. = 0.007, p < 0.001), while in Model B, the cut-offs of SWEH and RI were modified to minimize the risk of false negatives (AUC of 0.973, S.E. = 0.009, p < 0.001). Model C used the same cut-offs as Model B, but excluded SWEH from the formula, to make the Cassandra model usable even if the US machine does not have SW-ES capability (AUC of 0.940, S.E. = 0.015, p < 0.001). A two-tiered model was finally set up, leveraging the strong predictive capabilities of SWEH and RI. In the first tier, only SWES and RI were evaluated: a positive result was predicted if both hardness and roundness were present (SWES > 137 kPa and RI < 1.55), and conversely, a negative result was predicted if both were absent (SWES < 137 kPa and RI > 1.55). In the second tier, if there was a mix of the results (SWES > 137 kPa and RI > 1.55 or SWES < 137 kPa and RI < 1.55), the algorithm in Model B was applied. The model demonstrated an overall prediction accuracy of 90.2% in the training set, 87.5% in the validation set, and 88.9% across the entire dataset. The NPV was notably high at 99.2% in the validation set. This model was named the Cassandra score (CS) and is proposed for the clinical management of BC patients. Conclusion: CS is a simple, non-invasive, fast, and reliable method that showed a PPV of 99.1% in the malignancy prediction of ALNs, potentially being also well suited for young sonographers.
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Affiliation(s)
- Simona Parisi
- Department of Advanced Medical and Surgical Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy; (F.S.L.); (F.M.M.); (R.R.); (F.F.); (S.T.); (D.P.); (L.D.); (C.G.)
| | - Francesco Saverio Lucido
- Department of Advanced Medical and Surgical Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy; (F.S.L.); (F.M.M.); (R.R.); (F.F.); (S.T.); (D.P.); (L.D.); (C.G.)
| | - Federico Maria Mongardini
- Department of Advanced Medical and Surgical Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy; (F.S.L.); (F.M.M.); (R.R.); (F.F.); (S.T.); (D.P.); (L.D.); (C.G.)
| | - Roberto Ruggiero
- Department of Advanced Medical and Surgical Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy; (F.S.L.); (F.M.M.); (R.R.); (F.F.); (S.T.); (D.P.); (L.D.); (C.G.)
| | - Francesca Fisone
- Department of Advanced Medical and Surgical Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy; (F.S.L.); (F.M.M.); (R.R.); (F.F.); (S.T.); (D.P.); (L.D.); (C.G.)
| | - Salvatore Tolone
- Department of Advanced Medical and Surgical Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy; (F.S.L.); (F.M.M.); (R.R.); (F.F.); (S.T.); (D.P.); (L.D.); (C.G.)
| | - Antonio Santoriello
- Breast Unit, Division of Surgery, Cobelli’s Hospital, Vallo della Lucania, 84078 Salerno, Italy;
| | - Francesco Iovino
- Department of Traslational Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy;
| | - Domenico Parmeggiani
- Department of Advanced Medical and Surgical Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy; (F.S.L.); (F.M.M.); (R.R.); (F.F.); (S.T.); (D.P.); (L.D.); (C.G.)
| | - David Vagni
- National Research Council, Institute for Research and Biomedical Innovation, 98164 Messina, Italy;
| | - Loredana Cerbara
- National Research Council, Institute for Research on Population and Social Policies (CNR-IRPPS), 00185 Rome, Italy;
| | - Ludovico Docimo
- Department of Advanced Medical and Surgical Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy; (F.S.L.); (F.M.M.); (R.R.); (F.F.); (S.T.); (D.P.); (L.D.); (C.G.)
| | - Claudio Gambardella
- Department of Advanced Medical and Surgical Sciences, Division of General, Oncological, Mini-Invasive and Obesity Surgery—University of Study of Campania “Luigi Vanvitelli”, 80136 Naples, Italy; (F.S.L.); (F.M.M.); (R.R.); (F.F.); (S.T.); (D.P.); (L.D.); (C.G.)
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10
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Xu S, Wang Q, Hong Z. The correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis. Front Oncol 2024; 14:1433872. [PMID: 39529837 PMCID: PMC11552536 DOI: 10.3389/fonc.2024.1433872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 10/14/2024] [Indexed: 11/16/2024] Open
Abstract
Objective This study aimed to explore the correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis. Method A total of 196 patients with surgically confirmed breast cancer between September 2019 and December 2023 were included. Data on preoperative B-mode ultrasound (US), color Doppler, and shear wave elastography (SWE) features of breast cancer masses were collected and analyzed to determine their correlation with axillary lymph node metastasis. The area under the receiver operating characteristic curve (AUC) of B-mode US, color Doppler, SWE, and the multi-mode predictive model for evaluating axillary lymph node metastasis were compared. Results Among the 196 patients, 70 had positive axillary lymph nodes, while 126 had negative axillary lymph nodes. There was no significant difference in the color features between the negative and positive axillary lymph node groups. Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were identified as independent risk factors to predict axillary lymph node metastasis according to binary logistic regression analysis. The AUC of the predictive model based on these independent risk factors was 0.803 (95% CI: 0.739-0.867), which was significantly higher than that of B-mode US or SWE alone. Conclusion Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were found to be valuable for predicting axillary lymph node metastasis in patients with breast cancer. The predictive model developed in this study, combining the multi-mode ultrasonographic features of breast cancer masses, could serve as a noninvasive and convenient method to predict axillary lymph node status. This approach could aid in clinical decision-making and individualized treatment to improve the prognosis of breast cancer patients.
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Affiliation(s)
| | | | - Zhe Hong
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
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11
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Fu Y, Lei YT, Huang YH, Mei F, Wang S, Yan K, Wang YH, Ma YH, Cui LG. Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Eur Radiol 2024; 34:7080-7089. [PMID: 38724768 PMCID: PMC11519196 DOI: 10.1007/s00330-024-10786-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/04/2024] [Accepted: 03/10/2024] [Indexed: 10/29/2024]
Abstract
OBJECTIVES Developing a deep learning radiomics model from longitudinal breast ultrasound and sonographer's axillary ultrasound diagnosis for predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer. METHODS Breast cancer patients undergoing NAC followed by surgery were recruited from three centers between November 2016 and December 2022. We collected ultrasound images for extracting tumor-derived radiomics and deep learning features, selecting quantitative features through various methods. Two machine learning models based on random forest were developed using pre-NAC and post-NAC features. A support vector machine integrated these data into a fusion model, evaluated via the area under the curve (AUC), decision curve analysis, and calibration curves. We compared the fusion model's performance against sonographer's diagnosis from pre-NAC and post-NAC axillary ultrasonography, referencing histological outcomes from sentinel lymph node biopsy or axillary lymph node dissection. RESULTS In the validation cohort, the fusion model outperformed both pre-NAC (AUC: 0.899 vs. 0.786, p < 0.001) and post-NAC models (AUC: 0.899 vs. 0.853, p = 0.014), as well as the sonographer's diagnosis of ALN status on pre-NAC and post-NAC axillary ultrasonography (AUC: 0.899 vs. 0.719, p < 0.001). Decision curve analysis revealed patient benefits from the fusion model across threshold probabilities from 0.02 to 0.98. The model also enhanced sonographer's diagnostic ability, increasing accuracy from 71.9% to 79.2%. CONCLUSION The deep learning radiomics model accurately predicted the ALN response to NAC in breast cancer. Furthermore, the model will assist sonographers to improve their diagnostic ability on ALN status before surgery. CLINICAL RELEVANCE STATEMENT Our AI model based on pre- and post-neoadjuvant chemotherapy ultrasound can accurately predict axillary lymph node metastasis and assist sonographer's axillary diagnosis. KEY POINTS Axillary lymph node metastasis status affects the choice of surgical treatment, and currently relies on subjective ultrasound. Our AI model outperformed sonographer's visual diagnosis on axillary ultrasound. Our deep learning radiomics model can improve sonographers' diagnosis and might assist in surgical decision-making.
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Affiliation(s)
- Ying Fu
- Department of Ultrasound, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Yu-Tao Lei
- Department of General Surgery, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Yu-Hong Huang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
| | - Fang Mei
- Department of Pathology, Peking University Third Hospital, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Song Wang
- Department of Ultrasound, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), No. 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Kun Yan
- Department of Ultrasound, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), No. 52 Fucheng Road, Haidian District, Beijing, 100142, China
| | - Yi-Hua Wang
- Department of Ultrasound, North China University of Science and Technology Affiliated Hospital, 73 South Jianshe Road, Lubei District, Tangshan, 066300, China
| | - Yi-Han Ma
- Department of Ultrasound, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Li-Gang Cui
- Department of Ultrasound, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China.
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12
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Ma J, Fu Y, Chen X, Lin Y, Zeng L, Mei F, Cui L. Utilizing the Postvascular Phase of Contrast-Enhanced Ultrasound to Predict Breast Cancer Lymph Node Metastasis. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1780. [PMID: 39596965 PMCID: PMC11596673 DOI: 10.3390/medicina60111780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2024] [Revised: 10/21/2024] [Accepted: 10/29/2024] [Indexed: 11/29/2024]
Abstract
Background and Objectives: To evaluate the value of the postvascular phase of contrast-enhanced ultrasound (CEUS) in differentiating between benign and metastatic lymph nodes (LNs) in patients with breast cancer (BC). Materials and Methods: This study retrospectively analyzed 96 suspicious LNs in the lymphatic drainage area of the breast from 90 patients with BC. All LNs were assessed by conventional ultrasound (US) and CEUS following intravenous Sonazoid injection. All LNs underwent puncture biopsy, and pathological results were obtained. The correlations between US and CEUS indicators of LNs and LN metastasis (LNM) were analyzed. Results: Of the 96 LNs, 66 were metastatic. Overall, 80.00% (24/30) of the benign LNs exhibited relative hyper-enhancement in the postvascular phase, whereas 96.97% (64/66) of the metastatic LNs exhibited relative hypo-enhancement (p < 0.001). This CEUS finding was highly predictive of metastasis, with a sensitivity of 96.97%, specificity of 80.00%, positive predictive value of 91.43%, negative predictive value of 92.31%, and accuracy of 91.67%. The mean postvascular phase intensity (MPI) was significantly lower for malignant (median MPI, 12 dB) than for benign (median MPI, 75 dB) LNs. The postvascular phase was more sensitive, specific, and accurate than conventional US or the vascular phase of CEUS for the diagnosis of LNM, with an area under the curve of 0.95 (95% confidence interval: 0.89-0.99). Conclusions: Qualitative and quantitative indicators of the postvascular phase of CEUS provide a reliable diagnostic approach to differentiate benign and metastatic LNs in patients with BC.
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Affiliation(s)
- Jiuyi Ma
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Ying Fu
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Xiangmei Chen
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen 518036, China
| | - Yuxuan Lin
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Lan Zeng
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Fang Mei
- Department of Pathology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Ligang Cui
- Department of Ultrasound, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
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13
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Dobruch-Sobczak K, Szlenk A, Gumowska M, Mączewska J, Fronczewska K, Łukasiewicz E, Roszkowska-Purska K, Jakubczak M. Multiparametric ultrasound assessment of axillary lymph nodes in patients with breast cancer. Sci Rep 2024; 14:23072. [PMID: 39366984 PMCID: PMC11452636 DOI: 10.1038/s41598-024-73376-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Accepted: 09/17/2024] [Indexed: 10/06/2024] Open
Abstract
The presence and extent of metastatic disease in axillary lymph nodes (ALNs) in the setting of breast cancer (BC) are important factors for staging and therapy planning. The purpose of this study was to perform a multiparametric sonographic evaluation of ALNs to better differentiate between benign and metastatic nodes. Ninety-nine patients (mean age 54.1 y) with 103 BCs were included in this study, and 103 ALNs were examined sonographically. B-mode parameters, such as size in two dimensions, shape, cortical thickness and capsule outline, were obtained, followed by vascularity assessment via colour Doppler and microflow imaging and stiffness evaluation via shear wave elastography. Postoperative histopathological evaluation was the reference standard. In the statistical analysis, logistic regression and ROC analyses were conducted to search for feature patterns of both types of ALNs to evaluate the prediction qualities of the analysed variables and their combinations. For a cortex larger than 3 mm, without a circumscribed margin of the LN capsule and SWE (E max > 26 kPa), the AUC was 0.823. Multiparametric assessment, which combined conventional US, quantitative SWE and vascularity analysis, was superior to the single-parameter approach in the evaluation of ALNs.
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Affiliation(s)
- Katarzyna Dobruch-Sobczak
- Radiology Department II, The Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland.
- Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.
| | - Axana Szlenk
- Radiology Department II, The Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Magdalena Gumowska
- Radiology Department II, The Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Joanna Mączewska
- Radiology Department II, The Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Katarzyna Fronczewska
- Radiology Department II, The Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Ewa Łukasiewicz
- Radiology Department II, The Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | | | - Magda Jakubczak
- Radiology Department II, The Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland
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14
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Wang Q, Lin Y, Ding C, Guan W, Zhang X, Jia J, Zhou W, Liu Z, Bai G. Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography. Eur Radiol 2024; 34:6121-6131. [PMID: 38337068 DOI: 10.1007/s00330-024-10638-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 12/05/2023] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVES We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. METHODS We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. RESULTS The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. CONCLUSION The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. CLINICAL RELEVANCE STATEMENT The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. KEY POINTS • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI.
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Affiliation(s)
- Qian Wang
- Department of Radiology, The Affiliated Huaian Clinical College of Xuzhou Medical University, Huaian, Jiangsu, China
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, China
| | - Cong Ding
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Wenting Guan
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Xiaoling Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, China
| | - Jianye Jia
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Wei Zhou
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Ziyan Liu
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Genji Bai
- Department of Radiology, The Affiliated Huaian Clinical College of Xuzhou Medical University, Huaian, Jiangsu, China.
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
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Liu X, Huang YN, Wu YL, Zhu XY, Xie ZM, Li J. The value of quantitative shear wave elastography combined with conventional ultrasound in evaluating and guiding fine needle aspiration biopsy of axillary lymph node for early breast cancer: implication for axillary surgical stage. BMC Med Imaging 2024; 24:229. [PMID: 39215218 PMCID: PMC11365282 DOI: 10.1186/s12880-024-01407-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024] Open
Abstract
OBJECTIVES To investigate the value of conventional ultrasonography (US) combined with quantitative shear wave elastography (SWE) in evaluating and identifying target axillary lymph node (TALN) for fine needle aspiration biopsy (FNAB) of patients with early breast cancer. MATERIALS AND METHODS A total of 222 patients with 223 ALNs were prospectively recruited from January 2018 to December 2021. All TALNs were evaluated by US, SWE and subsequently underwent FNAB. The diagnostic performances of US, SWE, UEor (either US or SWE was positive) and UEand (both US and SWE were positive), and FNAB guided by the above four methods for evaluating ALN status were assessed using receiver operator characteristic curve (ROC) analyses. Univariate and multivariate logistic regression analyses used to determine the independent predictors of axillary burden. RESULTS The area under the ROC curve (AUC) for diagnosing ALNs using conventional US and SWE were 0.69 and 0.66, respectively, with sensitivities of 78.00% and 65.00% and specificities of 60.98% and 66.67%. The combined method, UEor, demonstrated significantly improved sensitivity of 86.00% (p < 0.001 when compared with US and SWE alone). The AUC of the UEor-guided FNAB [0.85 (95% CI, 0.80-0.90)] was significantly higher than that of US-guided FNAB [0.83 (95% CI, 0.78-0.88), p = 0.042], SWE-guided FNAB [0.79 (95% CI, 0.72-0.84), p = 0.001], and UEand-guided FNAB [0.77 (95% CI, 0.71-0.82), p < 0.001]. Multivariate logistic regression showed that FNAB and number of suspicious ALNs were found independent predictors of axillary burden in patients with early breast cancer. CONCLUSION The UEor had superior sensitivity compared to US or SWE alone in ALN diagnosis. The UEor-guided FNAB achieved a lower false-negative rate compared to FNAB guided solely by US or SWE, which may be a promising tool for the preoperative diagnosis of ALNs in early breast cancer, and had the potential implication for the selection of axillary surgical modality.
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Affiliation(s)
- Xuan Liu
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Yuexiu District, Guangzhou City, Guangdong Province, China
| | - Yi-Ni Huang
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Yuexiu District, Guangzhou City, Guangdong Province, China
| | - Ying-Lan Wu
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Yuexiu District, Guangzhou City, Guangdong Province, China
| | - Xiao-Yao Zhu
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Yuexiu District, Guangzhou City, Guangdong Province, China
| | - Ze-Ming Xie
- Department of Breast Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jian Li
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Yuexiu District, Guangzhou City, Guangdong Province, China.
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Wang X, Nie L, Zhu Q, Zuo Z, Liu G, Sun Q, Zhai J, Li J. Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer. BMC Cancer 2024; 24:910. [PMID: 39075447 PMCID: PMC11285453 DOI: 10.1186/s12885-024-12619-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 07/09/2024] [Indexed: 07/31/2024] Open
Abstract
PURPOSE A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study. METHODS A total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis. RESULTS The area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis. CONCLUSION This model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.
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Affiliation(s)
- Xuefei Wang
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China
| | - Lunyiu Nie
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Qingli Zhu
- Ultrasonography Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China
| | - Zhichao Zuo
- Radiology Department, Xiangtan Central Hospital, Hunan, China
| | - Guanmo Liu
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China
| | - Qiang Sun
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China.
| | - Jidong Zhai
- Department of Computer Science and Technology, Tsinghua University, Beijing, China.
| | - Jianchu Li
- Ultrasonography Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China.
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Yi M, Lin Y, Lin Z, Xu Z, Li L, Huang R, Huang W, Wang N, Zuo Y, Li N, Ni D, Zhang Y, Li Y. Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network. Clin Breast Cancer 2024; 24:e319-e332.e2. [PMID: 38494415 DOI: 10.1016/j.clbc.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 02/04/2024] [Accepted: 02/06/2024] [Indexed: 03/19/2024]
Abstract
OBJECTIVES To develop predictive nomograms based on clinical and ultrasound features and to improve the clinical strategy for US BI-RADS 4A lesions. METHODS Patients with US BI-RADS 4A lesions from 3 hospitals between January 2016 and June 2020 were retrospectively included. Clinical and ultrasound features were extracted to establish nomograms CE (based on clinical experience) and DL (based on deep-learning algorithm). The performances of nomograms were evaluated by receiver operator characteristic curves, calibration curves and decision curves. Diagnostic performances with DL of radiologists were analyzed. RESULTS 1616 patients from 2 hospitals were randomly divided into training and internal validation cohorts at a ratio of 7:3. Hundred patients from another hospital made up external validation cohort. DL achieved more optimized AUCs than CE (internal validation: 0.916 vs. 0.863, P < .01; external validation: 0.884 vs. 0.776, P = .05). The sensitivities of DL were higher than CE (internal validation: 81.03% vs. 72.41%, P = .044; external validation: 93.75% vs. 81.25%, P = .4795) without losing specificity (internal validation: 84.91% vs. 86.47%, P = .353; external validation: 69.14% vs. 71.60%, P = .789). Decision curves indicated DL adds more clinical net benefit. With DL's assistance, both radiologists achieved higher AUCs (0.712 vs. 0.801; 0.547 vs. 0.800), improved specificities (70.93% vs. 74.42%, P < .001; 59.3% vs. 81.4%, P = .004), and decreased unnecessary biopsy rates by 6.7% and 24%. CONCLUSION DL was developed to discriminate US BI-RADS 4A lesions with a higher diagnostic power and more clinical net benefit than CE. Using DL may guide clinicians to make precise clinical decisions and avoid overtreatment of benign lesions.
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Affiliation(s)
- Mei Yi
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yue Lin
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zehui Lin
- Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Ziting Xu
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lian Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ruobing Huang
- Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Weijun Huang
- Department of Ultrasound, The First People's Hospital of Foshan, Foshan, China
| | - Nannan Wang
- Department of Ultrasound, The First People's Hospital of Foshan, Foshan, China
| | - Yanling Zuo
- Department of Ultrasound Imaging, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Nuo Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Dong Ni
- Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yanyan Zhang
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Yingjia Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, Guangzhou, China.
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18
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Ye X, Zhang X, Lin Z, Liang T, Liu G, Zhao P. Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer. Am J Transl Res 2024; 16:2398-2410. [PMID: 39006270 PMCID: PMC11236629 DOI: 10.62347/kepz9726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 05/18/2024] [Indexed: 07/16/2024]
Abstract
OBJECTIVE To develop a nomogram for predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer. METHODS We included 307 patients with clinicopathologically confirmed invasive breast cancer. The cohort was divided into a training group (n=215) and a validation group (n=92). Ultrasound images were used to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) algorithm helped select pertinent features, from which Radiomics Scores (Radscores) were calculated using the LASSO regression equation. We developed three logistic regression models based on Radscores and 2D image features, and assessed the models' performance in the validation group. A nomogram was created from the best-performing model. RESULTS In the training set, the area under the curve (AUC) for the Radscore model, 2D feature model, and combined model were 0.76, 0.85, and 0.88, respectively. In the validation set, the AUCs were 0.71, 0.78, and 0.83, respectively. The combined model demonstrated good calibration and promising clinical utility. CONCLUSION Our ultrasound-based radiomics nomogram can accurately and non-invasively predict ALNM in breast cancer, suggesting potential clinical applications to optimize surgical and medical strategies.
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Affiliation(s)
- Xiaolu Ye
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, China
| | - Xiaoxue Zhang
- Guangzhou University of Chinese MedicineGuangzhou 510006, Guangdong, China
| | - Zhuangteng Lin
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, China
| | - Ting Liang
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, China
| | - Ge Liu
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, China
| | - Ping Zhao
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, China
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19
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Washington I, Palm RF, White J, Rosenberg SA, Ataya D. The Role of MRI in Breast Cancer and Breast Conservation Therapy. Cancers (Basel) 2024; 16:2122. [PMID: 38893241 PMCID: PMC11171236 DOI: 10.3390/cancers16112122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/19/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
Contrast-enhanced breast MRI has an established role in aiding in the detection, evaluation, and management of breast cancer. This article discusses MRI sequences, the clinical utility of MRI, and how MRI has been evaluated for use in breast radiotherapy treatment planning. We highlight the contribution of MRI in the decision-making regarding selecting appropriate candidates for breast conservation therapy and review the emerging role of MRI-guided breast radiotherapy.
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Affiliation(s)
- Iman Washington
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA;
| | - Russell F. Palm
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA;
| | - Julia White
- Department of Radiation Oncology, The University of Kansas Medical Center, 4001 Rainbow Blvd, Kansas City, KS 66160, USA;
| | - Stephen A. Rosenberg
- Department of Radiation Therapy, H. Lee Moffitt Cancer Center & Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA;
| | - Dana Ataya
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, 10920 N. McKinley Drive, Tampa, FL 33612, USA;
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20
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Moreth M, Herröder N, Hödl P, Bufe A, Bretschneider C, Möbus V, Rom J, Müller-Schimpfle M. Clinical Axillary Staging in Breast Cancer Patients Using Ultrasound Imaging. Breast Care (Basel) 2024; 19:149-154. [PMID: 38894954 PMCID: PMC11182635 DOI: 10.1159/000538816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 04/09/2024] [Indexed: 06/21/2024] Open
Abstract
Introduction The presence of axillary lymph node involvement is an important prognostic factor and has a major impact on treatment decisions in early breast cancer patients. This study aimed to determine the role of cortical thickness in axillary ultrasound (AUS) as an indicator of lymph node metastasis. Methods 766 patients with primary breast cancer who received AUS during clinical work-up were selected for this retrospective study. Lymph nodes were defined as suspicious if they showed a cortical thickness of >3.0 mm at 11-15 MHz harmonic imaging ultrasound. Lymph node involvement was assessed by core needle biopsy (n = 150), sentinel node dissection or axillary dissection. Extensive axillary spread (EAS) was diagnosed if more than two lymph nodes showed metastatic disease in histology. Results AUS for detecting all lymph node metastases had a sensitivity of 62.27%, a specificity of 93.15% and a negative predictive value of 81.74%. However, the resulting negative predictive value for transcapsular growth was 93.97%, and for EAS 97.52%. Conclusion EAS - in contrast to non-palpable involvement of 1 or 2 lymph nodes - contributes relevantly to the individualization of breast cancer treatment. In combination with SNB, AUS using cortical thickness as the main distinctive parameter seems to be an easily available, robust tool of diagnosing extensive axillary metastases. If AUS proves negative, it helps to reduce the number of classic axillary dissections.
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Affiliation(s)
- Maximilian Moreth
- Radiology, Neuro-Radiology and Nuclear Medicine, Varisano Klinikum Frankfurt Höchst, Frankfurt, Germany
| | - Nicole Herröder
- Gynecology and Obstetrics, Varisano Klinikum Frankfurt Höchst, Frankfurt, Germany
| | - Petra Hödl
- Institute of Pathology, Varisano Klinikum Frankfurt Höchst, Frankfurt, Germany
| | - Alexa Bufe
- Gynecology and Obstetrics, Varisano Klinikum Frankfurt Höchst, Frankfurt, Germany
| | - Christiane Bretschneider
- Radiology, Neuro-Radiology and Nuclear Medicine, Varisano Klinikum Frankfurt Höchst, Frankfurt, Germany
| | - Volker Möbus
- Gynecology and Obstetrics, Varisano Klinikum Frankfurt Höchst, Frankfurt, Germany
| | - Joachim Rom
- Gynecology and Obstetrics, Varisano Klinikum Frankfurt Höchst, Frankfurt, Germany
| | - Markus Müller-Schimpfle
- Radiology, Neuro-Radiology and Nuclear Medicine, Varisano Klinikum Frankfurt Höchst, Frankfurt, Germany
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21
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McDonald ES, Scheel JR, Lewin AA, Weinstein SP, Dodelzon K, Dogan BE, Fitzpatrick A, Kuzmiak CM, Newell MS, Paulis LV, Pilewskie M, Salkowski LR, Silva HC, Sharpe RE, Specht JM, Ulaner GA, Slanetz PJ. ACR Appropriateness Criteria® Imaging of Invasive Breast Cancer. J Am Coll Radiol 2024; 21:S168-S202. [PMID: 38823943 DOI: 10.1016/j.jacr.2024.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 02/28/2024] [Indexed: 06/03/2024]
Abstract
As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.
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Affiliation(s)
- Elizabeth S McDonald
- Research Author, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - John R Scheel
- Vanderbilt University Medical Center, Nashville, Tennessee.
| | - Alana A Lewin
- Panel Chair, New York University Grossman School of Medicine, New York, New York
| | - Susan P Weinstein
- Panel Vice Chair, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Basak E Dogan
- University of Texas Southwestern Medical Center, Dallas, Texas
| | - Amy Fitzpatrick
- Boston Medical Center, Boston, Massachusetts, Primary care physician
| | | | - Mary S Newell
- Emory University Hospital, Atlanta, Georgia; RADS Committee
| | | | - Melissa Pilewskie
- University of Michigan, Ann Arbor, Michigan; Society of Surgical Oncology
| | - Lonie R Salkowski
- University of Wisconsin School of Medicine & Public Health, Madison, Wisconsin
| | - H Colleen Silva
- The University of Texas Medical Branch, Galveston, Texas; American College of Surgeons
| | | | - Jennifer M Specht
- University of Washington, Seattle, Washington; American Society of Clinical Oncology
| | - Gary A Ulaner
- Hoag Family Cancer Institute, Newport Beach, California; University of Southern California, Los Angeles, California; Commission on Nuclear Medicine and Molecular Imaging
| | - Priscilla J Slanetz
- Specialty Chair, Boston University School of Medicine, Boston, Massachusetts
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22
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Sun C, Gong X, Hou L, Yang D, Li Q, Li L, Wang Y. A nomogram based on conventional and contrast-enhanced ultrasound radiomics for the noninvasively prediction of axillary lymph node metastasis in breast cancer patients. Front Oncol 2024; 14:1400872. [PMID: 38800371 PMCID: PMC11116775 DOI: 10.3389/fonc.2024.1400872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 04/25/2024] [Indexed: 05/29/2024] Open
Abstract
Background This study aimed to investigate whether quantitative radiomics features extracted from conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) of primary breast lesions can help noninvasively predict axillary lymph nodes metastasis (ALNM) in breast cancer patients. Method A total of 111 breast cancer patients with 111 breast lesions were prospectively enrolled. All the included patients received presurgical CUS screening and CEUS examination and were randomly assigned to the training and validation sets at a ratio of 7:3 (n = 78 versus 33). Radiomics features were respectively extracted based on CUS and CEUS using the PyRadiomics package. The max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) analyses were used for feature selection and radiomics score calculation in the training set. The variance inflation factor (VIF) was performed to check the multicollinearity among selected predictors. The best performing model was selected to develop a nomogram using binary logistic regression analysis. The calibration and clinical utility of the nomogram were assessed. Results The model combining CUS reported ALN status, CUS radiomics score (CUS-radscore) and CEUS radiomics score (CEUS-radscore) exhibited the best performance. The areas under the curves (AUC) of our proposed nomogram in the training and external validation sets were 0.845 [95% confidence interval (CI), 0.739-0.950] and 0.901 (95% CI, 0.758-1). The calibration curves and decision curve analysis (DCA) demonstrated the nomogram's robust consistency and clinical utility. Conclusions The established nomogram is a promising prediction tool for noninvasive prediction of ALN status. The radiomics features based on CUS and CEUS can help improve the predictive performance.
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Affiliation(s)
- Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuantong Gong
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lu Hou
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Di Yang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qian Li
- Department of Ultrasound, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Chudobiński C, Świderski B, Antoniuk I, Kurek J. Enhancements in Radiological Detection of Metastatic Lymph Nodes Utilizing AI-Assisted Ultrasound Imaging Data and the Lymph Node Reporting and Data System Scale. Cancers (Basel) 2024; 16:1564. [PMID: 38672646 PMCID: PMC11048706 DOI: 10.3390/cancers16081564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The paper presents a novel approach for the automatic detection of neoplastic lesions in lymph nodes (LNs). It leverages the latest advances in machine learning (ML) with the LN Reporting and Data System (LN-RADS) scale. By integrating diverse datasets and network structures, the research investigates the effectiveness of ML algorithms in improving diagnostic accuracy and automation potential. Both Multinominal Logistic Regression (MLR)-integrated and fully connected neuron layers are included in the analysis. The methods were trained using three variants of combinations of histopathological data and LN-RADS scale labels to assess their utility. The findings demonstrate that the LN-RADS scale improves prediction accuracy. MLR integration is shown to achieve higher accuracy, while the fully connected neuron approach excels in AUC performance. All of the above suggests a possibility for significant improvement in the early detection and prognosis of cancer using AI techniques. The study underlines the importance of further exploration into combined datasets and network architectures, which could potentially lead to even greater improvements in the diagnostic process.
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Affiliation(s)
- Cezary Chudobiński
- Copernicus Regional Multi-Specialty Oncology and Trauma Centre, 93-513 Lódź, Poland;
| | - Bartosz Świderski
- Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland; (B.Ś.); (I.A.)
| | - Izabella Antoniuk
- Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland; (B.Ś.); (I.A.)
| | - Jarosław Kurek
- Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland; (B.Ś.); (I.A.)
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24
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Zahra K, Farooqi HA. Cutting-edge innovations in breast cancer diagnosis- the potential of HDMI biomarkers. Clin Exp Metastasis 2024; 41:77-78. [PMID: 37847349 DOI: 10.1007/s10585-023-10238-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023]
Affiliation(s)
- Kainat Zahra
- Islamic International Medical College, Riphah International University, Rawalpindi, Pakistan.
| | - Hanzala Ahmed Farooqi
- Islamic International Medical College, Riphah International University, Rawalpindi, Pakistan
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Loonis AST, Chesebro AL, Bay CP, Portnow LH, Weiss A, Chikarmane SA, Giess CS. Positive predictive value of axillary lymph node cortical thickness and nodal, clinical, and tumor characteristics in newly diagnosed breast cancer patients. Breast Cancer Res Treat 2024; 203:511-521. [PMID: 37950089 DOI: 10.1007/s10549-023-07155-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/30/2023] [Indexed: 11/12/2023]
Abstract
PURPOSE Axillary lymph nodes (LNs) with cortical thickness > 3 mm have a higher likelihood of malignancy. To examine the positive predictive value (PPV) of axillary LN cortical thickness in newly diagnosed breast cancer patients, and nodal, clinical, and tumor characteristics associated with axillary LN metastasis. METHODS Retrospective review of axillary LN fine needle aspirations (FNAs) performed 1/1/2018-12/31/2019 included 135 axillary FNAs in 134 patients who underwent axillary surgery. Patient demographics, clinical characteristics, histopathology, and imaging features were obtained from medical records. Hypothesis testing was performed to identify predictors of axillary LN metastasis. RESULTS Cytology was positive in 72/135 (53.3%), negative in 61/135 (45.2%), and non-diagnostic in 2/135 (1.5%). At surgery, histopathology was positive in 84 (62.2%) and negative in 51 (37.8%). LN cortices were thicker in metastatic compared to negative nodes (p < 0.0001). PPV of axillary LNs with cortical thickness ≥ 3 mm, ≥ 3.5 mm, ≥ 4 mm and, ≥ 4.25 mm was 0.62 [95% CI 0.53, 0.70], 0.63 [0.54, 0.72], 0.67 [0.57, 0.76] , and 0.74 [0.64, 0.83], respectively. At multivariable analysis, abnormal hilum (OR = 3.44, p = 0.016) and diffuse cortical thickening (OR = 2.86, p = 0.038) were associated with nodal metastasis. CONCLUSION In newly diagnosed breast cancer patients, increasing axillary LN cortical thickness, abnormal fatty hilum, and diffuse cortical thickening are associated with nodal metastasis. PPV of axillary LN cortical thickness ≥ 3 mm and ≥ 3.5 mm is similar but increases for cortical thickness ≥ 4 mm. FNA of axillary LNs with cortex < 4 mm may be unnecessary for some patients undergoing sentinel LN biopsy.
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Affiliation(s)
- Anne-Sophie T Loonis
- Division of Breast Imaging, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
- Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA
| | - Allyson L Chesebro
- Division of Breast Imaging, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA.
- Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA.
| | - Camden P Bay
- Division of Breast Imaging, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Leah H Portnow
- Division of Breast Imaging, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
- Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA
| | - Anna Weiss
- Division of Breast Imaging, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
- Division of Surgical Oncology, Department of Surgery, University of Rochester, Rochester, NY, USA
| | - Sona A Chikarmane
- Division of Breast Imaging, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
- Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA
| | - Catherine S Giess
- Division of Breast Imaging, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
- Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA
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Kim SM, Jang M, Yun BL, Shin SU, Rim J, Kang E, Kim EK, Shin HC, Park SY, Kim B. Automated Versus Handheld Breast Ultrasound for Evaluating Axillary Lymph Nodes in Patients With Breast Cancer. Korean J Radiol 2024; 25:146-156. [PMID: 38238017 PMCID: PMC10831296 DOI: 10.3348/kjr.2023.0100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 01/31/2024] Open
Abstract
OBJECTIVE Automated breast ultrasound (ABUS) is a relevant imaging technique for early breast cancer diagnosis and is increasingly being used as a supplementary tool for mammography. This study compared the performance of ABUS and handheld ultrasound (HHUS) in detecting and characterizing the axillary lymph nodes (LNs) in patients with breast cancer. MATERIALS AND METHODS We retrospectively reviewed the medical records of women with recently diagnosed early breast cancer (≤ T2) who underwent both ABUS and HHUS examinations for axilla (September 2017-May 2018). ABUS and HHUS findings were compared using pathological outcomes as reference standards. Diagnostic performance in predicting any axillary LN metastasis and heavy nodal-burden metastases (i.e., ≥ 3 LNs) was evaluated. The ABUS-HHUS agreement for visibility and US findings was calculated. RESULTS The study included 377 women (53.1 ± 11.1 years). Among 385 breast cancers in 377 patients, 101 had axillary LN metastases and 30 had heavy nodal burden metastases. ABUS identified benign-looking or suspicious axillary LNs (average, 1.4 ± 0.8) in 246 axillae (63.9%, 246/385). According to the per-breast analysis, the sensitivity, specificity, positive and negative predictive values, and accuracy of ABUS in predicting axillary LN metastases were 43.6% (44/101), 95.1% (270/284), 75.9% (44/58), 82.6% (270/327), and 81.6% (314/385), respectively. The corresponding results for HHUS were 41.6% (42/101), 95.1% (270/284), 75.0% (42/56), 82.1% (270/329), and 81.0% (312/385), respectively, which were not significantly different from those of ABUS (P ≥ 0.53). The performance results for heavy nodal-burden metastases were 70.0% (21/30), 89.6% (318/355), 36.2% (21/58), 97.3% (318/327), and 88.1% (339/385), respectively, for ABUS and 66.7% (20/30), 89.9% (319/355), 35.7% (20/56), 97.0% (319/329), and 88.1% (339/385), respectively, for HHUS, also not showing significant difference (P ≥ 0.57). The ABUS-HHUS agreement was 95.9% (236/246; Cohen's kappa = 0.883). CONCLUSION Although ABUS showed limited sensitivity in diagnosing axillary LN metastasis in early breast cancer, it was still useful as the performance was comparable to that of HHUS.
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Affiliation(s)
- Sun Mi Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Mijung Jang
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
| | - Bo La Yun
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Sung Ui Shin
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jiwon Rim
- Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
| | - Eunyoung Kang
- Department of Surgery, Daerim St. Mary's Hospital, Seoul, Republic of Korea
| | - Eun-Kyu Kim
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Hee-Chul Shin
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - So Yeon Park
- Department of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Bohyoung Kim
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
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Zhang XD, Zhang K. Comparative analysis of conventional ultrasound and shear wave elastography features in primary breast diffuse large B-cell lymphoma. World J Clin Cases 2023; 11:7994-8002. [DOI: 10.12998/wjcc.v11.i33.7994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/18/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND Primary breast diffuse large B-cell lymphoma (PB-DLBCL) is a rare subtype of non-Hodgkin lymphoma that accounts for < 3% of extranodal lymphomas and 1% of breast tumors. Its diagnosis and management are challenging because of its rarity, heterogeneity, and aggressive behavior. Conventional ultrasound (US) is the first-line imaging modality for breast lesions; however, it has limited specificity and accuracy for PB-DLBCL. Shear wave elastography (SWE) is a novel US technique that measures tissue stiffness and may reflect the histological characteristics and biological behavior of breast lesions.
AIM To compare the conventional US and SWE features of PB-DLBCL and evaluate their diagnostic performance and prognostic value.
METHODS We retrospectively reviewed the clinical data and US images of 32 patients with pathologically confirmed PB-DLBCL who underwent conventional US and SWE before treatment. We analyzed conventional US features (shape, margin, orientation, echo, posterior acoustic features, calcification, and vascularity) and SWE features (mean elasticity value, standard deviation, minimum elasticity value, maximum elasticity value, and lesion-to-fat ratio) of the PB-DLBCL lesions. Using receiver operating characteristic curve analysis, we determined the optimal cutoff values and diagnostic performance of conventional US and SWE features. We also performed a survival analysis to assess the prognostic value of conventional US and SWE features.
RESULTS The results showed that the PB-DLBCL lesions were mostly irregular in shape (84.4%), microlobulated or spiculated in margins (75%), parallel in orientation (65.6%), hypoechoic in echo (87.5%), and had posterior acoustic enhancement (65.6%). Calcification was rare (6.3%) and vascularity was variable (31.3% avascular, 37.5% hypovascular, and 31.3% hypervascular). The mean elasticity value of PB-DLBCL lesions was significantly higher than that of benign breast lesions (113.4 ± 46.9 kPa vs 27.8 ± 16.4 kPa, P < 0.001). The optimal cutoff value of the mean elasticity for distinguishing PB-DLBCL from benign breast lesions was 54.5 kPa, with a sensitivity of 93.8%, specificity of 92.9%, positive predictive value of 93.8%, negative predictive value of 92.9%, and accuracy of 93.3%. The mean elasticity value was also significantly correlated with Ki-67 expression level (r = 0.612, P < 0.001), which is a marker of tumor proliferation and aggressiveness. Survival analysis showed that patients with higher mean elasticity values (> 54.5 kPa) had worse overall survival (OS) and progression-free survival (PFS) than those with lower mean elasticity values (< 54.5 kPa) (P = 0.038 for OS and P = 0.027 for PFS).
CONCLUSION Conventional US and SWE provide useful information for diagnosing and forecasting PB-DLBCL. SWE excels in distinguishing PB-DLBCL from benign breast lesions, reflects tumor proliferation and aggressiveness, and improves disease management.
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Affiliation(s)
- Xiao-Duan Zhang
- Department of Ultrasound, The Affiliated Hospital of Guizhou Medical University, Guiyang 550081, Guizhou Province, China
| | - Kai Zhang
- Department of Medical Oncology, Shijiazhuang People's Hospital, Shijiazhuang 050000, Hebei Province, China
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Wadhwa A, Majidi SS, Reimer S, Bogachkov A, Dernell C, Astle J, Jorns JM. Axillary node evaluation and biopsy: Predictors of malignancy based on sonographic morphology and mode of detection. Clin Imaging 2023; 104:110014. [PMID: 39492227 DOI: 10.1016/j.clinimag.2023.110014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/04/2023] [Accepted: 10/23/2023] [Indexed: 11/05/2024]
Abstract
PURPOSE The purpose of this study was to evaluate various morphologic features of axillary nodes on ultrasound (US) in predicting malignancy and estimate the incidence of malignancy in axillary nodes based on their imaging mode of detection. METHODS AND MATERIALS A retrospective review of all percutaneous US-guided biopsies on axillary nodes performed at our institution between 1/1/2019-09/30/2021 was performed. Sonographic morphologic features of the biopsied node, imaging mode of detection and size of primary breast malignancy were correlated with malignancy. RESULTS There were 224 malignancies detected in a total of 594 patients who underwent an axillary node biopsy. The positive predictive value (PPV) was significantly associated with the extent of nodal cortical thickening (p < 0.0001). The PPV of malignancy was significantly higher in nodes which lacked a normal hilum (0.61 vs 0.23). The PPV of malignancy in nodes detected on screening mammography (18.8%) or MRI (15.8%) was much lower than those detected on diagnostic imaging, by palpation or on CT/PET (48.4%, 43.8% and 65.3% respectively). Of all screening detected nodes in patients with no history of malignancy and mild cortical thickening, only 2 (4.3%) demonstrated malignancy. CONCLUSIONS Morphology of axillary nodes on sonography is vital in predicting nodal metastasis. Cortical thickness > 5 mm and/or absence of a normal hilum had the highest PPV for metastatic disease. Using clinical history in conjunction with imaging findings will help improve accuracy of axillary nodal biopsies, especially for incidental nodes detected on screening.
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Affiliation(s)
- Anubha Wadhwa
- Department of Radiology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI 53226, United States of America.
| | - Shadie S Majidi
- Department of Radiology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI 53226, United States of America
| | - Shelly Reimer
- Department of Radiology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI 53226, United States of America
| | - Abraham Bogachkov
- Department of Radiology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI 53226, United States of America
| | - Carl Dernell
- Department of Radiology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI 53226, United States of America
| | - John Astle
- Department of Radiology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI 53226, United States of America
| | - Julie M Jorns
- Department of Radiology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI 53226, United States of America
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Loibl S, Azim HA, Bachelot T, Berveiller P, Bosch A, Cardonick E, Denkert C, Halaska MJ, Hoeltzenbein M, Johansson ALV, Maggen C, Markert UR, Peccatori F, Poortmans P, Saloustros E, Saura C, Schmid P, Stamatakis E, van den Heuvel-Eibrink M, van Gerwen M, Vandecaveye V, Pentheroudakis G, Curigliano G, Amant F. ESMO Expert Consensus Statements on the management of breast cancer during pregnancy (PrBC). Ann Oncol 2023; 34:849-866. [PMID: 37572987 DOI: 10.1016/j.annonc.2023.08.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023] Open
Abstract
The management of breast cancer during pregnancy (PrBC) is a relatively rare indication and an area where no or little evidence is available since randomized controlled trials cannot be conducted. In general, advances related to breast cancer (BC) treatment outside pregnancy cannot always be translated to PrBC, because both the interests of the mother and of the unborn should be considered. Evidence remains limited and/or conflicting in some specific areas where the optimal approach remains controversial. In 2022, the European Society for Medical Oncology (ESMO) held a virtual consensus-building process on this topic to gain insights from a multidisciplinary group of experts and develop statements on controversial topics that cannot be adequately addressed in the current evidence-based ESMO Clinical Practice Guideline. The aim of this consensus-building process was to discuss controversial issues relating to the management of patients with PrBC. The virtual meeting included a multidisciplinary panel of 24 leading experts from 13 countries and was chaired by S. Loibl and F. Amant. All experts were allocated to one of four different working groups. Each working group covered a specific subject area with two chairs appointed: Planning, preparation and execution of the consensus process was conducted according to the ESMO standard operating procedures.
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Affiliation(s)
- S Loibl
- GBG c/o GBG Forschungs GmbH, Neu-Isenburg; Centre for Haematology and Oncology Bethanien, Frankfurt am Main, Frankfurt; Goethe University Frankfurt, Frankfurt am Main, Frankfurt, Germany.
| | - H A Azim
- Breast Cancer Center, School of Medicine, Tecnologico de Monterrey, San Pedro Garza Garcia, Nuevo Leon, Mexico
| | - T Bachelot
- Department of medical oncology, Centre Léon Bérard, Lyon, France
| | - P Berveiller
- Department of Gynecology and Obstetrics, Poissy-Saint Germain Hospital, Poissy; UMR 1198 - BREED, INRAE, Paris Saclay University, RHuMA, Montigny-Le-Bretonneux, France
| | - A Bosch
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund; Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - E Cardonick
- Cooper Medical School at Rowan University, Camden, USA
| | - C Denkert
- Philipps-University Marburg and Marburg University Hospital (UKGM), Marburg, Germany
| | - M J Halaska
- Department of Obstetrics and Gynaecology, Third Faculty of Medicine, Charles University in Prague and Universital Hospital Kralovske Vinohrady, Prague, Czech Republic
| | - M Hoeltzenbein
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Clinical Pharmacology and Toxicology, Embryotox Center of Clinical Teratology and Drug Safety in Pregnancy, Berlin, Germany
| | - A L V Johansson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Cancer Registry of Norway, Oslo, Norway
| | - C Maggen
- Department of Obstetrics and Prenatal Medicine, University Hospital Brussels, Brussels, Belgium
| | - U R Markert
- Placenta Lab, Department of Obstetrics, Jena University Hospital, Jena, Germany
| | - F Peccatori
- Gynecologic Oncology Department, European Institute of Oncology IRCCS, Milan, Italy
| | - P Poortmans
- Iridium Netwerk, Antwerp; University of Antwerp, Antwerp, Belgium
| | - E Saloustros
- Department of Oncology, University General Hospital of Larissa, Larissa, Greece
| | - C Saura
- Medical Oncology Department, Vall d'Hebron University Hospital, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - P Schmid
- Cancer Institute, Queen Mary University London, London, UK
| | - E Stamatakis
- Department of Anesthesiology, 'Alexandra' General Hospital, Athens, Greece
| | | | - M van Gerwen
- Gynecologic Oncology, Antoni van Leeuwenhoek-Netherlands Cancer Institute, Amsterdam; Department of Child and Adolescent Psychiatry and Psychosocial Care, Amsterdam UMC, University of Amsterdam; Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - V Vandecaveye
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - G Pentheroudakis
- European Society for Medical Oncology (ESMO), Lugano, Switzerland
| | - G Curigliano
- Division of Early Drug Development, European Institute of Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - F Amant
- Gynecologic Oncology, Antoni van Leeuwenhoek-Netherlands Cancer Institute, Amsterdam; Division Gynaecologic Oncology, UZ Leuven, Belgium
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30
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Rezkallah EMN, Elsaify A, Tin SMM, Elsaify W. Diagnostic Accuracy of Ultrasonography in Axillary Staging in Breast Cancer Patients. J Med Ultrasound 2023; 31:293-297. [PMID: 38264585 PMCID: PMC10802873 DOI: 10.4103/jmu.jmu_99_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/25/2022] [Accepted: 11/30/2022] [Indexed: 01/25/2024] Open
Abstract
Background Breast cancer is the most common malignancy affecting women all over the world and represents 7% of all cancer-related deaths in the UK. One of the most crucial elements in assessing a patient's prognosis and chance of survival with breast cancer is the condition of their axillary lymph nodes. Ultrasonography (US) is now used as a routine preoperative diagnostic tool for pretherapeutic axillary evaluation. The aim of the current study is to investigate the diagnostic accuracy of US in axillary staging in breast cancer patients. Methods We carried out this retrospective study for all invasive breast cancer patients who had surgery in addition to preoperative axillary staging using US during the period from January 2020 to February 2021. The final histology results were compared with the preoperative US findings to ascertain the sensitivity, specificity, positive predictive value, and negative predictive value of AUS in axillary staging. Results One hundred and twenty-eight patients were included in our study. The average age of diagnosis was 63.9 ± 12.3 years of age. We calculated sensitivity rate of 59.6%, specificity rate of 95.1%, positive predictive value of 87.5%, and negative predictive value of 80.2% with overall diagnostic accuracy of 82.2%. Conclusion Despite the important role of preoperative US in axillary staging in breast cancer patients; it failed to detect metastatic diseases in 14.8% of our patients. These findings necessitate the routine histological evaluation of the axilla for more accurate staging of the disease.
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Affiliation(s)
| | - Andrew Elsaify
- Foundation Doctor, Misr University for Science and Technology, Giza, Egypt
| | - Su Min Min Tin
- Department of General Surgery, James Cook University Hospital, Middlesbrough, UK
| | - Wael Elsaify
- Department of General Surgery, James Cook University Hospital, Middlesbrough, UK
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31
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van Hemert AKE, van Duijnhoven FH, Vrancken Peeters MJTFD. This house believes that: MARI/TAD is better than sentinel node biopsy after PST for cN+ patients. Breast 2023; 71:89-95. [PMID: 37562108 PMCID: PMC10432821 DOI: 10.1016/j.breast.2023.06.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/03/2023] [Accepted: 06/28/2023] [Indexed: 08/12/2023] Open
Abstract
The increasing use and effectiveness of primary systemic treatment (PST) enables tailored locoregional treatment. About one third of clinically node positive (cN+) breast cancer patients achieve pathologic complete response (pCR) of the axilla, with higher rates observed in Human Epidermal growth factor Receptor (HER)2-positive or triple negative (TN) breast cancer subtypes. Tailoring axillary treatment for patients with axillary pCR is necessary, as they are unlikely to benefit from axillary lymph node dissection (ALND), but may suffer complications and long-term morbidity such as lymphedema and impaired shoulder motion. By combining pre-PST and post-PST axillary staging techniques, ALND can be omitted in most cN + patients with pCR. Different post-PST staging techniques (MARI/TAD/SN) show low or ultra-low false negative rates for detection of residual disease. More importantly, trials using the MARI (Marking Axillary lymph nodes with Radioactive Iodine seeds) procedure or sentinel lymph node biopsy (SLNB) as axillary staging technique post-PST have already shown the safety of tailoring axillary treatment in patients with an excellent response. Tailored axillary treatment using the MARI procedure in stage I-III breast cancer resulted in 80% reduction of ALND and excellent five-year axillary recurrence free interval (aRFI) of 97%. Similar oncologic outcomes were seen for post-SLNB in stage I-II patients. The MARI technique requires only one invasive procedure pre-NST and a median of one node is removed post-PST, whereas for the SLNB and TAD techniques two to four nodes are removed. A disadvantage of the MARI technique is its use of radioactive iodine, which is subject to extensive regulations.
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Affiliation(s)
- Annemiek K E van Hemert
- Department of Surgical Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Plesmanlaan 121, 1066, CX, Amsterdam, the Netherlands
| | - Frederieke H van Duijnhoven
- Department of Surgical Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Plesmanlaan 121, 1066, CX, Amsterdam, the Netherlands
| | - Marie-Jeanne T F D Vrancken Peeters
- Department of Surgical Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Plesmanlaan 121, 1066, CX, Amsterdam, the Netherlands; Department of Surgery, Amsterdam University Medical Center, Meibergdreef 9, 1105, AZ, Amsterdam, the Netherlands.
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Pesapane F, Mariano L, Magnoni F, Rotili A, Pupo D, Nicosia L, Bozzini AC, Penco S, Latronico A, Pizzamiglio M, Corso G, Cassano E. Future Directions in the Assessment of Axillary Lymph Nodes in Patients with Breast Cancer. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1544. [PMID: 37763661 PMCID: PMC10534800 DOI: 10.3390/medicina59091544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/15/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
Background and Objectives: Breast cancer (BC) is a leading cause of morbidity and mortality worldwide, and accurate assessment of axillary lymph nodes (ALNs) is crucial for patient management and outcomes. We aim to summarize the current state of ALN assessment techniques in BC and provide insights into future directions. Materials and Methods: This review discusses various imaging techniques used for ALN evaluation, including ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography. It highlights advancements in these techniques and their potential to improve diagnostic accuracy. The review also examines landmark clinical trials that have influenced axillary management, such as the Z0011 trial and the IBCSG 23-01 trial. The role of artificial intelligence (AI), specifically deep learning algorithms, in improving ALN assessment is examined. Results: The review outlines the key findings of these trials, which demonstrated the feasibility of avoiding axillary lymph node dissection (ALND) in certain patient populations with low sentinel lymph node (SLN) burden. It also discusses ongoing trials, including the SOUND trial, which investigates the use of axillary ultrasound to identify patients who can safely avoid sentinel lymph node biopsy (SLNB). Furthermore, the potential of emerging techniques and the integration of AI in enhancing ALN assessment accuracy are presented. Conclusions: The review concludes that advancements in ALN assessment techniques have the potential to improve patient outcomes by reducing surgical complications while maintaining accurate disease staging. However, challenges such as standardization of imaging protocols and interpretation criteria need to be addressed. Future research should focus on large-scale clinical trials to validate emerging techniques and establish their efficacy and cost-effectiveness. Over-all, this review provides valuable insights into the current status and future directions of ALN assessment in BC, highlighting opportunities for improving patient care.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Luciano Mariano
- Breast Imaging Division, AOU Città della Scienza e della Salute di Torino, 10126 Turin, Italy;
| | - Francesca Magnoni
- Division of Breast Surgery, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (F.M.); (G.C.)
- European Cancer Prevention Organization (ECP), 20122 Milan, Italy
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Davide Pupo
- Radiology Division, Department of Precision Medicine, University of Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Anna Carla Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Antuono Latronico
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Maria Pizzamiglio
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
| | - Giovanni Corso
- Division of Breast Surgery, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (F.M.); (G.C.)
- European Cancer Prevention Organization (ECP), 20122 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy; (A.R.); (L.N.); (A.C.B.); (S.P.); (A.L.); (M.P.); (E.C.)
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Ferroni G, Sabeti S, Abdus-Shakur T, Scalise L, Carter JM, Fazzio RT, Larson NB, Fatemi M, Alizad A. Noninvasive prediction of axillary lymph node breast cancer metastasis using morphometric analysis of nodal tumor microvessels in a contrast-free ultrasound approach. Breast Cancer Res 2023; 25:65. [PMID: 37296471 PMCID: PMC10257266 DOI: 10.1186/s13058-023-01670-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE Changes in microcirculation of axillary lymph nodes (ALNs) may indicate metastasis. Reliable noninvasive imaging technique to quantify such variations is lacking. We aim to develop and investigate a contrast-free ultrasound quantitative microvasculature imaging technique for detection of metastatic ALN in vivo. EXPERIMENTAL DESIGN The proposed ultrasound-based technique, high-definition microvasculature imaging (HDMI) provides superb images of tumor microvasculature at sub-millimeter size scales and enables quantitative analysis of microvessels structures. We evaluated the new HDMI technique on 68 breast cancer patients with ultrasound-identified suspicious ipsilateral axillary lymph nodes recommended for fine needle aspiration biopsy (FNAB). HDMI was conducted before the FNAB and vessel morphological features were extracted, analyzed, and the results were correlated with the histopathology. RESULTS Out of 15 evaluated quantitative HDMI biomarkers, 11 were significantly different in metastatic and reactive ALNs (10 with P << 0.01 and one with 0.01 < P < 0.05). We further showed that through analysis of these biomarkers, a predictive model trained on HDMI biomarkers combined with clinical information (i.e., age, node size, cortical thickness, and BI-RADS score) could identify metastatic lymph nodes with an area under the curve of 0.9 (95% CI [0.82,0.98]), sensitivity of 90%, and specificity of 88%. CONCLUSIONS The promising results of our morphometric analysis of HDMI on ALNs offer a new means of detecting lymph node metastasis when used as a complementary imaging tool to conventional ultrasound. The fact that it does not require injection of contrast agents simplifies its use in routine clinical practice.
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Affiliation(s)
- Giulia Ferroni
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
| | - Soroosh Sabeti
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
| | - Tasneem Abdus-Shakur
- Department of Radiology, Mayo Clinic College of Medicine and Science, 200 1st. St. SW, Rochester, MN, 55905, USA
| | - Lorenzo Scalise
- Department of Industrial Engineering and Mathematical Science, Marche Polytechnic University, 60131, Ancona, Italy
| | - Jodi M Carter
- Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada
| | - Robert T Fazzio
- Department of Radiology, Mayo Clinic College of Medicine and Science, 200 1st. St. SW, Rochester, MN, 55905, USA
| | - Nicholas B Larson
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA
| | - Azra Alizad
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA.
- Department of Radiology, Mayo Clinic College of Medicine and Science, 200 1st. St. SW, Rochester, MN, 55905, USA.
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Kurt SA, Eryurekli AE, Kayadibi Y, Samanci C, Velidedeoglu M, Onur I, Arslan S, Taskin F. Diagnostic Performance of Superb Microvascular Imaging in Differentiating Benign and Malignant Axillary Lymph Nodes. Ultrasound Q 2023; 39:74-80. [PMID: 35943392 DOI: 10.1097/ruq.0000000000000617] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
ABSTRACT The aim was to evaluate the effectiveness of superb microvascular imaging (SMI) in axillary lymph nodes (LNs).Benign and malignant LNs diagnosed via histopathological examination constituted the study subgroups. In addition to grayscale findings for morphological evaluation, vascular patterns and appearance of internal vessels were analyzed by both power Doppler ultrasound (PDUS) and SMI. The number of vascular branches was counted, and a vascularity index (VI) was calculated by SMI.Fifty-two LNs with suspicious findings in terms of metastasis (33 malignant and 19 benign) were evaluated. Diagnostic accuracy according to vascular patterns was 82% for PDUS and 92% for SMI. In the presence of asymmetric cortical thickening, there was a significant difference between benign and malignant LNs in the number of vascular branches of both thin and thick cortical sides ( P < 0.01). Mean VI was significantly higher in the malignant group ( P < 0.05). In differentiating malignancy, when a cutoff VI value was set to 9%, sensitivity was 69.7%, and specificity was 63.2%.Evaluating the vascularity of axillary LNs by SMI is a useful tool in determining the potential of axillary metastasis, especially in the absence of typical sonographic findings. Superb microvascular imaging can beneficially be used to select the most suspicious LN and suspicious area of the LN to sample.
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Affiliation(s)
| | | | | | | | | | - Irem Onur
- Pathology, Istanbul University-Cerrahpasa
| | | | - Fusun Taskin
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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Kim SY, Choi Y, Kim YS, Ha SM, Lee SH, Han W, Kim HK, Cho N, Moon WK, Chang JM. Use of imaging prediction model for omission of axillary surgery in early-stage breast cancer patients. Breast Cancer Res Treat 2023; 199:489-499. [PMID: 37097375 DOI: 10.1007/s10549-023-06952-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 04/13/2023] [Indexed: 04/26/2023]
Abstract
PURPOSE To develop a prediction model incorporating clinicopathological information, US, and MRI to diagnose axillary lymph node (LN) metastasis with acceptable false negative rate (FNR) in patients with early stage, clinically node-negative breast cancers. METHODS In this single center retrospective study, the inclusion criteria comprised women with clinical T1 or T2 and N0 breast cancers who underwent preoperative US and MRI between January 2017 and July 2018. Patients were temporally divided into the development and validation cohorts. Clinicopathological information, US, and MRI findings were collected. Two prediction models (US model and combined US and MRI model) were created using logistic regression analysis from the development cohort. FNRs of the two models were compared using the McNemar test. RESULTS A total of 964 women comprised the development (603 women, 54 ± 11 years) and validation (361 women, 53 ± 10 years) cohorts with 107 (18%) and 77 (21%) axillary LN metastases in each cohort, respectively. The US model consisted of tumor size and morphology of LN on US. The combined US and MRI model consisted of asymmetry of LN number, long diameter of LN, tumor type, and multiplicity of breast cancers on MRI, in addition to tumor size and morphology of LN on US. The combined model showed significantly lower FNR than the US model in both development (5% vs. 32%, P < .001) and validation (9% vs. 35%, P < .001) cohorts. CONCLUSION Our prediction model combining US and MRI characteristics of index cancer and LN lowered FNR compared to using US alone, and could potentially lead to avoid unnecessary SLNB in early stage, clinically node-negative breast cancers.
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Affiliation(s)
- Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Yunhee Choi
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yeon Soo Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Su Min Ha
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Su Hyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Wonshik Han
- Department of Surgery, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Hong-Kyu Kim
- Department of Surgery, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
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Yu JG, Wu Z, Ming Y, Deng S, Li Y, Ou C, He C, Wang B, Zhang P, Wang Y. Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images. Med Image Anal 2023; 85:102748. [PMID: 36731274 DOI: 10.1016/j.media.2023.102748] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 10/25/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
Computerized identification of lymph node metastasis of breast cancer (BCLNM) from whole-slide pathological images (WSIs) can largely benefit therapy decision and prognosis analysis. Besides the general challenges of computational pathology, like extra-high resolution, very expensive fine-grained annotation, etc., two particular difficulties with this task lie in (1) modeling the significant inter-tumoral heterogeneity in BCLNM pathological images, and (2) identifying micro-metastases, i.e., metastasized tumors with tiny foci. Towards this end, this paper presents a novel weakly supervised method, termed as Prototypical Multiple Instance Learning (PMIL), to learn to predict BCLNM from WSIs with slide-level class labels only. PMIL introduces the well-established vocabulary-based multiple instance learning (MIL) paradigm into computational pathology, which is characterized by utilizing the so-called prototypes to model pathological data and construct WSI features. PMIL mainly consists of two innovatively designed modules, i.e., the prototype discovery module which acquires prototypes from training data by unsupervised clustering, and the prototype-based slide embedding module which builds WSI features by matching constitutive patches against the prototypes. Relative to existing MIL methods for WSI classification, PMIL has two substantial merits: (1) being more explicit and interpretable in modeling the inter-tumoral heterogeneity in BCLNM pathological images, and (2) being more effective in identifying micro-metastases. Evaluation is conducted on two datasets, i.e., the public Camelyon16 dataset and the Zbraln dataset created by ourselves. PMIL achieves an AUC of 88.2% on Camelyon16 and 98.4% on Zbraln (at 40x magnification factor), which consistently outperforms other compared methods. Comprehensive analysis will also be carried out to further reveal the effectiveness and merits of the proposed method.
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Affiliation(s)
- Jin-Gang Yu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China; Pazhou Laboratory, Guangzhou 510335, China
| | - Zihao Wu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Yu Ming
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Shule Deng
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Yuanqing Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China; Pazhou Laboratory, Guangzhou 510335, China
| | - Caifeng Ou
- Department of Breast Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
| | - Chunjiang He
- Department of Breast Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
| | - Baiye Wang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.
| | - Pusheng Zhang
- Department of Breast Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.
| | - Yu Wang
- Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China.
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Abbreviated MRI for Comprehensive Regional Lymph Node Staging during Pre-Operative Breast MRI. Cancers (Basel) 2023; 15:cancers15061859. [PMID: 36980744 PMCID: PMC10046951 DOI: 10.3390/cancers15061859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 03/11/2023] [Accepted: 03/17/2023] [Indexed: 03/22/2023] Open
Abstract
Background: The detection of regional lymph node metastases (LNM), in particular significant LNM (≥N2), is important to guide treatment decisions in women with breast cancer. The purpose of this study was to determine whether a coronal pulse sequence as part of pre-operative breast MRI is useful to identify women without significant LNM. Material: Retrospective study between January 2017 and December 2019 on 414 consecutive women with breast cancer who underwent pre-operative breast MRI on a 1.5 T system. For lymph node (LN) staging, a coronal pre-contrast non-fat-suppressed T1-weighted TSE sequence was acquired with the system’s built-in body coil, covering the chest wall; acquisition time 3:12 min. Two radiologists rated the likelihood of LNM on a 3-point scale (absent/possible/present). Validation was obtained by histology from sentinel LN biopsy, axillary LN dissection, and/or PET/CT. Results: 368/414 women were staged to have no or non-significant LNM (pN0 in 282/414, pN1 in 86/414), and significant LNM (≥pN2) in 46/414. For identification of women with significant LNM, MRI was true-positive in 42/46, false-negative in 4/46, true-negative in 327/368, and false-positive in 41/83, the latter mostly caused by women with N1-disease (38/41), yielding an NPV and PPV for significant LNM of 98.8% [95%-CI: 97.0–100%] and 50.6% [43.1–58.1%], respectively. Conclusions: A 3 min coronal T1-weighted pulse sequence covering the chest wall as part of pre-operative breast MRI is useful to rule out significant LNM with high NPV. Where MRI staging is positive for significant LNM, additional work-up is indicated to improve the distinction of N1 and N2 disease.
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Wang X, Zhang G, Zuo Z, Zhu Q, Liu Z, Wu S, Li J, Du J, Yan C, Ma X, Shi Y, Shi H, Zhou Y, Mao F, Lin Y, Shen S, Zhang X, Sun Q. A novel nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer. Cancer Med 2023; 12:7039-7050. [PMID: 36524283 PMCID: PMC10067027 DOI: 10.1002/cam4.5503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 10/29/2022] [Accepted: 11/22/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND OR PURPOSE A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status. METHODS Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n = 472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models. RESULTS In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p = 0.003); Her-2 status (p = 0.049); and ALN-US shape (p = 0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p = 0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability. CONCLUSION This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.
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Affiliation(s)
- Xue‐fei Wang
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Guo‐chao Zhang
- Department of Thoracic SurgeryNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhi‐chao Zuo
- Radiology Department, Xiangtan Central HospitalHunanChina
| | - Qing‐li Zhu
- Ultrasound Medicine DepartmentChinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College HospitalBeijingChina
| | - Zhen‐zhen Liu
- Ultrasound Medicine DepartmentChinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College HospitalBeijingChina
| | - Sha‐fei Wu
- Molecular Pathology Research Center, Department of PathologyPeking Union Medical College Hospital, Chinese Academy of Medical SciencesBeijingChina
| | - Jia‐xin Li
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Jian‐hua Du
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Cun‐li Yan
- Breast Surgery DepartmentBaoji Maternal and Child Health HospitalShaanxiChina
| | - Xiao‐ying Ma
- Breast Surgery DepartmentQinghai Provincial People's HospitalQinghaiChina
| | - Yue Shi
- Breast Surgery DepartmentShanxi Traditional Chinese Medical HospitalShanxiChina
| | - He Shi
- Breast Surgery DepartmentShanxi Traditional Chinese Medical HospitalShanxiChina
| | - Yi‐dong Zhou
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Feng Mao
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Yan Lin
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Song‐jie Shen
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Xiao‐hui Zhang
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Qiang Sun
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
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Surgical Planning after Neoadjuvant Treatment in Breast Cancer: A Multimodality Imaging-Based Approach Focused on MRI. Cancers (Basel) 2023; 15:cancers15051439. [PMID: 36900231 PMCID: PMC10001061 DOI: 10.3390/cancers15051439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
Neoadjuvant chemotherapy (NACT) today represents a cornerstone in the treatment of locally advanced breast cancer and highly chemo-sensitive tumors at early stages, increasing the possibilities of performing more conservative treatments and improving long term outcomes. Imaging has a fundamental role in the staging and prediction of the response to NACT, thus aiding surgical planning and avoiding overtreatment. In this review, we first examine and compare the role of conventional and advanced imaging techniques in preoperative T Staging after NACT and in the evaluation of lymph node involvement. In the second part, we analyze the different surgical approaches, discussing the role of axillary surgery, as well as the possibility of non-operative management after-NACT, which has been the subject of recent trials. Finally, we focus on emerging techniques that will change the diagnostic assessment of breast cancer in the near future.
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Laws A, Kantor O, King TA. Surgical Management of the Axilla for Breast Cancer. Hematol Oncol Clin North Am 2023; 37:51-77. [PMID: 36435614 DOI: 10.1016/j.hoc.2022.08.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
This review discusses the contemporary surgical management of the axilla in patients with breast cancer. Surgical paradigms are highlighted by clinical nodal status at presentation and treatment approach, including upfront surgery and neoadjuvant systemic therapy settings. This review focuses on the increasing opportunities for de-escalating the extent of axillary surgery in the era of sentinel lymph node biopsy, while also reviewing the remaining indications for axillary clearance with axillary lymph node dissection.
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Affiliation(s)
- Alison Laws
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Harvard Medical School, Boston, MA, USA
| | - Olga Kantor
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Harvard Medical School, Boston, MA, USA
| | - Tari A King
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Harvard Medical School, Boston, MA, USA.
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Li J, Wang SR, Li QL, Zhu T, Zhu PS, Chen M, Cui XW. Diagnostic value of multiple ultrasound diagnostic techniques for axillary lymph node metastases in breast cancer: A systematic analysis and network meta-analysis. Front Oncol 2023; 12:1043185. [PMID: 36686798 PMCID: PMC9853394 DOI: 10.3389/fonc.2022.1043185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/25/2022] [Indexed: 01/09/2023] Open
Abstract
Background Early diagnosis of axillary lymph node metastasis is very important for the recurrence and prognosis of breast cancer. Currently, Lymph node biopsy is one of the important methods to detect lymph node metastasis in breast cancer, however, its invasiveness might bring complications to patients. Therefore, this study investigated the diagnostic performance of multiple ultrasound diagnostic methods for axillary lymph node metastasis of breast cancer. Materials and methods In this study, we searched PubMed, Web of Science, CNKI and Wan Fang databases, conducted Bayesian network meta-analysis (NMA) on the studies that met the inclusion criteria, and evaluated the consistency of five different ultrasound imaging techniques in axillary lymph node metastasis of breast cancer. Funnel graph was used to evaluate whether it had publication bias. The diagnostic performance of each ultrasound imaging method was ranked using SUCRA. Results A total of 22 papers were included, US+CEUS showed the highest SUCRA values in terms of sensitivity (SEN) (0.874), specificity (SPE) (0.911), positive predictive value (PPV) (0.972), negative predictive value (NPV) (0.872) and accuracy (ACC) (0.990). Conclusion In axillary lymph node metastasis of breast cancer, the US+CEUS combined diagnostic method showed the highest SUCRA value among the five ultrasound diagnostic methods. This study provides a theoretical basis for preoperative noninvasive evaluation of axillary lymph node metastases in breast cancer patients and clinical treatment decisions. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022351977.
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Affiliation(s)
- Jun Li
- Department of Medical Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China,NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases (First Affiliated Hospital, School of Medicine, Shihezi University), Shihezi, Xinjiang, China,*Correspondence: Jun Li, ; Xin-Wu Cui,
| | - Si-Rui Wang
- Department of Medical Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China,NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases (First Affiliated Hospital, School of Medicine, Shihezi University), Shihezi, Xinjiang, China
| | - Qiao-Li Li
- Department of Medical Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China,NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases (First Affiliated Hospital, School of Medicine, Shihezi University), Shihezi, Xinjiang, China
| | - Tong Zhu
- School of Medicine, Shihezi University, Shihezi, China
| | - Pei-Shan Zhu
- Department of Medical Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China,NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases (First Affiliated Hospital, School of Medicine, Shihezi University), Shihezi, Xinjiang, China
| | - Ming Chen
- Department of Medical Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Jun Li, ; Xin-Wu Cui,
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Aladag Kurt S, Kayadibi Y, Onur I, Uslu Besli L, Necati Sanli A, Velidedeoglu M. Predicting axillary nodal metastasis based on the side of asymmetrical cortical thickening in breast cancer: Evaluation with grayscale and microvascular imaging findings. Eur J Radiol 2023; 158:110643. [PMID: 36535079 DOI: 10.1016/j.ejrad.2022.110643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To investigate the relationship between sonographic findings and the axillary status, especially the side of thickening in the presence of cortical asymmetry. METHODS Patients with biopsy-proven axillary lymph node (ALN) metastasis were included in this study. The lymph nodes were divided into three groups depending on the type of cortical thickening as diffuse, closer (eccentric cortical thickening on the side near the tumor and/or breast) and distant (thickening on the further side) asymmetry. Longitudinal to transverse axis (L/T) ratio, the largest cortical thickness, cortex to hilum ratio (C/H), hilar status (normal/displaced/absent), orientation (parallel/vertical), capsular integrity (sharp/indistinct), vascularisation pattern (hilar/peripheral/penetrant/anarchic/avascular) on superb microvascular imaging (SMI) and presence of conglomeration were recorded for each lymph node. Axillary nodal status on 18F-FDG PET-CT/MRI scans was recorded, if available. Features of the breast lesions like size, laterality, nuclear grade, hormone receptor status and the level of Ki-67 expression have been added. RESULTS A total of 219 metastatic ALNs [diffuse (n = 122), closer asymmetry (n = 71), distant asymmetry (n = 26)] were evaluated. By the univariate analysis, ALN metastasis was significantly associated with the presence of closer asymmetrical cortical thickening (p < 0,0001), C/H ratio (p = 0.001), cortical thickness (p = 0.001), hilar status (p < 0.005) and vascular pattern (p < 0.005). L/T ratio was only a statistically significant parameter for lymph nodes with diffuse cortical enlargement in predicting metastasis, and conglomeration was also observed only in this group (p < 0.05). By multivariate analysis, nodal metastasis was significantly associated with asymmetrical cortical thickening (p = 0.001), C/H ratio (p = 0.005) and vascular pattern (p < 0.0001). CONCLUSION Asymmetrical cortical enlargement on the side closer to the breast, C/H ratio and abnormal microvascular pattern are the independent predictors of axillary nodal involvement. Closer asymmetry is an eligible, easy-to-detect grayscale US finding to decide sampling that highly predicts ALN metastasis.
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Affiliation(s)
- Seda Aladag Kurt
- Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Radiology, Kocamustafapasa, Istanbul 34098, Turkey.
| | - Yasemin Kayadibi
- Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Radiology, Kocamustafapasa, Istanbul 34098, Turkey.
| | - Irem Onur
- Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Pathology, Kocamustafapasa, Istanbul 34098, Turkey
| | - Lebriz Uslu Besli
- Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Nuclear Medicine, Kocamustafapasa, Istanbul 34098, Turkey.
| | - Ahmet Necati Sanli
- Department of General Surgery, Abdulkadir Yuksel State Hospital, Gaziantep 27090, Turkey
| | - Mehmet Velidedeoglu
- Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of General Surgery, Kocamustafapasa, Istanbul 34098, Turkey.
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Niu Z, Gao Y, Xiao M, Mao F, Zhou Y, Zhu Q, Jiang Y. Contrast-enhanced lymphatic US can improve the preoperative diagnostic performance for sentinel lymph nodes in early breast cancer. Eur Radiol 2023; 33:1593-1602. [PMID: 36152038 PMCID: PMC9510155 DOI: 10.1007/s00330-022-09139-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 08/20/2022] [Accepted: 09/05/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVES To evaluate the preoperative diagnostic value of contrast-enhanced lymphatic ultrasound (CEUS) for the sentinel lymph node (SLN) status in early breast cancer. MATERIALS AND METHODS We prospectively recruited 102 consecutive patients with clinically node-negative early breast cancer from July 2021 to October 2021. All patients underwent conventional US and percutaneous CEUS examinations. The CEUS of SLNs were classified into four enhancement patterns: homogeneous (I), featured inhomogeneous (II), focal defect (III), and no enhancement (IV). The diagnostic performance of conventional US and CEUS for SLN metastasis was assessed by receiver operating characteristic (ROC) curves and decision curves. RESULTS A total of 78 women were enrolled in this study, including 55, 18, and 5 patients with negative axilla, 1-2, and ≥ 3 metastastic SLNs pathologically, respectively. The identification rate of SLNs by CEUS was 100%. Patterns I and II can select 91.7% (44/48) of patients with disease-free axilla, while patterns III and IV had higher percentages of metastasis (65.2%, p < 0.001 and 57.1%, p < 0.002, respectively). For the SLN metastatic burden, 100% (48/48) of patients with pattern I/II had ≤ 2 metastatic SLNs. Compared with conventional US, the CEUS enhancement patterns showed significant improvement in diagnosing metastatic SLNs (0.813 vs 0.601, p < 0.001). CEUS had greater clinical benefits and correctly reclassified 48% of metastatic SLNs (p < 0.001) without sacrificing the classification accuracy of negative SLNs (p = 0.25), and could improve prediction accuracy by 0.42 (p < 0.001). CONCLUSIONS CEUS demonstrated better diagnostic performance and greater clinical benefits than conventional US for the preoperative diagnosis of SLNs, showing its potential to select candidates for precluding axillary surgery in early breast cancer. KEY POINTS • The homogeneous and featured inhomogeneous enhancement of SLNs are highly suggestive of negative LNs, while focal defect (p < 0.001) and no enhancement (p < 0.002) patterns had higher percentages of metastasis. • The proportion of SLNs with highly suspicious signs on conventional US increases as the type of enhancement pattern increases (no suspicious signs in pattern I/II, 34.8% in pattern III, and 85.7% in pattern IV). • Compared with conventional US, CEUS improved the area under the receiver operating characteristic curve (0.813 vs. 0.601, p < 0.001) and had greater clinical benefits (IDI = 0.42, p < 0.001) for the diagnosis of axillary metastasis.
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Affiliation(s)
- Zihan Niu
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730 People’s Republic of China
| | - Yuanjing Gao
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730 People’s Republic of China
| | - Mengsu Xiao
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730 People’s Republic of China
| | - Feng Mao
- Department of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730 People’s Republic of China
| | - Yidong Zhou
- Department of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730 People’s Republic of China
| | - Qingli Zhu
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, People's Republic of China.
| | - Yuxin Jiang
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, People's Republic of China.
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Kim H, Han BK, Ko EY, Ko ES, Choi JS. Magnetic resonance imaging evaluation of single axillary lymph node metastasis in breast cancer: Emphasis on the location of lymph nodes. Medicine (Baltimore) 2022; 101:e31836. [PMID: 36550794 PMCID: PMC9771340 DOI: 10.1097/md.0000000000031836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
To evaluate the frequency and location of abnormal lymph nodes (LNs) in breast cancer patients with a single axillary lymph node (ALN) metastasis on breast magnetic resonance imaging (MRI). We retrospectively reviewed the MRI findings of 219 consecutive patients with breast cancer with single ALN metastasis who were surgically confirmed at our institution between January 2018 and December 2018. The morphological features and locations of the abnormal LN on MRI were analyzed. Pathology reports were reviewed to evaluate the size of the metastases and whether they were sentinel LNs (SLNs). Of the 219 patients with a single ALN metastasis, 56 (25.6%) showed abnormal MRI findings. Of these, 54 (96.4%) had either the lowest or second-lowest LN in the level I axilla. In 184 (91.5%) of 201 patients who underwent SLN biopsy, the metastatic LN were SLN. Macrometastases were found more frequently in cases with abnormal LNs than in those with normal-looking LNs (P = .004). The most frequent morphological feature of metastatic ALNs was a diffuse cortical thickening of 3 to 5 mm (37.5%). Although MRI findings of single ALN metastasis in breast cancer patients are none or minimal, abnormalities are observed in the lowest or second-lowest LN in the lower axilla when present, suggesting the location of the SLNs.
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Affiliation(s)
- Haejung Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Boo-Kyung Han
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- * Correspondence: Boo-Kyung Han, Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea (e-mail: )
| | - Eun Young Ko
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Eun Sook Ko
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Ji Soo Choi
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Zhang X, Liu M, Ren W, Sun J, Wang K, Xi X, Zhang G. Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model. Front Oncol 2022; 12:1069733. [PMID: 36561533 PMCID: PMC9763602 DOI: 10.3389/fonc.2022.1069733] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/15/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose To develop a multiparametric MRI model for predicting axillary lymph node metastasis in invasive breast cancer. Methods Clinical data and T2WI, DWI, and DCE-MRI images of 252 patients with invasive breast cancer were retrospectively analyzed and divided into the axillary lymph node metastasis (ALNM) group and non-ALNM group using biopsy results as a reference standard. The regions of interest (ROI) in T2WI, DWI, and DCE-MRI images were segmented using MATLAB software, and the ROI was unified into 224 × 224 sizes, followed by image normalization as input to T2WI, DWI, and DCE-MRI models, all of which were based on ResNet 50 networks. The idea of a weighted voting method in ensemble learning was employed, and then T2WI, DWI, and DCE-MRI models were used as the base models to construct a multiparametric MRI model. The entire dataset was randomly divided into training sets and testing sets (the training set 202 cases, including 78 ALNM, 124 non-ALNM; the testing set 50 cases, including 20 ALNM, 30 non-ALNM). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of models were calculated. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic performance of each model for axillary lymph node metastasis, and the DeLong test was performed, P< 0.05 statistically significant. Results For the assessment of axillary lymph node status in invasive breast cancer on the test set, multiparametric MRI models yielded an AUC of 0.913 (95% CI, 0.799-0.974); T2WI-based model yielded an AUC of 0.908 (95% CI, 0.792-0.971); DWI-based model achieved an AUC of 0.702 (95% CI, 0.556-0.823); and the AUC of the DCE-MRI-based model was 0.572 (95% CI, 0.424-0.711). The improvement in the diagnostic performance of the multiparametric MRI model compared with the DWI and DCE-MRI-based models were significant (P< 0.01 for both). However, the increase was not meaningful compared with the T2WI-based model (P = 0.917). Conclusion Multiparametric MRI image analysis based on an ensemble CNN model with deep learning is of practical application and extension for preoperative prediction of axillary lymph node metastasis in invasive breast cancer.
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Affiliation(s)
- Xiaodong Zhang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Menghan Liu
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Wanqing Ren
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Jingxiang Sun
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Kesong Wang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Xiaoming Xi
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Guang Zhang
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,*Correspondence: Guang Zhang,
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Fong W, Tan L, Tan C, Wang H, Liu F, Tian H, Shen S, Gu R, Hu Y, Jiang X, Mei J, Liang J, Hu T, Chen K, Yu F. Predicting the risk of axillary lymph node metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features and the use of nomograms: a prospective single-center observational study. Eur Radiol 2022; 32:8200-8212. [PMID: 36169686 DOI: 10.1007/s00330-022-08855-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 04/24/2022] [Accepted: 05/01/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES The purpose of this study was to establish two preoperative nomograms to evaluate the risk for axillary lymph node (ALN) metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features. METHODS We prospectively evaluated 593 consecutive female participants who were diagnosed with cT1-3N0-1M0 breast cancer between March 2018 and May 2019 at Sun Yat-Sen Memorial Hospital. The participants were randomly classified into training and validation sets in a 4:1 ratio for the development and validation of the nomograms, respectively. Multivariate logistic regression analysis was performed to identify independent predictors of ALN status. We developed Nomogram A and Nomogram B to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2), respectively. RESULTS A total of 528 participants were evaluated in the final analyses. Multivariable analysis revealed that the number of suspicious lymph nodes, long axis, short-to-long axis ratio, cortical thickness, tumor location, and histological grade were independent predictors of ALN status. The AUCs of nomogram A in the training and validation groups were 0.83 and 0.78, respectively. The AUCs of nomogram B in the training and validation groups were 0.87 and 0.87, respectively. Both nomograms were well-calibrated. CONCLUSION We developed two preoperative nomograms that can be used to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2) in early breast cancer patients. Both nomograms are useful tools that will help clinicians predict the risk of ALN metastasis and facilitate therapy decision-making about axillary surgery. KEY POINTS • We developed two preoperative nomograms to predict axillary lymph node status based on ultrasonographic-clinicopathologic features. • Nomogram A was used to predict axillary lymph node metastasis (presence vs. absence). The AUCs in the training and validation groups were 0.83 and 0.78, respectively. Nomogram B was used to estimate the number of metastatic lymph nodes ( ≤ 2 vs. > 2). The AUCs in the training and validation group were 0.87 and 0.87, respectively. • Our nomograms may help clinicians weigh the risks and benefits of axillary surgery more appropriately.
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Affiliation(s)
- Wengcheng Fong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China
| | - Luyuan Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China
| | - Cui Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Pathology, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Hongli Wang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Fengtao Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Huan Tian
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China
| | - Shiyu Shen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ran Gu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yue Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaofang Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jingsi Mei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jing Liang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Tingting Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China
| | - Kai Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China. .,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China. .,Artificial Intelligence Laboratory, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Fengyan Yu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China. .,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China.
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Man V, Luk WP, Fung LH, Kwong A. The role of pre-operative axillary ultrasound in assessment of axillary tumor burden in breast cancer patients: a systematic review and meta-analysis. Breast Cancer Res Treat 2022; 196:245-254. [PMID: 36138294 DOI: 10.1007/s10549-022-06699-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/27/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Recent studies have suggested that a significant proportion of patients with axillary nodal metastases diagnosed by pre-operative axillary ultrasound (AUS)-guided needle biopsy were over-treated with axillary lymph node dissection (ALND). The role of routine AUS and needle biopsy in early breast cancer was questioned. This review aims to determine if pre-operative AUS could predict the extent of axillary tumor burden and need of ALND. METHODS PubMed and Embase literature databases were searched systematically for abnormal AUS characteristics and axillary nodal burden. Studies were eligible if they correlated the sonographic abnormalities in AUS with the resultant axillary nodal burden in ALND according to the ACOSOG Z0011 criteria. RESULTS Eleven retrospective studies and one prospective study with 1658 patients were included. Sixty-five percent of patients with one abnormal lymph node in AUS and 56% of those with two had low axillary nodal burden. Using one abnormal lymph node as the cut-off, the pooled sensitivity and specificity in prediction of axillary nodal burden were 66% (95%CI 63-69%) and 73% (95% CI 70-76%), respectively. Across the six studies that evaluated suspicious nodal characteristics, increased nodal cortical thickness may be associated with high axillary nodal burden. CONCLUSION More than half of the patients with pre-operative positive AUS and biopsy proven axillary nodal metastases were over-treated by ALND. Quantification of suspicious nodes and extent of cortical morphological changes in AUS may help identify suitable patients for sentinel lymph node biopsy.
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Affiliation(s)
- Vivian Man
- Division of Breast Surgery, Department of Surgery, The University of Hong Kong Li Ka Shing Faculty of Medicine, Queen Mary Hospital, K1401, Hong Kong, Hong Kong SAR
| | - Wing-Pan Luk
- Medical Physics and Research Department, Hong Kong Sanatorium and Hospital, Hong Kong, Hong Kong SAR
| | - Ling-Hiu Fung
- Medical Physics and Research Department, Hong Kong Sanatorium and Hospital, Hong Kong, Hong Kong SAR
| | - Ava Kwong
- Chief of Breast Surgery Division, Department of Surgery,, Daniel CK Yu Professor in Breast Cancer Research, The University of Hong Kong Li Ka Shing Faculty of Medicine, Queen Mary Hospital, K1401, Hong Kong, Hong Kong SAR.
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Togawa R, Binder LL, Feisst M, Barr RG, Fastner S, Gomez C, Hennigs A, Nees J, Pfob A, Schäfgen B, Stieber A, Riedel F, Heil J, Golatta M. Shear wave elastography as a supplemental tool in the assessment of unsuspicious axillary lymph nodes in patients undergoing breast ultrasound examination. Br J Radiol 2022; 95:20220372. [DOI: 10.1259/bjr.20220372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Objectives: To define reference values for shear wave elastography (SWE) in unsuspicious axillary lymph nodes in patients undergoing breast ultrasound examination. Methods: In total, 177 clinically and sonographically unsuspicious axillary lymph nodes were prospectively evaluated with SWE using Virtual Touch Tissue Imaging Quantification (VTIQ) in 175 women. Mean values of tissue stiffness for axillary fatty tissue, lymph node cortex, and lymph node hilus were measured. Additionally, test-retest reliability of SWE in the assessment of axillary lymph node stiffness was evaluated by repeating each measurement three times. Results: In 177 axillary lymph nodes, the mean stiffness of lymph node cortex, hilus, and surrounding fatty tissue as quantified by SWE was 1.90 m/s (SD: 0.34 m/s), 2.02 m/s (SD: 0.37 m/s), and 1.75 m/s (SD: 0.38 m/s), respectively. The mean stiffness of cortex and hilus was significantly higher compared to fatty tissue (p < 0.0001). SWE demonstrated good test–retest reliability in the assessment of stiffness of the lymph node hilus, cortex, and the surrounding fatty tissue with an intraclass correlation of 0.79 (95% CI: 0.75; 0.83), 0.75 (95% CI: 0.70; 0.79), and 0.78 (95% CI: 0.74; 0.82), respectively, (p < 0.0001). Conclusions: Reference values for SWE in unsuspicious axillary lymph nodes are determined. These results may help to better identify axillary lymph node metastasis for breast cancer patients when combined with other lymph node features. SWE is a reliable method for the objective quantification of tissue stiffness of axillary lymph nodes. Advances in knowledge: This study presents physiological reference values for tissue stiffness by examining the axillary lymph nodes with SWE in 175 women with sonomorphologically unsuspicious lymph nodes.
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Affiliation(s)
- Riku Togawa
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Leah-Larissa Binder
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Manuel Feisst
- Institute of Medical Biometry (IMBI), Heidelberg University, Heidelberg, Germany
| | - Richard G. Barr
- Department of Radiology, Northeastern Ohio Medical University, OH, United States
| | - Sarah Fastner
- Breast Unit, Sankt Elisabeth Hospital, Heidelberg, Germany
| | - Christina Gomez
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - André Hennigs
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Juliane Nees
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - André Pfob
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Benedikt Schäfgen
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne Stieber
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Riedel
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jörg Heil
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
- Breast Unit, Sankt Elisabeth Hospital, Heidelberg, Germany
| | - Michael Golatta
- Breast Unit,Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
- Breast Unit, Sankt Elisabeth Hospital, Heidelberg, Germany
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Di Paola V, Mazzotta G, Pignatelli V, Bufi E, D’Angelo A, Conti M, Panico C, Fiorentino V, Pierconti F, Kilburn-Toppin F, Belli P, Manfredi R. Beyond N Staging in Breast Cancer: Importance of MRI and Ultrasound-based Imaging. Cancers (Basel) 2022; 14:cancers14174270. [PMID: 36077805 PMCID: PMC9454572 DOI: 10.3390/cancers14174270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/27/2022] [Accepted: 08/30/2022] [Indexed: 12/29/2022] Open
Abstract
The correct N-staging in breast cancer is crucial to tailor treatment and stratify the prognosis. N-staging is based on the number and the localization of suspicious regional nodes on physical examination and/or imaging. Since clinical examination of the axillary cavity is associated with a high false negative rate, imaging modalities play a central role. In the presence of a T1 or T2 tumor and 0–2 suspicious nodes, on imaging at the axillary level I or II, a patient should undergo sentinel lymph node biopsy (SLNB), whereas in the presence of three or more suspicious nodes at the axillary level I or II confirmed by biopsy, they should undergo axillary lymph node dissection (ALND) or neoadjuvant chemotherapy according to a multidisciplinary approach, as well as in the case of internal mammary, supraclavicular, or level III axillary involved lymph nodes. In this scenario, radiological assessment of lymph nodes at the time of diagnosis must be accurate. False positives may preclude a sentinel lymph node in an otherwise eligible woman; in contrast, false negatives may lead to an unnecessary SLNB and the need for a second surgical procedure. In this review, we aim to describe the anatomy of the axilla and breast regional lymph node, and their diagnostic features to discriminate between normal and pathological nodes at Ultrasound (US) and Magnetic Resonance Imaging (MRI). Moreover, the technical aspects, the advantage and limitations of MRI versus US, and the possible future perspectives are also analyzed, through the analysis of the recent literature.
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Affiliation(s)
- Valerio Di Paola
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Correspondence: or
| | - Giorgio Mazzotta
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Vincenza Pignatelli
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Enida Bufi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Anna D’Angelo
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Marco Conti
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Camilla Panico
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Vincenzo Fiorentino
- Institute of Pathology, Università Cattolica del S. Cuore, Fondazione Policlinico “A. Gemelli”, 00168 Rome, Italy
| | - Francesco Pierconti
- Institute of Pathology, Università Cattolica del S. Cuore, Fondazione Policlinico “A. Gemelli”, 00168 Rome, Italy
| | - Fleur Kilburn-Toppin
- Cambridge Breast Unit, Cambridge University Hospital NHS Foundation Trust, Addenbrookes’ Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - Paolo Belli
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Riccardo Manfredi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
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Li Z, Gao Y, Gong H, Feng W, Ma Q, Li J, Lu X, Wang X, Lei J. Different Imaging Modalities for the Diagnosis of Axillary Lymph Node Metastases in Breast Cancer: A Systematic Review and Network Meta-Analysis of Diagnostic Test Accuracy. J Magn Reson Imaging 2022; 57:1392-1403. [PMID: 36054564 DOI: 10.1002/jmri.28399] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Accurate diagnosis of axillary lymph node metastasis (ALNM) of breast cancer patients is important to guide local and systemic treatment. PURPOSE To evaluate the diagnostic performance of different imaging modalities for ALNM in patients with breast cancer. STUDY TYPE Systematic review and network meta-analysis (NMA). SUBJECTS Sixty-one original articles with 8011 participants. FIELD STRENGTH 1.5 T and 3.0 T. ASSESSMENT We used the QUADAS-2 and QUADAS-C tools to assess the risk of bias in eligible studies. The identified articles assessed ultrasonography (US), MRI, mammography, ultrasound elastography (UE), PET, CT, PET/CT, scintimammography, and PET/MRI. STATISTICAL ANALYSIS We used random-effects conventional meta-analyses and Bayesian network meta-analyses for data analyses. We used sensitivity and specificity, relative sensitivity and specificity, superiority index, and summary receiver operating characteristic curve (SROC) analysis to compare the diagnostic value of different imaging modalities. RESULTS Sixty-one studies evaluated nine imaging modalities. At patient level, sensitivities of the nine imaging modalities ranged from 0.27 to 0.84 and specificities ranged from 0.84 to 0.95. Patient-based NMA showed that UE had the highest superiority index (5.95) with the highest relative sensitivity of 1.13 (95% confidence interval [CI]: 0.93-1.29) among all imaging methods when compared to US. At lymph node level, MRI had the highest superiority index (6.91) with highest relative sensitivity of 1.13 (95% CI: 1.01-1.23) and highest relative specificity of 1.11 (95% CI: 0.95-1.23) among all imaging methods when compared to US. SROCs also showed that UE and MRI had the largest area under the curve (AUC) at patient level and lymph node level of 0.92 and 0.94, respectively. DATA CONCLUSION UE and MRI may be superior to other imaging modalities in the diagnosis of ALNM in breast cancer patients at the patient level and the lymph node level, respectively. Further studies are needed to provide high-quality evidence to validate our findings. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zhifan Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.,Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Hengxin Gong
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Wen Feng
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Qinqin Ma
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Jinkui Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Xingru Lu
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Xiaohui Wang
- Department of Obstetrics and Gynecology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Junqiang Lei
- Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
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