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Sheikhy A, Dehghani Firouzabadi F, Lay N, Jarrah N, Yazdian Anari P, Malayeri A. State of the art review of AI in renal imaging. Abdom Radiol (NY) 2025:10.1007/s00261-025-04963-3. [PMID: 40293518 DOI: 10.1007/s00261-025-04963-3] [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: 11/22/2024] [Revised: 04/15/2025] [Accepted: 04/18/2025] [Indexed: 04/30/2025]
Abstract
Renal cell carcinoma (RCC) as a significant health concern, with incidence rates rising annually due to increased use of cross-sectional imaging, leading to a higher detection of incidental renal lesions. Differentiation between benign and malignant renal lesions is essential for effective treatment planning and prognosis. Renal tumors present numerous histological subtypes with different prognoses, making precise subtype differentiation crucial. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), shows promise in radiological analysis, providing advanced tools for renal lesion detection, segmentation, and classification to improve diagnosis and personalize treatment. Recent advancements in AI have demonstrated effectiveness in identifying renal lesions and predicting surveillance outcomes, yet limitations remain, including data variability, interpretability, and publication bias. In this review we explored the current role of AI in assessing kidney lesions, highlighting its potential in preoperative diagnosis and addressing existing challenges for clinical implementation.
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Affiliation(s)
- Ali Sheikhy
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
| | - Fatemeh Dehghani Firouzabadi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Nathan Lay
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
- Artificial Intelligence Resource, National Institutes of Health, Bethesda, USA
| | - Negin Jarrah
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
| | - Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
| | - Ashkan Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA.
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Rabe M, Kurz C, Thummerer A, Landry G. Artificial intelligence for treatment delivery: image-guided radiotherapy. Strahlenther Onkol 2025; 201:283-297. [PMID: 39138806 DOI: 10.1007/s00066-024-02277-9] [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: 03/01/2024] [Accepted: 07/07/2024] [Indexed: 08/15/2024]
Abstract
Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.
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Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Adrian Thummerer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- German Cancer Consortium (DKTK), partner site Munich, a partnership between the DKFZ and the LMU University Hospital Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- Bavarian Cancer Research Center (BZKF), Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
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Mamiya H, Tochigi T, Hayano K, Ohira G, Imanishi S, Maruyama T, Kurata Y, Takahashi Y, Hirata A, Matsubara H. Texture analysis of CT colonography to develop a novel imaging biomarker for the management of colorectal cancer. Ann Gastroenterol Surg 2025; 9:145-152. [PMID: 39759993 PMCID: PMC11693556 DOI: 10.1002/ags3.12852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 07/26/2024] [Accepted: 07/29/2024] [Indexed: 01/07/2025] Open
Abstract
Background Recent studies have focused on evaluating the biomarker value of textural features in radiological images. Our study investigated whether or not a texture analysis of computed tomographic colonography (CTC) images could be a novel biomarker for colorectal cancer (CRC). Methods This retrospective study investigated 263 patients with CRC who underwent contrast-enhanced CTC (CE-CTC) before curative surgery between January 2014 and December 2017. Multiple texture analyses (fractal, histogram, and gray-level co-occurrence matrix [GLCM] texture analyses) were applied to CE-CTC (portal-venous phase), and fractal dimension (FD), skewness, kurtosis, entropy, and GLCM texture parameters, including GLCM-correlation, GLCM-autocorrelation, GLCM-entropy, and GLCM-homogeneity, of the tumor were calculated. These texture parameters were compared with pathological factors (tumor depth, lymph node metastasis, vascular invasion, and lymphatic invasion) and overall survival (OS). Results Tumor depth was significantly associated with FD, kurtosis, entropy, GLCM-correlation, GLCM-autocorrelation, GLCM-entropy, and GLCM-homogeneity (p = 0.001, 0.001, 0.001, 0.001, 0.018, 0.008, and 0.001, respectively); lymph node metastasis was associated with GLCM-homogeneity (p = 0.004); lymphatic invasion was associated with GLCM-correlation and GLCM-homogeneity (p = 0.001 and 0.012, respectively); and venous invasion was associated with FD, entropy, GLCM-correlation, GLCM-autocorrelation, and GLCM-entropy of the tumor (p = 0.001, 0.033, 0.021, 0.046, respectively). In the Kaplan-Meier analysis, patients with high GLCM-correlation tumors or high GLCM-homogeneity tumors showed a significantly worse OS than others (p = 0.001 and 0.04, respectively). Multivariate analyses showed that the GLCM correlation was an independent prognostic factor for the OS (p = 0.021). Conclusion CE-CTC-derived texture parameters may be clinically useful biomarkers for managing CRC patients.
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Affiliation(s)
- Hisashi Mamiya
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
| | - Toru Tochigi
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
| | - Koichi Hayano
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
| | - Gaku Ohira
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
| | - Shunsuke Imanishi
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
| | - Tetsuro Maruyama
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
| | - Yoshihiro Kurata
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
| | - Yumiko Takahashi
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
| | - Atsushi Hirata
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
| | - Hisahiro Matsubara
- Department of Frontier SurgeryChiba University Graduate School of MedicineChibaJapan
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Guan X, Li M, Pang Y, He Y, Wang J, Xu X, Cheng K, Li Z, Liu L. Recent advances in algorithms predicting hemodynamic instability undergoing surgery for phaeochromocytoma and paraganglioma. Best Pract Res Clin Endocrinol Metab 2024; 38:101956. [PMID: 39477697 DOI: 10.1016/j.beem.2024.101956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/08/2024]
Abstract
Abdominal pheochromocytomas and paragangliomas (PPGLs) are characterized by the overproduction of catecholamines, which are associated with hemodynamic instability (HDI) during surgery. Therefore, perioperative management to prevent intraoperative HDI is imperative for the surgical treatment of PPGLs. Owing to the rarity and heterogeneous nature of these tumors, pre-surgical prediction of HDI is a clinical dilemma. The reported risk factors for HDI include perioperative preparation, genetic background, tumor conditions, body composition, catecholamine levels, and surgical approach. Additionally, several personalized algorithms or models including these factors have been developed. The first part of this review outlines the prediction models that include clinical features such as tumor size and location, body mass index (BMI), blood glucose level, catecholamine levels, and preoperative management with α-adrenoceptor blockade and crystal/colloid fluid. We then summarize recently reported models that consider additional factors such as genetic background, radiomics, robotic-assisted surgical approach, three-dimensional visualization, and machine-learning models. These findings suggest that a comprehensive model including risk factors is the most likely approach for achieving effective perioperative management.
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Affiliation(s)
- Xiao Guan
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Minghao Li
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yingxian Pang
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yao He
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jing Wang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaowen Xu
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Kai Cheng
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhi Li
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Longfei Liu
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
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Wang Y, Zhang X, Wang S, Shi H, Zhao X, Chen Y. Predicting first-line VEGFR-TKI resistance and survival in metastatic clear cell renal cell carcinoma using a clinical-radiomic nomogram. Cancer Imaging 2024; 24:151. [PMID: 39529158 PMCID: PMC11552170 DOI: 10.1186/s40644-024-00792-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 10/17/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND This study aims to construct predicting models using radiomic and clinical features in predicting first-line vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI) early resistance in metastatic clear cell renal cell carcinoma (mccRCC) patients. We also aim to explore the correlation of predicting models with short and long-term survival of mccRCC patients. MATERIALS AND METHODS In this retrospective study, 110 mccRCC patients from 2009 to 2019 were included and assigned into training and test sets. Radiomic features were extracted from tumor 3D-ROI of baseline enhanced CT images. Radiomic features were selected by Lasso method to construct a radiomic score. A combined nomogram was established using the combination of radiomic score and clinical factors. The discriminative abilities of the radiomic, clinical and combined nomogram were quantified using ROC curve. Cox regression analysis was used to test the correlation of nomogram score with progression-free survival (PFS) and overall survival (OS). PFS and OS were compared between different risk groups by log-rank test. RESULTS The radiomic, clinical and combined nomogram demonstrated AUCs of 0.81, 0.75, and 0.83 in training set; 0.79, 0.77, and 0.88 in test set. Nomogram score ≥ 1.18 was an independent prognostic factor of PFS (HR 0.22 (0.10, 0.47), p < 0.001) and OS (HR 0.38 (0.20, 0.71), p = 0.002), in training set. PFS in low-risk group were significantly longer than high-risk group in training (p < 0.001) and test (p < 0.001) set, respectively. OS in low-risk group were significantly longer than high-risk group in training (p = 0.003) and test (p = 0.009) set, respectively. CONCLUSION A nomogram combining baseline radiomic signature and clinical factors helped detecting first-line VEGFR-TKI early resistance and predicting short and long-term prognosis in mccRCC patients.
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Affiliation(s)
- Yichen Wang
- 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, Panjianyuannanli No.17, Chaoyang District, Beijing, 100021, China
| | - Xinxin Zhang
- 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, Panjianyuannanli No.17, Chaoyang District, Beijing, 100021, China
| | | | - Hongzhe Shi
- Department of Urology, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xinming Zhao
- 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, Panjianyuannanli No.17, Chaoyang District, Beijing, 100021, China
| | - Yan Chen
- 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, Panjianyuannanli No.17, Chaoyang District, Beijing, 100021, China.
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Durant AM, Medero RC, Briggs LG, Choudry MM, Nguyen M, Channar A, Ghaffar U, Banerjee I, Bin Riaz I, Abdul-Muhsin H. The Current Application and Future Potential of Artificial Intelligence in Renal Cancer. Urology 2024; 193:157-163. [PMID: 39029807 DOI: 10.1016/j.urology.2024.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/12/2024] [Accepted: 07/06/2024] [Indexed: 07/21/2024]
Abstract
Artificial intelligence (AI) is the integration of human tasks into machine processes. The role of AI in kidney cancer evaluation, management, and outcome predictions are constantly evolving. We performed a narrative review utilizing PubMed electronic database to query AI as a method of analysis in kidney cancer research. Key search-words included: Artificial Intelligence, Supervised/Unsupervised Machine Learning, Deep Learning, Natural Language Processing, Neural Networks, radiomics, pathomics, and kidney or renal neoplasms or cancer. 72 clinically relevant and impactful studies related to imaging, histopathology, and outcomes were recognized. We anticipate the incorporation of AI tools into future clinical decision-making for kidney cancer.
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Affiliation(s)
- Adri M Durant
- Department of Urology, Mayo Clinic Arizona, Phoenix, AZ.
| | - Ramon Correa Medero
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ
| | | | | | - Mimi Nguyen
- Department of Urology, Mayo Clinic Arizona, Phoenix, AZ
| | - Aneeta Channar
- Division of Hematology and Oncology, Department of Internal Medicine, Mayo Clinic Arizona, Phoenix, AZ
| | - Umar Ghaffar
- Department of Urology, Mayo Clinic Rochester, Rochester, MN
| | - Imon Banerjee
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ; Department of Radiology, Mayo Clinic Arizona, Scottsdale, AZ
| | - Irbaz Bin Riaz
- Division of Hematology and Oncology, Department of Internal Medicine, Mayo Clinic Arizona, Phoenix, AZ
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Li H, Zhuang Y, Yuan W, Gu Y, Dai X, Li M, Chen H, Zhou H. Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013-2023). Front Oncol 2024; 14:1464104. [PMID: 39558950 PMCID: PMC11571149 DOI: 10.3389/fonc.2024.1464104] [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: 07/28/2024] [Accepted: 10/04/2024] [Indexed: 11/20/2024] Open
Abstract
Background The incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis and precise treatment are essential for improving patient survival outcomes. Over the past decade, the integration of artificial intelligence (AI) and medical imaging technologies has positioned radiomics as a critical area of research in the diagnosis, treatment, and prognosis of CRC. Methods We conducted a comprehensive review of CRC-related radiomics literature published between 1 January 2013 and 31 December 2023 using the Web of Science Core Collection database. Bibliometric tools such as Bibliometrix, VOSviewer, and CiteSpace were employed to perform an in-depth bibliometric analysis. Results Our search yielded 1,226 publications, revealing a consistent annual growth in CRC radiomics research, with a significant rise after 2019. China led in publication volume (406 papers), followed by the United States (263 papers), whereas the United States dominated in citation numbers. Notable institutions included General Electric, Harvard University, University of London, Maastricht University, and the Chinese Academy of Sciences. Prominent researchers in this field are Tian J from the Chinese Academy of Sciences, with the highest publication count, and Ganeshan B from the University of London, with the most citations. Journals leading in publication and citation counts are Frontiers in Oncology and Radiology. Keyword and citation analysis identified deep learning, texture analysis, rectal cancer, image analysis, and management as prevailing research themes. Additionally, recent trends indicate the growing importance of AI and multi-omics integration, with a focus on improving precision medicine applications in CRC. Emerging keywords such as deep learning and AI have shown rapid growth in citation bursts over the past 3 years, reflecting a shift toward more advanced technological applications. Conclusions Radiomics plays a crucial role in the clinical management of CRC, providing valuable insights for precision medicine. It significantly contributes to predicting molecular biomarkers, assessing tumor aggressiveness, and monitoring treatment efficacy. Future research should prioritize advancing AI algorithms, enhancing multi-omics data integration, and further expanding radiomics applications in CRC precision medicine.
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Affiliation(s)
- Hao Li
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yupei Zhuang
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Weichen Yuan
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yutian Gu
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xinyan Dai
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Muhan Li
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Haibin Chen
- Science and Technology Department, Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hongguang Zhou
- Department of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, The First Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
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Khene ZE, Tachibana I, Bertail T, Fleury R, Bhanvadia R, Kapur P, Rajaram S, Guo J, Christie A, Pedrosa I, Lotan Y, Margulis V. Clinical application of radiomics for the prediction of treatment outcome and survival in patients with renal cell carcinoma: a systematic review. World J Urol 2024; 42:541. [PMID: 39325194 DOI: 10.1007/s00345-024-05247-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: 05/17/2024] [Accepted: 08/27/2024] [Indexed: 09/27/2024] Open
Abstract
PURPOSE The management of renal cell carcinoma (RCC) relies on clinical and histopathological features for treatment decisions. Recently, radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving RCC management. This review evaluates the current application and limitations of radiomics for predicting treatment and oncological outcomes in RCC. METHODS A systematic search was conducted in Medline, EMBASE, and Web of Science databases or studies that used radiomics to predict response to treatment and survival outcomes in patients with RCC. The study quality was assessed using the Radiomics Quality Score (RQS) tools. RESULTS The systematic review identified a total of 27 studies, examining 6,119 patients. The most used imaging modality was contrast-enhanced abdominal CT. The reviewed studies extracted between 19 and 3376 radiomics features, including Histogram, Texture, Filter, or transformation method. Radiomics-based risk stratification models provided valuable insights into treatment response and oncological outcomes. All developed signatures demonstrated at least modest accuracy (AUC range: 0.55-0.99). The studies included in this analysis reported heterogeneous results regarding radiomics methods. The range of Radiomics Quality Score (RQS) was from - 5 to 20, with a mean RQS total of 9.15 ± 7.95. CONCLUSION Radiomics has emerged as a promising tool in the management of RCC. It offers the potential for improved risk stratification and response assessment. However, future trials must demonstrate the generalizability of findings to prospective cohorts before progressing towards clinical translation.
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Affiliation(s)
- Zine-Eddine Khene
- Department of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA
- Department of Urology, University of Rennes, Rennes, France
- Image and Signal Processing Laboratory, Inserm U1099, University of Rennes, Rennes, France
| | - Isamu Tachibana
- Department of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA
| | - Theophile Bertail
- Department of Urology, University of Rennes, Rennes, France
- Radiation Oncology Department, CLCC Eugene Marquis, Rennes, France
| | - Raphael Fleury
- Department of Urology, University of Rennes, Rennes, France
| | - Raj Bhanvadia
- Department of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA
| | - Payal Kapur
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Satwik Rajaram
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Junyu Guo
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Alana Christie
- Simmons Comprehensive Cancer Center Biostatistics, University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Ivan Pedrosa
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Yair Lotan
- Department of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA
| | - Vitaly Margulis
- Department of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA.
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9
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Collinson F, Royle KL, Swain J, Ralph C, Maraveyas A, Eisen T, Nathan P, Jones R, Meads D, Min Wah T, Martin A, Bestall J, Kelly-Morland C, Linsley C, Oughton J, Chan K, Theodoulou E, Arias-Pinilla G, Kwan A, Daverede L, Handforth C, Trainor S, Salawu A, McCabe C, Goh V, Buckley D, Hewison J, Gregory W, Selby P, Brown J, Brown J. Temporary treatment cessation compared with continuation of tyrosine kinase inhibitors for adults with renal cancer: the STAR non-inferiority RCT. Health Technol Assess 2024; 28:1-171. [PMID: 39250424 PMCID: PMC11403377 DOI: 10.3310/jwtr4127] [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] [Indexed: 09/11/2024] Open
Abstract
Background There is interest in using treatment breaks in oncology, to reduce toxicity without compromising efficacy. Trial design A Phase II/III multicentre, open-label, parallel-group, randomised controlled non-inferiority trial assessing treatment breaks in patients with renal cell carcinoma. Methods Patients with locally advanced or metastatic renal cell carcinoma, starting tyrosine kinase inhibitor as first-line treatment at United Kingdom National Health Service hospitals. Interventions At trial entry, patients were randomised (1 : 1) to a drug-free interval strategy or a conventional continuation strategy. After 24 weeks of treatment with sunitinib/pazopanib, drug-free interval strategy patients took up a treatment break until disease progression with additional breaks dependent on disease response and patient choice. Conventional continuation strategy patients continued on treatment. Both trial strategies continued until treatment intolerance, disease progression on treatment, withdrawal or death. Objective To determine if a drug-free interval strategy is non-inferior to a conventional continuation strategy in terms of the co-primary outcomes of overall survival and quality-adjusted life-years. Co-primary outcomes For non-inferiority to be concluded, a margin of ≤ 7.5% in overall survival and ≤ 10% in quality-adjusted life-years was required in both intention-to-treat and per-protocol analyses. This equated to the 95% confidence interval of the estimates being above 0.812 and -0.156, respectively. Quality-adjusted life-years were calculated using the utility index of the EuroQol-5 Dimensions questionnaire. Results Nine hundred and twenty patients were randomised (461 conventional continuation strategy vs. 459 drug-free interval strategy) from 13 January 2012 to 12 September 2017. Trial treatment and follow-up stopped on 31 December 2020. Four hundred and eighty-eight (53.0%) patients [240 (52.1%) vs. 248 (54.0%)] continued on trial post week 24. The median treatment-break length was 87 days. Nine hundred and nineteen patients were included in the intention-to-treat analysis (461 vs. 458) and 871 patients in the per-protocol analysis (453 vs. 418). For overall survival, non-inferiority was concluded in the intention-to-treat analysis but not in the per-protocol analysis [hazard ratio (95% confidence interval) intention to treat 0.97 (0.83 to 1.12); per-protocol 0.94 (0.80 to 1.09) non-inferiority margin: 95% confidence interval ≥ 0.812, intention to treat: 0.83 > 0.812 non-inferior, per-protocol: 0.80 < 0.812 not non-inferior]. Therefore, a drug-free interval strategy was not concluded to be non-inferior to a conventional continuation strategy in terms of overall survival. For quality-adjusted life-years, non-inferiority was concluded in both the intention-to-treat and per-protocol analyses [marginal effect (95% confidence interval) intention to treat -0.05 (-0.15 to 0.05); per-protocol 0.04 (-0.14 to 0.21) non-inferiority margin: 95% confidence interval ≥ -0.156]. Therefore, a drug-free interval strategy was concluded to be non-inferior to a conventional continuation strategy in terms of quality-adjusted life-years. Limitations The main limitation of the study is the fewer than expected overall survival events, resulting in lower power for the non-inferiority comparison. Future work Future studies should investigate treatment breaks with more contemporary treatments for renal cell carcinoma. Conclusions Non-inferiority was shown for the quality-adjusted life-year end point but not for overall survival as pre-defined. Nevertheless, despite not meeting the primary end point of non-inferiority as per protocol, the study suggested that a treatment-break strategy may not meaningfully reduce life expectancy, does not reduce quality of life and has economic benefits. Although the treating clinicians' perspectives were not formally collected, the fact that clinicians recruited a large number of patients over a long period suggests support for the study and provides clear evidence that a treatment-break strategy for patients with renal cell carcinoma receiving tyrosine kinase inhibitor therapy is feasible. Trial registration This trial is registered as ISRCTN06473203. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment Programme (NIHR award ref: 09/91/21) and is published in full in Health Technology Assessment; Vol. 28, No. 45. See the NIHR Funding and Awards website for further award information.
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Affiliation(s)
- Fiona Collinson
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Kara-Louise Royle
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Jayne Swain
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Christy Ralph
- Leeds Institute of Medical Research, St James's University Hospital, University of Leeds, Leeds, UK
| | - Anthony Maraveyas
- Academic Oncology, Faculty of Health Sciences, Hull York Medical School, Queens Centre Oncology and Haematology, Hull, UK
| | - Tim Eisen
- Department of Oncology, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK
| | - Paul Nathan
- Department of Oncology, Mount Vernon Cancer Centre, East and North Hertfordshire NHS Trust, Hertfordshire, UK
| | - Robert Jones
- School of Cancer Sciences, University of Glasgow, Beatson West of Scotland Cancer Centre, Glasgow, UK
| | - David Meads
- Academic Unit of Health Economics, University of Leeds, Leeds, UK
| | - Tze Min Wah
- Department of Diagnostic and Interventional Radiology, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Adam Martin
- Academic Unit of Health Economics, University of Leeds, Leeds, UK
| | - Janine Bestall
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | | | | | - Jamie Oughton
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Kevin Chan
- Medical Oncology, Weston Park Cancer Hospital, Sheffield, UK
| | - Elisavet Theodoulou
- Division of Clinical Medicine, University of Sheffield, Weston Park Hospital, Sheffield, UK
| | - Gustavo Arias-Pinilla
- Division of Clinical Medicine, University of Sheffield, Weston Park Hospital, Sheffield, UK
| | - Amy Kwan
- Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, UK
| | - Luis Daverede
- Department of Clinical Oncology, Austral University Hospital, Buenos Aires, Argentina
| | - Catherine Handforth
- Division of Clinical Medicine, University of Sheffield, Weston Park Hospital, Sheffield, UK
| | - Sebastian Trainor
- St James's Institute of Oncology, St James's University Hospital, Leeds, UK
| | - Abdulazeez Salawu
- Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, UK
| | | | - Vicky Goh
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - David Buckley
- Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, UK
| | - Jenny Hewison
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Walter Gregory
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Peter Selby
- Leeds Institute of Medical Research, St James's University Hospital, University of Leeds, Leeds, UK
| | - Julia Brown
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Janet Brown
- Division of Clinical Medicine, University of Sheffield, Weston Park Hospital, Sheffield, UK
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10
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Shokeir FA, Elmokadem AH, Soliman N, Khater A, Bayoumi D. Texture analysis using non-contrast MRI to predict estrogen receptor status in breast cancer lesions: Is it feasible? Clin Radiol 2024; 79:e892-e899. [PMID: 38719689 DOI: 10.1016/j.crad.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 06/02/2024]
Abstract
PURPOSE We aimed to evaluate the feasibility of non-contrast-enhanced T1 sequence in texture analysis of breast cancer lesions to predict their estrogen receptor status. METHODS The study included 85 pathologically proven breast cancer lesions in 53 patients. Immunohistochemical studies were performed to determine the estrogen receptor status (ER). Lesions were divided into two groups: ER + ve status and ER-ve status. Texture analysis using the second-order analysis features [The Co-occurrence matrix (11 features)] was applied on both T1 and dynamic contrast-enhanced (DCE) MRI images for each lesion. Texture features gained from both T1 and DCE images were analyzed to obtain cut-off values using ROC curves to sort lesions according to their estrogen receptor status. RESULTS Angular second momentum and some of the entropy-based features showed statistically significant cut-off values in differentiation between the two groups [P-values for pre- and post-contrast images for AngSecMom (0.001, 0.008), sum entropy (0.003,0.005), and entropy (0.033,0.019), respectively]. On comparing the AUCs between pre- and post-contrast images, we found that differences were statistically insignificant. Sum of squares, sum variance, and sum average showed statistically significant cut-off points only on pre-contrast images [P-values for sum of squares (0.018), sum variance (0.024), and sum average (0.039)]. CONCLUSIONS Texture analysis features showed promising results in predicting estrogen receptor status of breast cancer lesions on non-contrast T1 images.
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Affiliation(s)
- F A Shokeir
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Egypt.
| | - A H Elmokadem
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Egypt.
| | - N Soliman
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Egypt.
| | - A Khater
- Mansoura University Oncology Center, Elgomhoria St. 35516, Egypt.
| | - D Bayoumi
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Egypt.
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11
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Awais M, Khan N, Khan AK, Rehman A. CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study. Abdom Radiol (NY) 2024; 49:857-867. [PMID: 37996544 DOI: 10.1007/s00261-023-04103-9] [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: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/25/2023]
Abstract
PURPOSE Peritoneal carcinomatosis (PC) and peritoneal tuberculosis (PTB) have similar clinical and radiologic imaging features, which make it very difficult to differentiate between the two entities clinically. Our aim was to determine if the CT textural parameters of omental lesions among patients with PC were different from those with PTB. METHODS All patients who had undergone omental biopsy at our institution from January 2010 to December 2018 and had a tissue diagnosis of PC or PTB were eligible for inclusion. Patients who did not have a contrast-enhanced CT abdomen within one month of the omental biopsy were excluded. A region of interest (ROI) was manually drawn over omental lesions and radiomic features were extracted using open-source LIFEx software. Statistical analysis was performed to compare mean differences in CT texture parameters between the PC and PTB groups. RESULTS A total of 66 patients were included in the study of which 38 and 28 had PC and PTB, respectively. Omental lesions in patients with PC had higher mean radiodensity (mean difference: +32.4; p = 0.001), higher mean entropy (mean difference: +0.11; p < 0.001), and lower mean energy (mean difference: -0.024; p = 0.001) compared to those in PTB. Additionally, omental lesions in the PC group had lower gray-level co-occurrence matrix (GLCM) homogeneity (mean difference: -0.073; p < 0.001) and higher GLCM dissimilarity (mean difference: +0.480; p < 0.001) as compared to the PTB group. CONCLUSION CT texture parameters of omental lesions differed significantly between patients with PTB and those with PC, which may help clinicians in differentiating between the two entities.
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Affiliation(s)
- Muhammad Awais
- Department of Radiology, Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Sindh, Pakistan.
| | - Noman Khan
- Department of Radiology, Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Sindh, Pakistan
| | - Ayimen Khalid Khan
- Department of Radiology, Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Sindh, Pakistan
| | - Abdul Rehman
- Department of Medicine, Rutgers-New Jersey Medical School, Newark, NJ, 07103, USA
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12
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Cellina M, Irmici G, Pepa GD, Ce M, Chiarpenello V, Alì M, Papa S, Carrafiello G. Radiomics and Artificial Intelligence in Renal Lesion Assessment. Crit Rev Oncog 2024; 29:65-75. [PMID: 38505882 DOI: 10.1615/critrevoncog.2023051084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Ce
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Vittoria Chiarpenello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, 20147 Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
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13
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Wang TW, Chao HS, Chiu HY, Lin YH, Chen HC, Lu CF, Liao CY, Lee Y, Shiao TH, Chen YM, Huang JW, Wu YT. Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients. Cancers (Basel) 2023; 15:5125. [PMID: 37958300 PMCID: PMC10647242 DOI: 10.3390/cancers15215125] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.
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Affiliation(s)
- Ting-Wei Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Hwa-Yen Chiu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Yi-Hui Lin
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Hung-Chun Chen
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yen Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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De Kock I, Bos S, Delrue L, Van Welden S, Bunyard P, Hindryckx P, De Vos M, Villeirs G, Laukens D. MRI texture analysis of T2-weighted images is preferred over magnetization transfer imaging for readily longitudinal quantification of gut fibrosis. Eur Radiol 2023; 33:5943-5952. [PMID: 37071162 DOI: 10.1007/s00330-023-09624-x] [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: 12/12/2022] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 04/19/2023]
Abstract
OBJECTIVES To investigate the value of magnetization transfer (MT) MRI and texture analysis (TA) of T2-weighted MR images (T2WI) in the assessment of intestinal fibrosis in a mouse model. METHODS Chronic colitis was induced in mice by cyclic administration of dextran sodium sulphate (DSS) resulting in chronic inflammation and progressive bowel fibrosis. Mice underwent 7-T MR imaging at various time points. Bowel wall MT ratio (MTR) and textural features (skewness, kurtosis, entropy), extracted by a filtration histogram technique, were correlated with histopathology. Performance of both techniques were validated using antifibrotic therapy. Finally, a retrospective study was conducted in five patients with Crohn's disease (CD) who underwent bowel surgery. RESULTS MTR and texture entropy correlated with histopathological fibrosis (r = .85 and .81, respectively). Entropy was superior to MTR for monitoring bowel fibrosis in the presence of coexisting inflammation (linear regression R2 = .93 versus R2 = .01). Furthermore, texture entropy was able to assess antifibrotic therapy response (placebo mice versus treated mice at endpoint scan; Δmean = 0.128, p < .0001). An increase in entropy was indicative of fibrosis accumulation in human CD strictures (inflammation: 1.29; mixed strictures: 1.4 and 1.48; fibrosis: 1.73 and 1.9). CONCLUSION MT imaging and TA of T2WI can both noninvasively detect established intestinal fibrosis in a mouse model. However, TA is especially useful for the longitudinal quantification of fibrosis in mixed inflammatory-fibrotic tissue, as well as for antifibrotic treatment response evaluation. This accessible post-processing technique merits further validation as the benefits for clinical practice as well as antifibrotic trial design would be numerous. KEY POINTS • Magnetization transfer MRI and texture analysis of T2-weighted MR images can detect established bowel fibrosis in an animal model of gut fibrosis. • Texture entropy is able to identify and monitor bowel fibrosis progression in an inflammatory context and can assess the response to antifibrotic treatment. • A proof-of-concept study in five patients with Crohn's disease suggests that texture entropy can detect and grade fibrosis in human intestinal strictures.
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Affiliation(s)
- Isabelle De Kock
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Simon Bos
- Department of Internal Medicine and Pediatrics, Ghent University, Corneel Heymanslaan 10, 0MRB2, B-9000, Ghent, Belgium
- VIB Center for Inflammation Research, Ghent, Belgium
| | - Louke Delrue
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Sophie Van Welden
- Department of Internal Medicine and Pediatrics, Ghent University, Corneel Heymanslaan 10, 0MRB2, B-9000, Ghent, Belgium
- VIB Center for Inflammation Research, Ghent, Belgium
| | | | - Pieter Hindryckx
- Department of Gastroenterology, Ghent University Hospital, Ghent, Belgium
| | - Martine De Vos
- Department of Gastroenterology, Ghent University Hospital, Ghent, Belgium
| | - Geert Villeirs
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Debby Laukens
- Department of Internal Medicine and Pediatrics, Ghent University, Corneel Heymanslaan 10, 0MRB2, B-9000, Ghent, Belgium.
- VIB Center for Inflammation Research, Ghent, Belgium.
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Shehata M, Abouelkheir RT, Gayhart M, Van Bogaert E, Abou El-Ghar M, Dwyer AC, Ouseph R, Yousaf J, Ghazal M, Contractor S, El-Baz A. Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review. Cancers (Basel) 2023; 15:2835. [PMID: 37345172 PMCID: PMC10216706 DOI: 10.3390/cancers15102835] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.
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Affiliation(s)
- Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA;
| | - Rasha T. Abouelkheir
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt; (R.T.A.); (M.A.E.-G.)
| | | | - Eric Van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA; (E.V.B.); (S.C.)
| | - Mohamed Abou El-Ghar
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt; (R.T.A.); (M.A.E.-G.)
| | - Amy C. Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA; (A.C.D.); (R.O.)
| | - Rosemary Ouseph
- Kidney Disease Program, University of Louisville, Louisville, KY 40202, USA; (A.C.D.); (R.O.)
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (J.Y.); (M.G.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (J.Y.); (M.G.)
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA; (E.V.B.); (S.C.)
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA;
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16
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Fang Z, Pu H, Chen XL, Yuan Y, Zhang F, Li H. MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer. Abdom Radiol (NY) 2023; 48:2270-2283. [PMID: 37085730 DOI: 10.1007/s00261-023-03910-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/01/2023] [Accepted: 04/05/2023] [Indexed: 04/23/2023]
Abstract
PURPOSE To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Eighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan-Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up. RESULTS Clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838-0.953) and validation cohort (AUC = 0.900,95%CI:0.771-1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838-0.953) in the training and (AUC = 0.912,95%CI:0.771-1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649-1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864-1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030). CONCLUSION Clinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage.
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Affiliation(s)
- Zhu Fang
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Xiao-Li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, 55#Four Section of South Renmin Road, Wuhou District, Chengdu, 610000, China
| | - Yi Yuan
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Feng Zhang
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China.
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Ricardo ALF, da Silva GA, Ogawa CM, Nussi AD, De Rosa CS, Martins JS, de Castro Lopes SLP, Appenzeller S, Braz-Silva PH, Costa ALF. Magnetic resonance imaging texture analysis for quantitative evaluation of the mandibular condyle in juvenile idiopathic arthritis. Oral Radiol 2023; 39:329-340. [PMID: 35948783 DOI: 10.1007/s11282-022-00641-y] [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] [Received: 05/04/2022] [Accepted: 07/13/2022] [Indexed: 10/15/2022]
Abstract
OBJECTIVES Juvenile idiopathic arthritis (JIA) is a chronic inflammatory disease that affects the joints and other organs, including the development of the former in a growing child. This study aimed to evaluate the feasibility of texture analysis (TA) based on magnetic resonance imaging (MRI) to provide biomarkers that serve to identify patients likely to progress to temporomandibular joint damage by associating JIA with age, gender and disease onset age. METHODS The radiological database was retrospectively reviewed. A total of 45 patients were first divided into control group (23) and JIA group (22). TA was performed using grey-level co-occurrence matrix (GLCM) parameters, in which 11 textural parameters were calculated using MaZda software. These 11 parameters were ranked based on the p value obtained with ANOVA and then correlated with age, gender and disease onset age. RESULTS Significant differences in texture parameters of condyle were demonstrated between JIA group and control group (p < 0.05). There was a progressive loss of uniformity in the grayscale pixels of MRI with an increasing age in JIA group. CONCLUSIONS MRI TA of the condyle can make it possible to detect the alterations in bone marrow of patients with JIA and promising tool which may help the image analysis.
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Affiliation(s)
- Ana Lúcia Franco Ricardo
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil
| | - Gabriel Araújo da Silva
- Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), Campinas, Brazil
| | - Celso Massahiro Ogawa
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil
| | - Amanda D Nussi
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil
| | | | - Jaqueline Serra Martins
- Rheumatology Department, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Sérgio Lúcio Pereira de Castro Lopes
- Department of Diagnosis and Surgery, São José Dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos, SP, Brazil
| | - Simone Appenzeller
- Rheumatology Department, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | | | - Andre Luiz Ferreira Costa
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil.
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18
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Hou D, Zheng X, Song W, Liu X, Wang S, Zhou L, Tao X, Lv L, Sun Q, Jin Y, Zhang Z, Ding L, Wu N, Zhao S. Radiomic-signature changes after early treatment improve the prediction of progression-free survival in patients with advanced anaplastic lymphoma kinase-positive non-small cell lung cancer. Acta Radiol 2023; 64:1194-1204. [PMID: 35971221 DOI: 10.1177/02841851221119621] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND The prognosis of lung cancer varies widely, even in cases wherein the tumor stage, genetic mutation, and treatment regimens are the same. Thus, an effective means for risk stratification of patients with lung cancer is needed. PURPOSE To develop and validate a combined model for predicting progression-free survival and risk stratification in patients with advanced anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) treated with ensartinib. MATERIAL AND METHODS We analyzed 203 tumor lesions in 114 patients and evaluated average radiomic feature measures from all lesions at baseline and changes in these features after early treatment (Δradiomic features). Combined models were developed by integrating clinical with radiomic features. The prediction performance and clinical value of the proposed models were evaluated using receiver operating characteristic analysis, calibration curve, decision curve analysis (DCA), and Kaplan-Meier survival analysis. RESULTS Both the baseline and delta combined models achieved predictive efficacy with a high area under the curve. The calibration curve and DCA indicated the high accuracy and clinical usefulness of the combined models for tumor progression prediction. In the Kaplan-Meier analysis, the delta and baseline combined models, Δradiomic signature, and two selected clinical features could distinguish patients with a higher progression risk within 42 weeks. The delta combined model had the best performance. CONCLUSION The combination of clinical and radiomic features provided a prognostic value for survival and progression in patients with NSCLC receiving ensartinib. Radiomic-signature changes after early treatment could be more valuable than those at baseline alone.
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Affiliation(s)
- Donghui Hou
- 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, PR China
| | - Xiaomin Zheng
- Department of Endocrinology, Chui Yang Liu Hospital affiliated to Tsinghua University, Beijing, PR China
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, PR China
| | - Xiaoqing Liu
- Department of Pulmonary Oncology, the Fifth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Sicong Wang
- Life Sciences, GE Healthcare, Beijing, PR China
| | - Lina Zhou
- 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, PR China
| | - Xiuli Tao
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Lv Lv
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Qi Sun
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, PR China
| | - Yujing Jin
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Zewei Zhang
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Lieming Ding
- 576287Betta Pharmaceuticals Co., Ltd, Hangzhou, PR China
| | - Ning Wu
- 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, PR China
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Shijun Zhao
- 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, PR China
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19
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Lanza C, Carriero S, Biondetti P, Angileri SA, Carrafiello G, Ierardi AM. Advances in imaging guidance during percutaneous ablation of renal tumors. Semin Ultrasound CT MR 2023; 44:162-169. [DOI: 10.1053/j.sult.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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20
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Atkins MB, Ascierto PA, Feltquate D, Gulley JL, Johnson DB, Khushalani NI, Sosman J, Yap TA, Kluger H, Sullivan RJ, Tawbi H. Society for Immunotherapy of Cancer (SITC) consensus definitions for resistance to combinations of immune checkpoint inhibitors with targeted therapies. J Immunother Cancer 2023; 11:e005923. [PMID: 36918225 PMCID: PMC10016252 DOI: 10.1136/jitc-2022-005923] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/31/2022] [Indexed: 03/15/2023] Open
Abstract
Immunotherapy offers deep and durable disease control to some patients, but many tumors do not respond to treatment with single-agent immune checkpoint inhibitors (ICIs). One strategy to enhance responses to immunotherapy is via combinations with signal transduction inhibitors, such as antiangiogenic therapies, which not only directly target cancer cells but also could potentially favorably modulate the tumor immune microenvironment. Combination strategies with ICIs have demonstrated enhanced antitumor activity compared with tumor-targeted or antiangiogenic therapy alone in randomized trials in a variety of solid tumor settings, leading to regulatory approval from the US Food and Drug Administration and agencies in other countries for the treatment of endometrial cancer, kidney cancer, melanoma, and hepatocellular carcinoma. Despite improved survival and response rates for some patients when antiangiogenic or targeted therapies are administered with ICIs, many patients continue to progress after combination treatment and urgently need new strategies to address this manifestation of resistance to immunotherapy. Previously, the Society for Immunotherapy of Cancer (SITC) published consensus definitions for resistance to single-agent anti-PD-(L)1. To provide guidance for clinical trial design and to support analyses of emerging molecular and immune profiling data surrounding mechanisms of resistance to ICI-based combinations, SITC convened a follow-up workshop in 2021 to develop consensus definitions for resistance to multiagent ICI combinations. This manuscript reports the consensus clinical definitions for combinations of anti-PD-(L)1 ICIs and targeted therapies. Definitions for resistance to ICIs in combination with chemotherapy and with other ICIs will be published in companion volumes to this paper.
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Affiliation(s)
| | - Paolo A Ascierto
- Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy
| | | | | | | | | | | | - Timonthy A Yap
- University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | | | - Hussein Tawbi
- University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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21
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Ferro M, Musi G, Marchioni M, Maggi M, Veccia A, Del Giudice F, Barone B, Crocetto F, Lasorsa F, Antonelli A, Schips L, Autorino R, Busetto GM, Terracciano D, Lucarelli G, Tataru OS. Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects. Int J Mol Sci 2023; 24:4615. [PMID: 36902045 PMCID: PMC10003020 DOI: 10.3390/ijms24054615] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology (IEO) IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology (IEO) IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, 66100 Chieti, Italy
- Urology Unit, SS. Annunziata Hospital, 66100 Chieti, Italy
- Department of Urology, ASL Abruzzo 2, 66100 Chieti, Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, University of Rome, 00161 Rome, Italy
| | - Alessandro Veccia
- Department of Urology, Azienda Ospedaliera Universitaria Integrata of Verona, University of Verona, 37126 Verona, Italy
| | - Francesco Del Giudice
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, University of Rome, 00161 Rome, Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Felice Crocetto
- Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Francesco Lasorsa
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari Aldo Moro, 70124 Bari, Italy
| | - Alessandro Antonelli
- Department of Urology, Azienda Ospedaliera Universitaria Integrata of Verona, University of Verona, 37126 Verona, Italy
| | - Luigi Schips
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, 66100 Chieti, Italy
- Urology Unit, SS. Annunziata Hospital, 66100 Chieti, Italy
- Department of Urology, ASL Abruzzo 2, 66100 Chieti, Italy
| | | | - Gian Maria Busetto
- Department of Urology and Renal Transplantation, University of Foggia, 71122 Foggia, Italy
| | - Daniela Terracciano
- Department of Translational Medical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari Aldo Moro, 70124 Bari, Italy
| | - Octavian Sabin Tataru
- Department of Simulation Applied in Medicine, The Institution Organizing University Doctoral Studies (I.O.S.U.D.), George Emil Palade University of Medicine, Pharmacy, Sciences, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania
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Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy. Cancers (Basel) 2023; 15:cancers15041105. [PMID: 36831445 PMCID: PMC9954441 DOI: 10.3390/cancers15041105] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/27/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.
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23
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Chen Y, Yuan E, Sun G, Song B, Yao J. Delta Radiomics Model Predicts Lesion-Level Responses to Tyrosine Kinase Inhibitors in Patients with Advanced Renal Cell Carcinoma: A Preliminary Result. J Clin Med 2023; 12:1301. [PMID: 36835837 PMCID: PMC9966873 DOI: 10.3390/jcm12041301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND This study aimed to develop and internally validate computed tomography (CT)-based radiomic models to predict the lesion-level short-term response to tyrosine kinase inhibitors (TKIs) in patients with advanced renal cell carcinoma (RCC). METHODS This retrospective study included consecutive patients with RCC that were treated using TKIs as the first-line treatment. Radiomic features were extracted from noncontrast (NC) and arterial-phase (AP) CT images. The model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS A total of 36 patients with 131 measurable lesions were enrolled (training: validation = 91: 40). The model with five delta features achieved the best discrimination capability with AUC values of 0.940 (95% CI, 0.890‒0.990) in the training cohort and 0.916 (95% CI, 0.828‒1.000) in the validation cohort. Only the delta model was well calibrated. The DCA showed that the net benefit of the delta model was greater than that of the other radiomic models, as well as that of the treat-all and treat-none criteria. CONCLUSIONS Models based on CT delta radiomic features may help predict the short-term response to TKIs in patients with advanced RCC and aid in lesion stratification for potential treatments.
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Affiliation(s)
- Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Enyu Yuan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Guangxi Sun
- Department of Urology, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610000, China
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610000, China
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Posada Calderon L, Eismann L, Reese SW, Reznik E, Hakimi AA. Advances in Imaging-Based Biomarkers in Renal Cell Carcinoma: A Critical Analysis of the Current Literature. Cancers (Basel) 2023; 15:cancers15020354. [PMID: 36672304 PMCID: PMC9856305 DOI: 10.3390/cancers15020354] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Cross-sectional imaging is the standard diagnostic tool to determine underlying biology in renal masses, which is crucial for subsequent treatment. Currently, standard CT imaging is limited in its ability to differentiate benign from malignant disease. Therefore, various modalities have been investigated to identify imaging-based parameters to improve the noninvasive diagnosis of renal masses and renal cell carcinoma (RCC) subtypes. MRI was reported to predict grading of RCC and to identify RCC subtypes, and has been shown in a small cohort to predict the response to targeted therapy. Dynamic imaging is promising for the staging and diagnosis of RCC. PET/CT radiotracers, such as 18F-fluorodeoxyglucose (FDG), 124I-cG250, radiolabeled prostate-specific membrane antigen (PSMA), and 11C-acetate, have been reported to improve the identification of histology, grading, detection of metastasis, and assessment of response to systemic therapy, and to predict oncological outcomes. Moreover, 99Tc-sestamibi and SPECT scans have shown promising results in distinguishing low-grade RCC from benign lesions. Radiomics has been used to further characterize renal masses based on semantic and textural analyses. In preliminary studies, integrated machine learning algorithms using radiomics proved to be more accurate in distinguishing benign from malignant renal masses compared to radiologists' interpretations. Radiomics and radiogenomics are used to complement risk classification models to predict oncological outcomes. Imaging-based biomarkers hold strong potential in RCC, but require standardization and external validation before integration into clinical routines.
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Affiliation(s)
- Lina Posada Calderon
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lennert Eismann
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Stephen W. Reese
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ed Reznik
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Abraham Ari Hakimi
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence:
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Zhang X, Lu B, Yang X, Lan D, Lin S, Zhou Z, Li K, Deng D, Peng P, Zeng Z, Long L. Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature. Eur Radiol 2023; 33:825-835. [PMID: 36166088 PMCID: PMC9889474 DOI: 10.1007/s00330-022-09123-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 08/05/2022] [Accepted: 08/19/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To evaluate the value of time-serial CT radiomics features in predicting progression-free survival (PFS) for lung adenocarcinoma (LUAD) patients after epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) therapy. MATERIALS AND METHODS LUAD patients treated with EGFR-TKIs were retrospectively included from three independent institutes and divided into training and validation cohorts. Intratumoral and peritumoral features were extracted from time-serial non-contrast chest CT (including pre-therapy and first follow-up images); moreover, the percentage variation per unit time (day) was introduced to adjust for the different follow-up periods of each patient. Test-retest was performed to exclude irreproducible features, while the Boruta algorithm was used to select critical radiomics features. Radiomics signatures were constructed with random forest survival models in the training cohort and compared against baseline clinical characteristics through Cox regression and nonparametric testing of concordance indices (C-indices). RESULTS The training cohort included 131 patients (74 women, 56.5%) from one institute and the validation cohort encompassed 41 patients (24 women, 58.5%) from two other institutes. The optimal signature contained 10 features and 7 were unit time feature variations. The comprehensive radiomics model outperformed the pre-therapy clinical characteristics in predicting PFS (training: 0.78, 95% CI: [0.72, 0.84] versus 0.55, 95% CI: [0.49, 0.62], p < 0.001; validation: 0.72, 95% CI: [0.60, 0.84] versus 0.54, 95% CI: [0.42, 0.66], p < 0.001). CONCLUSION Radiomics signature derived from time-serial CT images demonstrated optimal prognostic performance of disease progression. This dynamic imaging biomarker holds the promise of monitoring treatment response and achieving personalized management. KEY POINTS • The intrinsic tumor heterogeneity can be highly dynamic under the therapeutic effect of EGFR-TKI treatment, and the inevitable development of drug resistance may disrupt the duration of clinical benefit. Decision-making remained challenging in practice to detect the emergence of acquired resistance during the early response phase. • Time-serial CT-based radiomics signature integrating intra- and peritumoral features offered the potential to predict progression-free survival for LUAD patients treated with EGFR-TKIs. • The dynamic imaging signature allowed for prognostic risk stratification.
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Affiliation(s)
- Xiaobo Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Bingfeng Lu
- Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | - Xinguan Yang
- Department of Radiology, Guilin People’s Hospital, Guilin, Guangxi China
| | - Dong Lan
- Department of Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | | | - Zhipeng Zhou
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi China
| | - Kai Li
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Dong Deng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Peng Peng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Zisan Zeng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
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Ferro M, Crocetto F, Barone B, del Giudice F, Maggi M, Lucarelli G, Busetto GM, Autorino R, Marchioni M, Cantiello F, Crocerossa F, Luzzago S, Piccinelli M, Mistretta FA, Tozzi M, Schips L, Falagario UG, Veccia A, Vartolomei MD, Musi G, de Cobelli O, Montanari E, Tătaru OS. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review. Ther Adv Urol 2023; 15:17562872231164803. [PMID: 37113657 PMCID: PMC10126666 DOI: 10.1177/17562872231164803] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/04/2023] [Indexed: 04/29/2023] Open
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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Affiliation(s)
| | - Felice Crocetto
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Francesco del Giudice
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation
Unit, Department of Emergency and Organ Transplantation, University of Bari,
Bari, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | | | - Michele Marchioni
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti,
Italy
| | - Francesco Cantiello
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Fabio Crocerossa
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Stefano Luzzago
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Mattia Piccinelli
- Cancer Prognostics and Health Outcomes Unit,
Division of Urology, University of Montréal Health Center, Montréal, QC,
Canada
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Tozzi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Luigi Schips
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
| | | | - Alessandro Veccia
- Urology Unit, Azienda Ospedaliera
Universitaria Integrata Verona, University of Verona, Verona, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology,
George Emil Palade University of Medicine, Pharmacy, Science and Technology
of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of
Vienna, Vienna, Austria
| | - Gennaro Musi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’
Granda – Ospedale Maggiore Policlinico, Department of Clinical Sciences and
Community Health, University of Milan, Milan, Italy
| | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral
Studies (IOSUD), George Emil Palade University of Medicine, Pharmacy,
Science and Technology of Târgu Mures, Târgu Mures, Romania
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Mahmoudi T, Radmard AR, Salehnia A, Ahmadian A, Davarpanah AH, Kafieh R, Arabalibeik H. Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images. J Biomed Phys Eng 2022; 12:655-668. [PMID: 36569560 PMCID: PMC9759639 DOI: 10.31661/jbpe.v0i0.2102-1283] [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: 02/16/2021] [Accepted: 03/01/2021] [Indexed: 12/02/2022]
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications.
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Affiliation(s)
- Tahereh Mahmoudi
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- PhD, Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran Iran
| | - Aneseh Salehnia
- MD, Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran Iran
| | - Alireza Ahmadian
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- PhD, Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir H Davarpanah
- MD, Department of Radiology and Imaging Sciences, Emory University School of Medicine 1364 Clifton Rd NE Atlanta, Georgia 30322, USA
| | - Raheleh Kafieh
- PhD, Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Arabalibeik
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- PhD, Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
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Nishiyama A, Kawata N, Yokota H, Hayano K, Matsuoka S, Shigeta A, Sugiura T, Tanabe N, Ishida K, Tatsumi K, Suzuki T, Uno T. Heterogeneity of Lung Density in Patients With Chronic Thromboembolic Pulmonary Hypertension (CTEPH). Acad Radiol 2022; 29:e229-e239. [PMID: 35466051 DOI: 10.1016/j.acra.2022.03.002] [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: 11/05/2021] [Revised: 02/21/2022] [Accepted: 03/01/2022] [Indexed: 11/29/2022]
Abstract
RATIONALE AND OBJECTIVES Pulmonary endarterectomy (PEA) is one of the most effective treatments for chronic thromboembolic pulmonary hypertension (CTEPH). Right heart catheterization (RHC) is the gold standard assessment for pulmonary circulatory dynamics. However, computed tomography (CT) is less invasive than RHC and can elucidate some of the morphological changes caused by thromboembolism. We hypothesized that CT could facilitate the evaluation of heterogeneous pulmonary perfusion. This study investigated whether CT imaging features reflect the disease severity and changes in pulmonary circulatory dynamics in patients with CTEPH before and after PEA. MATERIALS AND METHODS This retrospective study included 58 patients with CTEPH who underwent PEA. Pre-PEA and post-PEA CT images were assessed for heterogeneity using CT texture analysis (CTTA). The CT parameters were compared with the results of the RHC and other clinical indices and analyzed with receiver operating characteristic curves analysis for patients with and without residual pulmonary hypertension (PH) (post-PEA mean pulmonary artery pressure ≥ 25 mmHg). RESULTS CT measurements reflecting heterogeneity were significantly correlated with mean pulmonary artery pressure. Kurtosis, skewness, and uniformity were significantly lower, and entropy was significantly higher in patients with residual PH than patients without residual PH. Area under the curve values of pre-PEA and post-PEA entropy between patients with and without residual PH were 0.71 (95% confidence interval 0.57-0.84) and 0.75 (0.63-0.88), respectively. CONCLUSION Heterogeneity of lung density might reflect pulmonary circulatory dynamics, and CTTA for heterogeneity could be a less invasive technique for evaluation of changes in pulmonary circulatory dynamics in patients with CTEPH undergoing PEA.
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Affiliation(s)
- Akira Nishiyama
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan.
| | - Naoko Kawata
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Hajime Yokota
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Koichi Hayano
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Shin Matsuoka
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Ayako Shigeta
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Toshihiko Sugiura
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Nobuhiko Tanabe
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Keiichi Ishida
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Koichiro Tatsumi
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Takuji Suzuki
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
| | - Takashi Uno
- Department of Radiology (A.N.), Chiba University Hospital, Chiba, Japan; Department of Respirology (N.K., A.S., T.S., K.T., T.S.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Diagnostic Radiology and Radiation Oncology (H.Y., T.U.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Frontier Surgery (K.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Radiology (S.M.), St. Marianna University School of Medicine, Kanagawa, Japan; Department of Respirology (N.T.), Chibaken Saiseikai Narashino Hospital, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Cardiovascular Surgery (K.I.), Eastern Chiba Medical Center, Togane, Japan
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Radiomic Analysis for Pretreatment Prediction of Recurrence Post-Radiotherapy in Cervical Squamous Cell Carcinoma Cancer. Diagnostics (Basel) 2022; 12:diagnostics12102346. [PMID: 36292034 PMCID: PMC9600567 DOI: 10.3390/diagnostics12102346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 11/30/2022] Open
Abstract
Background: The current study aims to predict the recurrence of cervical cancer patients treated with radiotherapy from radiomics features on pretreatment T1- and T2-weighted MR images. Methods: A total of 89 patients were split into model training (63 patients) and model testing (26 patients). The predictors of recurrence were selected using the least absolute shrinkage and selection operator (LASSO) regression. The machine learning used neural network classifiers. Results: Using LASSO analysis of radiomics, we found 25 features from the T1-weighted and 4 features from T2-weighted MR images, respectively. The accuracy was highest with the combination of T1- and T2-weighted MR images. The model performances with T1- or T2-weighted MR images were 86.4% or 89.4% accuracy, 74.9% or 38.1% sensitivity, 81.8% or 72.2% specificity, and 0.89 or 0.69 of the area under the curve (AUC). The model performance with the combination of T1- and T2-weighted MR images was 93.1% accuracy, 81.6% sensitivity, 88.7% specificity, and 0.94 of AUC. Conclusions: The radiomics analysis with T1- and T2-weighted MR images could highly predict the recurrence of cervix cancer after radiotherapy. The variation of the distribution and the difference in the pixel number at the peripheral and the center were important predictors.
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Azadikhah A, Varghese BA, Lei X, Martin-King C, Cen SY, Duddalwar VA. Radiomics quality score in renal masses: a systematic assessment on current literature. Br J Radiol 2022; 95:20211211. [PMID: 35671097 PMCID: PMC10996962 DOI: 10.1259/bjr.20211211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To perform a systematic assessment and analyze the quality of radiomics methodology in current literature in the evaluation of renal masses using the Radiomics Quality Score (RQS) approach. METHODS We systematically reviewed recent radiomics literature in renal masses published in PubMed, EMBASE, Elsevier, and Web of Science. Two reviewers blinded by each other's scores evaluated the quality of radiomics methodology in studies published from 2015 to August 2021 using the RQS approach. Owing to the diversity in the imaging modalities and radiomics applications, a meta-analysis could not be performed. RESULTS Based on our inclusion/exclusion criteria, a total of 87 published studies were included in our study. The highest RQS was noted in three categories: reporting of clinical utility, gold standard, and feature reduction. The average RQS of the two reviewers ranged from 5 ≤ RQS≤19, with the maximum attainable RQS being 36. Very few (7/87 i.e., 8%) studies received an average RQS that ranged from 17 < RQS≤19, which represents studies with the highest RQS in our study. Many (39/87 i.e., 45%) studies received an average RQS that ranged from 13 < RQS≤15. No significant interreviewer scoring differences were observed. CONCLUSIONS We report that the overall scientific quality and reporting of radiomics studies in renal masses is suboptimal, and subsequent studies should bolster current deficiencies to improve reporting of radiomics methodologies. ADVANCES IN KNOWLEDGE The RQS approach is a meaningful quantitative scoring system to assess radiomics methodology quality and supports a comprehensive evaluation of the radiomics approach before its incorporation into clinical practice.
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Affiliation(s)
- Afshin Azadikhah
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Bino Abel Varghese
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Xiaomeng Lei
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Chloe Martin-King
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Steven Yong Cen
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Vinay Anant Duddalwar
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
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31
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Mazaheri Y, Thakur SB, Bitencourt AGV, Lo Gullo R, Hötker AM, Bates DDB, Akin O. Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods. BJR Open 2022; 4:20210072. [PMID: 36105425 PMCID: PMC9459949 DOI: 10.1259/bjro.20210072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 06/01/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow.
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Affiliation(s)
| | | | | | - Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Andreas M. Hötker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - David D B Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
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Mirón Mombiela R, Arildskov AR, Bruun FJ, Hasselbalch LH, Holst KB, Rasmussen SH, Borrás C. What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies. Int J Mol Sci 2022; 23:6504. [PMID: 35742947 PMCID: PMC9224495 DOI: 10.3390/ijms23126504] [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] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.
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Affiliation(s)
- Rebeca Mirón Mombiela
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Anne Rix Arildskov
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Frederik Jager Bruun
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Lotte Harries Hasselbalch
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Kristine Bærentz Holst
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Sine Hvid Rasmussen
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Consuelo Borrás
- Freshage Research Group, Department of Physiology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBERFES, Institute of Health Research-INCLIVA, 46010 Valencia, Spain
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Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian J Urol 2022; 9:243-252. [PMID: 36035341 PMCID: PMC9399557 DOI: 10.1016/j.ajur.2022.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/07/2022] [Accepted: 05/07/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.
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Marr L, Haller B, Pyka T, Peeken JC, Jesinghaus M, Scheidhauer K, Friess H, Combs SE, Münch S. Predictive value of clinical and 18F-FDG-PET/CT derived imaging parameters in patients undergoing neoadjuvant chemoradiation for esophageal squamous cell carcinoma. Sci Rep 2022; 12:7148. [PMID: 35504955 PMCID: PMC9065158 DOI: 10.1038/s41598-022-11076-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/12/2022] [Indexed: 12/24/2022] Open
Abstract
Aim of this study was to validate the prognostic impact of clinical parameters and baseline 18F-FDG-PET/CT derived textural features to predict histopathologic response and survival in patients with esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiation (nCRT) and surgery. Between 2005 and 2014, 38 ESCC were treated with nCRT and surgery. For all patients, the 18F-FDG-PET-derived parameters metabolic tumor volume (MTV), SUVmax, contrast and busyness were calculated for the primary tumor using a SUV-threshold of 3. The parameter uniformity was calculated using contrast-enhanced computed tomography. Based on histopathological response to nCRT, patients were classified as good responders (< 10% residual tumor) (R) or non-responders (≥ 10% residual tumor) (NR). Regression analyses were used to analyse the association of clinical parameters and imaging parameters with treatment response and overall survival (OS). Good response to nCRT was seen in 27 patients (71.1%) and non-response was seen in 11 patients (28.9%). Grading was the only parameter predicting response to nCRT (Odds Ratio (OR) = 0.188, 95% CI: 0.040–0.883; p = 0.034). No association with histopathologic treatment response was seen for any of the evaluated imaging parameters including SUVmax, MTV, busyness, contrast and uniformity. Using multivariate Cox-regression analysis, the heterogeneity parameters busyness (Hazard Ratio (HR) = 1.424, 95% CI: 1.044–1.943; p = 0.026) and contrast (HR = 6.678, 95% CI: 1.969–22.643;p = 0.002) were independently associated with OS, while no independent association with OS was seen for SUVmax and MTV. In patients with ESCC undergoing nCRT and surgery, baseline 18F-FDG-PET/CT derived parameters could not predict histopathologic response to nCRT. However, the PET/CT derived features busyness and contrast were independently associated with OS and should be further investigated.
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Affiliation(s)
- Lisa Marr
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Bernhard Haller
- Institute of Medical Informatics, Statistics and Epidemiology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Thomas Pyka
- Departement of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,Department of Nuclear Medicine, Inselspital Bern, Freiburgstr. 18, 3010, Bern, Switzerland
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Ingolstädter Landstraße 1, 85764, Oberschleißheim, Germany
| | - Moritz Jesinghaus
- Institute of Pathology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Klemens Scheidhauer
- Departement of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Helmut Friess
- Department of Surgery, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Ingolstädter Landstraße 1, 85764, Oberschleißheim, Germany
| | - Stefan Münch
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany. .,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.
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Brunese L, Mercaldo F, Reginelli A, Santone A. A novel methodology for head and neck carcinoma treatment stage detection by means of model checking. Artif Intell Med 2022; 127:102263. [DOI: 10.1016/j.artmed.2022.102263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 02/18/2022] [Accepted: 02/23/2022] [Indexed: 11/02/2022]
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Yao W, Yang S, Ge Y, Fan W, Xiang L, Wan Y, Gu K, Zhao Y, Zha R, Bu J. Computed Tomography Radiomics-Based Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Front Med (Lausanne) 2022; 9:819670. [PMID: 35402463 PMCID: PMC8987588 DOI: 10.3389/fmed.2022.819670] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 02/18/2022] [Indexed: 12/12/2022] Open
Abstract
Background Due to the high recurrence rate in hepatocellular carcinoma (HCC) after resection, preoperative prognostic prediction of HCC is important for appropriate patient management. Exploring and developing preoperative diagnostic methods has great clinical value in treating patients with HCC. This study sought to develop and evaluate a novel combined clinical predictive model based on standard triphasic computed tomography (CT) to discriminate microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods The preoperative findings of 82 patients with HCC, including conventional clinical factors, CT imaging findings, and CT texture analysis (TA), were analyzed retrospectively. All included cases were divided into MVI-negative (n = 33; no MVI) and MVI-positive (n = 49; low or high risk of MVI) groups. TA parameters were extracted from non-enhanced, arterial, portal venous, and equilibrium phase images and subsequently calculated using the Artificial Intelligence Kit. After statistical analyses, a clinical model comprising conventional clinical and CT image risk factors, radiomics signature models, and a novel combined model (fused radiomic signature) was constructed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to assess the performance of the various models in discriminating MVI. Results We found that tumor diameter and pathological grade were effective clinical predictors in clinical model and 12 radiomics features were effective for MVI prediction of each CT phase. The AUCs of the clinical, plain, artery, venous, and delay models were 0.77 (95% CI: 0.67–0.88), 0.75 (95% CI: 0.64–0.87), 0.79 (95% CI: 0.69–0.89), 0.73 (95% CI: 0.61–0.85), and 0.80 (95% CI: 0.70–0.91), respectively. The novel combined model exhibited the best performance, with an AUC of 0.83 (95% CI: 0.74–0.93). Conclusions Models derived from triphasic CT can preoperatively predict MVI in patients with HCC. Of the models tested here, the novel combined model was most predictive and could become a useful tool to guide subsequent personalized treatment of HCC.
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Affiliation(s)
- Wenjun Yao
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shuo Yang
- Department of Radiology, Anhui Mental Health Center, Hefei, China
| | | | - Wenlong Fan
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Li Xiang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yang Wan
- Department of Hematological Lab, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Kangchen Gu
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yan Zhao
- School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Rujing Zha
- Department of Radiology, Division of Life Science and Medicine, The First Affiliated Hospital of USTC, School of Life Science, University of Science and Technology of China, Hefei, China
| | - Junjie Bu
- School of Biomedical Engineering, Anhui Medical University, Hefei, China.,The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
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Li LP, Leidner AS, Wilt E, Mikheev A, Rusinek H, Sprague SM, Kohn OF, Srivastava A, Prasad PV. Radiomics-Based Image Phenotyping of Kidney Apparent Diffusion Coefficient Maps: Preliminary Feasibility & Efficacy. J Clin Med 2022; 11:jcm11071972. [PMID: 35407587 PMCID: PMC8999417 DOI: 10.3390/jcm11071972] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 12/11/2022] Open
Abstract
Given the central role of interstitial fibrosis in disease progression in chronic kidney disease (CKD), a role for diffusion-weighted MRI has been pursued. We evaluated the feasibility and preliminary efficacy of using radiomic features to phenotype apparent diffusion coefficient (ADC) maps and hence to the clinical classification(s) of the participants. The study involved 40 individuals (10 healthy and 30 with CKD (eGFR < 60 mL/min/1.73 m2)). Machine learning methods, such as hierarchical clustering and logistic regression, were used. Clustering resulted in the identification of two clusters, one including all individuals with CKD (n = 17), while the second one included all the healthy volunteers (n = 10) and the remaining individuals with CKD (n = 13), resulting in 100% specificity. Logistic regression identified five radiomic features to classify participants as with CKD vs. healthy volunteers, with a sensitivity and specificity of 93% and 70%, respectively, and an AUC of 0.95. Similarly, four radiomic features were able to classify participants as rapid vs. non-rapid CKD progressors among the 30 individuals with CKD, with a sensitivity and specificity of 71% and 43%, respectively, and an AUC of 0.75. These promising preliminary data should support future studies with larger numbers of participants with varied disease severity and etiologies to improve performance.
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Affiliation(s)
- Lu-Ping Li
- Department of Radiology, North Shore University HealthSystem, Evanston, IL 60201, USA; (E.W.); (P.V.P.)
- Correspondence:
| | - Alexander S. Leidner
- Division of Nephrology and Hypertension, Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (A.S.L.); (A.S.)
| | - Emily Wilt
- Department of Radiology, North Shore University HealthSystem, Evanston, IL 60201, USA; (E.W.); (P.V.P.)
| | - Artem Mikheev
- Center for Biomedical Imaging, New York University Langone Health, New York, NY 10016, USA; (A.M.); (H.R.)
| | - Henry Rusinek
- Center for Biomedical Imaging, New York University Langone Health, New York, NY 10016, USA; (A.M.); (H.R.)
| | - Stuart M. Sprague
- Division of Nephrology, Department of Medicine, North Shore University HealthSystem, Evanston, IL 60201, USA;
| | - Orly F. Kohn
- Division of Nephrology, Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, IL 60637, USA;
| | - Anand Srivastava
- Division of Nephrology and Hypertension, Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (A.S.L.); (A.S.)
| | - Pottumarthi V. Prasad
- Department of Radiology, North Shore University HealthSystem, Evanston, IL 60201, USA; (E.W.); (P.V.P.)
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Ideguchi M, Nishizaki T, Ikeda N, Fujii N, Ohno M, Shimabukuro T, Kimura T, Ikeda E, Suga K. Investigation of histological heterogeneity based on the discrepancy between the hyperintense area on T2-weighted images and the accumulation area on 11C-methionine PET in minimally enhancing glioma. INTERDISCIPLINARY NEUROSURGERY 2022. [DOI: 10.1016/j.inat.2021.101364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Zhang H, Yin F, Chen M, Yang L, Qi A, Cui W, Yang S, Wen G. Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma. Front Oncol 2022; 11:742547. [PMID: 35155180 PMCID: PMC8830916 DOI: 10.3389/fonc.2021.742547] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/29/2021] [Indexed: 11/17/2022] Open
Abstract
Background Many patients experience recurrence of renal cell carcinoma (RCC) after radical and partial nephrectomy. Radiomics nomogram is a newly used noninvasive tool that could predict tumor phenotypes. Objective To investigate Radiomics Features (RFs) associated with progression-free survival (PFS) of RCC, assessing its incremental value over clinical factors, and to develop a visual nomogram in order to provide reference for individualized treatment. Methods The RFs and clinicopathological data of 175 patients (125 in the training set and 50 in the validation set) with clear cell RCC (ccRCC) were retrospectively analyzed. In the training set, RFs were extracted from multiphase enhanced CT tumor volume and selected using the stability LASSO feature selection algorithm. A radiomics nomogram final model was developed that incorporated the RFs weighted sum and selected clinical predictors based on the multivariate Cox proportional hazard regression. The performances of a clinical variables-only model, RFs-only model, and the final model were compared by receiver operator characteristic (ROC) analysis and DeLong test. Nomogram performance was determined and validated with respect to its discrimination, calibration, reclassification, and clinical usefulness. Results The radiomics nomogram included age, clinical stage, KPS score, and RFs weighted sum, which consisted of 6 selected RFs. The final model showed good discrimination, with a C-index of 0.836 and 0.706 in training and validation, and good calibration. In the training set, the C-index of the final model was significantly larger than the clinical-only model (DeLong test, p = 0.008). From the clinical variables-only model to the final model, the reclassification of net reclassification improvement was 18.03%, and the integrated discrimination improvement was 19.08%. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. Conclusion The CT-based RF is an improvement factor for clinical variables-only model. The radiomics nomogram provides individualized risk assessment of postoperative PFS for patients with RCC.
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Affiliation(s)
- Haijie Zhang
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China.,PET/CT Center, Department of Nuclear Medicine, First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Fu Yin
- College of Information Engineering, Shenzhen University, Shenzhen, China
| | - Menglin Chen
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Liyang Yang
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Anqi Qi
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Weiwei Cui
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Shanshan Yang
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ge Wen
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Wu M, Zhang Y, Zhang J, Zhang Y, Wang Y, Chen F, Luo Y, He S, Liu Y, Yang Q, Li Y, Wei H, Zhang H, Lu N, Wang S, Guo Y, Ye Z, Liu Y. A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study. Front Oncol 2022; 11:688679. [PMID: 35083133 PMCID: PMC8784873 DOI: 10.3389/fonc.2021.688679] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 12/16/2021] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE Based on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC). METHODS 131 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled from 7 institutions. Using largest lesion (LL) and target lesions (TL) approaches, we performed a radiomics analysis based on pretreatment NCE-CT (NCE-radiomics) and CE-CT images (CE-radiomics), respectively. Meanwhile, a combined-radiomics model based on NCE-CT and CE-CT images was constructed. Finally, we developed their corresponding nomograms incorporating clinical factors. ROC was used to evaluate models' predictive performance in the training and testing set, and a DeLong test was employed to compare the differences between different models. RESULTS For TL approach, both NCE-radiomics and CE-radiomics performed poorly in predicting response to immunotherapy. For LL approach, NCE-radiomics nomograms and CE-radiomics nomograms incorporating with clinical factor of distant metastasis all showed satisfactory results, reflected by the AUCs in the training (AUC=0.84, 95% CI: 0.75-0.92; AUC=0.77, 95% CI: 0.67-0.87) and test sets (AUC=0.78, 95% CI: 0.64-0.92, AUC=0.73, 95% CI: 0.57-0.88), respectively. Compared with the NCE-radiomics nomograms, the combined-radiomics nomogram showed incremental predictive capacity in the training set (AUC=0.85, 95% CI: 0.77-0.92) and test set (AUC=0.81, 95% CI: 0.67-0.94), respectively, but no statistical difference (P=0.86, P=0.79). CONCLUSION Compared with radiomics based on single NCE or CE-CT images, the combined-radiomics model has potential advantages to identify patients with NSCLC most likely to benefit from immunotherapy, and may effectively improve more precise and individualized decision support.
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Affiliation(s)
- Minghao Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yanyan Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jianing Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yina Wang
- Department of Medical Oncology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Chen
- Department of Radiology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yahong Luo
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Shuai He
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Yang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanying Li
- Department of Thoracic Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zhang
- Department of Radiology, Tianjin Chest Hospital, Tianjin, China
| | - Nian Lu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Guangzhou, China
| | - Sicong Wang
- Prognostic Diagnosis, GE Healthcare China, Beijing, China
| | - Yan Guo
- Prognostic Diagnosis, GE Healthcare China, Beijing, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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Non-contrast-enhanced CT texture analysis of primary and metastatic pancreatic ductal adenocarcinomas: value in assessment of histopathological grade and differences between primary and metastatic lesions. Abdom Radiol (NY) 2022; 47:4151-4159. [PMID: 36104481 PMCID: PMC9626421 DOI: 10.1007/s00261-022-03646-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/02/2022] [Accepted: 08/03/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To evaluate the utility of non-contrast-enhanced CT texture analysis (CTTA) for predicting the histopathological differentiation of pancreatic ductal adenocarcinomas (PDAC) and to compare non-contrast-enhanced CTTA texture features between primary PDAC and hepatic metastases of PDAC. METHODS This retrospective study included 120 patients with histopathologically confirmed PDAC. Sixty-five patients underwent CT-guided biopsy of primary PDAC, while 55 patients underwent CT-guided biopsy of hepatic PDAC metastasis. All lesions were segmented in non-contrast-enhanced CT scans for CTTA based on histogram analysis, co-occurrence matrix, and run-length matrix. Statistical analysis was conducted for 372 texture features using Mann-Whitney U test, Bonferroni-Holm correction, and receiver operating characteristic (ROC) analysis. A p value < 0.05 was considered statistically significant. RESULTS Three features were identified that differed significantly between histopathological G2 and G3 primary tumors. Of these, "low gray-level zone emphasis" yielded the largest AUC (0.87 ± 0.04), reaching a sensitivity and specificity of 0.76 and 0.83, respectively, when a cut-off value of 0.482 was applied. Fifty-four features differed significantly between primary and hepatic metastatic PDAC. CONCLUSION Non-contrast-enhanced CTTA of PDAC identified differences in texture features between primary G2 and G3 tumors that could be used for non-invasive tumor assessment. Extensive differences between the features of primary and metastatic PDAC on CTTA suggest differences in tumor microenvironment.
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Park HJ, Qin L, Bakouny Z, Krajewski KM, Van Allen EM, Choueiri TK, Shinagare AB. OUP accepted manuscript. Oncologist 2022; 27:389-397. [PMID: 35348767 PMCID: PMC9074990 DOI: 10.1093/oncolo/oyac034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 01/07/2022] [Indexed: 11/15/2022] Open
Abstract
Background Materials and Methods Results Conclusion
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Affiliation(s)
- Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Lei Qin
- Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ziad Bakouny
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Katherine M Krajewski
- Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Eliezer M Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Toni K Choueiri
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Atul B Shinagare
- Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Corresponding author: Atul B. Shinagare, Department of Radiology, Brigham and Womens Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Tel.: +1 6176322988; Fax: +1 6175828574;
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Sellami S, Bourbonne V, Hatt M, Tixier F, Bouzid D, Lucia F, Pradier O, Goasduff G, Visvikis D, Schick U. Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images. Acta Oncol 2022; 61:73-80. [PMID: 34632924 DOI: 10.1080/0284186x.2021.1983207] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. MATERIAL AND METHODS A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. RESULTS Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p < .001), and 0.99 (p < .001) for clinical, CBCT4-radiomics (Coarseness) and clinical + radiomics based models respectively. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p < .001). CONCLUSIONS We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models.
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Affiliation(s)
- S. Sellami
- Radiation Oncology Department, University Hospital, Brest, France
| | - V. Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
| | - M. Hatt
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Tixier
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - D. Bouzid
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Lucia
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - O. Pradier
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - G. Goasduff
- Radiation Oncology Department, University Hospital, Brest, France
| | - D. Visvikis
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - U. Schick
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
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Chen X, Fu R, Shao Q, Chen Y, Ye Q, Li S, He X, Zhu J. Application of artificial intelligence to pancreatic adenocarcinoma. Front Oncol 2022; 12:960056. [PMID: 35936738 PMCID: PMC9353734 DOI: 10.3389/fonc.2022.960056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 06/24/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues. METHODS We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles. RESULTS Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied. CONCLUSIONS AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.
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Affiliation(s)
- Xi Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Ruibiao Fu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qian Shao
- Department of Surgical Ward 1, Ningbo Women and Children’s Hospital, Ningbo, China
| | - Yan Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qinghuang Ye
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Sheng Li
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiongxiong He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jinhui Zhu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Jinhui Zhu,
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Cheng S, Jin Z, Xue H. Assessment of Response to Chemotherapy in Pancreatic Cancer with Liver Metastasis: CT Texture as a Predictive Biomarker. Diagnostics (Basel) 2021; 11:diagnostics11122252. [PMID: 34943489 PMCID: PMC8700536 DOI: 10.3390/diagnostics11122252] [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: 10/25/2021] [Revised: 11/21/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
In this paper, we assess changes in CT texture of metastatic liver lesions after treatment with chemotherapy in patients with pancreatic cancer and determine if texture parameters correlate with measured time to progression (TTP). This retrospective study included 110 patients with pancreatic cancer with liver metastasis, and mean, entropy, kurtosis, skewness, mean of positive pixels, and standard deviation (SD) values were extracted during texture analysis. Response assessment was also obtained by using RECIST 1.1, Choi and modified Choi criteria, respectively. The correlation of texture parameters and existing assessment criteria with TTP were evaluated using Kaplan-Meier and Cox regression analyses in the training cohort. Kaplan-Meier curves of the proportion of patients without disease progression were significantly different for several texture parameters, and were better than those for RECIST 1.1-, Choi-, and modified Choi-defined response (p < 0.05 vs. p = 0.398, p = 0.142, and p = 0.536, respectively). Cox regression analysis showed that percentage change in SD was an independent predictor of TTP (p = 0.016) and confirmed in the validation cohort (p = 0.019). In conclusion, CT texture parameters have the potential to become predictive imaging biomarkers for response evaluation in pancreatic cancer with liver metastasis.
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Chen X, Zhou M, Wang Z, Lu S, Chang S, Zhou Z. Immunotherapy treatment outcome prediction in metastatic melanoma through an automated multi-objective delta-radiomics model. Comput Biol Med 2021; 138:104916. [PMID: 34656867 DOI: 10.1016/j.compbiomed.2021.104916] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 09/29/2021] [Accepted: 09/29/2021] [Indexed: 01/18/2023]
Abstract
Based on recent studies, immunotherapy led by immune checkpoint inhibitors has significantly improved the patient survival rate and effectively reduced the recurrence risk. However, immunotherapy has different therapeutic effects for different patients, leading to difficulties in predicting the treatment response. Conversely, delta-radiomic features, which measure the difference between pre- and post-treatment through quantitative image features, have proven to be promising descriptors for treatment outcome prediction. Consequently, we developed an effective model termed as the automated multi-objective delta-radiomics (Auto-MODR) model for the prediction of immunotherapy response in metastatic melanoma. In Auto-MODR, delta-radiomic features and traditional radiomic features were used as inputs. Furthermore, a novel automated multi-objective model was developed to obtain more reliable and balanced results between sensitivity and specificity. We conducted extensive comparisons with existing studies on treatment outcome prediction. Our method achieved an area under the curve (AUC) of 0.86 in a cross-validation study and an AUC of 0.73 in an independent study. Compared with the model using conventional radiomic features (pre- and post-treatment) only, better performance can be obtained when conventional radiomic and delta-radiomic features are combined. Furthermore, Auto-MODR outperformed the currently available radiomic strategies.
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Affiliation(s)
- Xi Chen
- School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Meijuan Zhou
- School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Zhilong Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Si Lu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Renal Cancer and Melanoma, Peking University Cancer Hospital & Institute, Beijing, China
| | - Shaojie Chang
- School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Zhiguo Zhou
- School of Computer Science and Mathematics, University of Central Missouri, Warrensburg, MO, USA.
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Khodabakhshi Z, Amini M, Mostafaei S, Haddadi Avval A, Nazari M, Oveisi M, Shiri I, Zaidi H. Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information. J Digit Imaging 2021. [PMID: 34382117 DOI: 10.1007/s10278-021-00500-y/figures/5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2023] Open
Abstract
The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients' overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.
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Affiliation(s)
- Zahra Khodabakhshi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Shayan Mostafaei
- Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
- Epidemiology and Biostatistics Unit, Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine , Kings College London, London, UK
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Korte JC, Cardenas C, Hardcastle N, Kron T, Wang J, Bahig H, Elgohari B, Ger R, Court L, Fuller CD, Ng SP. Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer. Sci Rep 2021; 11:17633. [PMID: 34480036 PMCID: PMC8417253 DOI: 10.1038/s41598-021-96600-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
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Affiliation(s)
- James C. Korte
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XDepartment of Biomedical Engineering, University of Melbourne, Melbourne, Australia
| | - Carlos Cardenas
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Nicholas Hardcastle
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1007.60000 0004 0486 528XCentre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Tomas Kron
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XSir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Jihong Wang
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Houda Bahig
- grid.410559.c0000 0001 0743 2111Radiation Oncology Department, Centre Hospitalier de l’Université de Montréal, Montreal, Canada
| | - Baher Elgohari
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.10251.370000000103426662Clinical Oncology & Nuclear Medicine Department, Mansoura University, Mansoura, Egypt
| | - Rachel Ger
- grid.470142.40000 0004 0443 9766Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ USA
| | - Laurence Court
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Clifton D. Fuller
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Sweet Ping Ng
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.1055.10000000403978434Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia ,grid.482637.cDepartment of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Melbourne, Australia
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Zhang H, Yin F, Chen M, Qi A, Yang L, Cui W, Yang S, Wen G. [Predicting postoperative recurrence of stage Ⅰ-Ⅲ renal clear cell carcinoma based on preoperative CT radiomics feature nomogram]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:1358-1365. [PMID: 34658350 DOI: 10.12122/j.issn.1673-4254.2021.09.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE To explore the preoperative radiomics features (RFs) and construct a nomogram for predicting postoperative recurrence of stage Ⅰ-Ⅲ clear cell renal carcinoma (ccRCC). METHODS The clinicopathological data and preoperative enhanced CT images collected from 256 patients with ccRCC were used as the training dataset (175 patients) and test dataset (81 patients). The enhanced CT images of the tumor were segmented using ITK-SNAP software, and the RFs were extracted using the PyRadiomics computing platform. In the training dataset, the RFs were screened based on Lasso-CV algorithm, and the Rad_score was calculated. The Clinic factors were screened by univariate and multivariate logistic regression analysis of the clinical and pathological factors and CT characteristics. The Rad_score, Clinic、Rad_score + Clinic nomograms were constructed and verified using the test dataset. The performance, discrimination power and calibration of the nomograms were compared, and their clinical value was evaluated using decision curve analysis. RESULTS Six RFs were retained to calculate the Rad_score. The Clinic factors included Rad_score, KPS score, platelet, calcification and TNM clinical stage. In terms of discrimination, the Rad_score + Clinic nomogram showed better performance (AUC=0.84 for training set; AUC=0.85 for test set) than the Rad_score nomogram (AUC=0.78 for training set, P=0.029; AUC=0.77 for Test set, P=0.025) and Clinic nomogram (AUC=0.77 for training set, P=0.014; AUC=0.77 for test set, P=0.011). In terms of calibration, the P value for goodness of fit test of the Rad_score+Clinic nomogram was 0.065 for the training set and 0.628 for the test set. Decision curve analysis showed a greater clinical value of the Rad_score+Clinic nomogram with Rad_score than the Clinic nomogram without Rad_score. CONCLUSION The nomogram based on preoperative CT RFs has a high value for predicting postoperative recurrence of stage Ⅰ-Ⅲ ccRCC to facilitate individualized treatment of RCC.
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Affiliation(s)
- H Zhang
- PET/CT Center, First Affiliated Hospital of Shenzhen University, Shenzhen 518052, China.,Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - F Yin
- Shenzhen University School of Information Engineering, Shenzhen 518052, China
| | - M Chen
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - A Qi
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - L Yang
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - W Cui
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - S Yang
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - G Wen
- Department of Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
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MR-Based Radiomics for Differential Diagnosis between Cystic Pituitary Adenoma and Rathke Cleft Cyst. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6438861. [PMID: 34422095 PMCID: PMC8373489 DOI: 10.1155/2021/6438861] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/12/2021] [Indexed: 11/17/2022]
Abstract
Background It is often tricky to differentiate cystic pituitary adenoma from Rathke cleft cyst with visual inspection because of similar MRI presentations between them. We aimed to design an MR-based radiomics model for improving differential diagnosis between them. Methods Conventional diagnostic MRI data (T1-,T2-, and postcontrast T1-weighted MR images) were obtained from 215 pathologically confirmed patients (105 cases with cystic pituitary adenoma and the other 110 cases with Rathke cleft cyst) and were divided into training (n = 172) and test sets (n = 43). MRI radiomics features were extracted from the imaging data, and semantic imaging features (n = 15) were visually estimated by two radiologists. Four classifiers were used to construct radiomics models through 5-fold crossvalidation after feature selection with least absolute shrinkage and selection operator. An integrated model by combining radiomics and semantic features was further constructed. The diagnostic performance was validated in the test set. Receiver operating characteristic curve was used to evaluate and compare the performance of the models at the background of diagnostic performance by radiologist. Results In test set, the combined radiomics and semantic model using ANN classifier obtained the best classification performance with an AUC of 0.848 (95% CI: 0.750-0.946), accuracy of 76.7% (95% CI: 64.1-89.4%), sensitivity of 73.9% (95% CI: 56.0-91.9%), and specificity of 80.0% (95% CI: 62.5-97.5%) and performed better than multiparametric model (AUC = 0.792, 95% CI: 0.674-0.910) or semantic model (AUC = 0.823, 95% CI: 0.705-0.941). The two radiologists had an accuracy of 69.8% and 74.4%, respectively, sensitivity of 69.6% and 73.9%, and specificity of 70.0% and 75.0%. Conclusions The MR-based radiomics model had technical feasibility and good diagnostic performance in the differential diagnosis between cystic pituitary adenoma and Rathke cleft cyst.
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