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Liu J, Hua L, Wang F, Chen M, Sun Y, Hu Z, Shu L, He A, Liu M, Yang Q, Zhu J, Qian Y. Comparison of four MRI diffusion models to differentiate benign from metastatic retropharyngeal lymph nodes. Eur Radiol Exp 2025; 9:50. [PMID: 40358858 PMCID: PMC12075726 DOI: 10.1186/s41747-025-00590-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2025] [Accepted: 04/16/2025] [Indexed: 05/15/2025] Open
Abstract
BACKGROUND Conventional magnetic resonance diffusion-weighted imaging (DWI) and morphological features have limitations in distinguishing benign from metastatic retropharyngeal lymph nodes (RLNs). We aimed to compare the value of continuous-time random walk (CTRW), fractional-order calculus (FROC), stretched-exponential model (SEM), and conventional DWI, in combination with morphological features, for differentiating between the two groups. METHODS Fifty-nine patients with 68 RLNs (23 benign and 45 metastatic) were enrolled. All patients underwent DWI with 12 b-values. Diffusion data were reconstructed using conventional DWI, SEM, FROC, and CTRW models, yielding nine parameters: apparent diffusion coefficient (ADC), distributed diffusion coefficient (DDC)SEM, αSEM, DFROC, βFROC, μFROC, DCTRW, αCTRW, and βCTRW. Diffusion parameters and morphological features were compared using Mann-Whitney U, independent sample t, or χ2 tests. Logistic regression analysis was performed to identify the best diffusion indicator for classification and to develop a multiparameter model combining morphological features. Area under the receiver operating curve (AUC) and DeLong tests were used. RESULTS Significant differences in diffusion parameters were found between benign and metastatic RLNs, except for αCTRW (p ≤ 0.022). Benign RLNs exhibited higher ADC, DDCSEM, DFROC, and DCTRW, while metastatic RLNs had higher αSEM, βFROC, μFROC, and βCTRW. Multivariate logistic regression analysis identified βCTRW as the optimal single diffusion indicator (AUC = 0.913). The combined model of βCTRW with morphological features further improved diagnostic performance and yielded an AUC of 0.948. CONCLUSION βCTRW is an effective noninvasive biomarker for distinguishing between benign and metastatic RLNs. Thus, combining βCTRW with morphological features enhances diagnostic efficiency. RELEVANCE STATEMENT This study demonstrates that βCTRW, derived from the continuous-time random walk diffusion model, when integrated with morphological features, offers a reliable, noninvasive diagnostic approach for differentiating between benign and metastatic retropharyngeal lymph nodes. KEY POINTS Non-Gaussian diffusion metrics outperformed conventional DWI. βCTRW was the best indicator for distinguishing benign from metastatic lymph nodes. Combining βCTRW with minimal axial diameter further improved diagnostic efficiency.
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Affiliation(s)
- Jun Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Medical Imaging, Anqing Medical Center of Anhui Medical University, Anqing, China
| | - Li Hua
- Department of Laboratory Medicine, Anqing Medical Center of Anhui Medical University, Anqing, China
| | - Fei Wang
- Department of Medical Imaging, Anqing Medical Center of Anhui Medical University, Anqing, China
| | - Ming Chen
- Department of Medical Imaging, Anqing Medical Center of Anhui Medical University, Anqing, China
| | - Yinan Sun
- Department of Medical Imaging, Anqing Medical Center of Anhui Medical University, Anqing, China
| | - Zhi Hu
- Department of Medical Imaging, Anqing Medical Center of Anhui Medical University, Anqing, China
| | - Luqing Shu
- Department of Medical Imaging, Anqing Medical Center of Anhui Medical University, Anqing, China
| | - Andong He
- Department of Medical Imaging, Anqing Medical Center of Anhui Medical University, Anqing, China
| | - Mengxiao Liu
- MR Search & Marketing Department, Siemens Healthineers Co., Ltd., Shanghai, China
| | - Qing Yang
- Department of Medical Imaging, Anqing Medical Center of Anhui Medical University, Anqing, China.
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China.
| | - Juan Zhu
- Department of Medical Imaging, Anqing Medical Center of Anhui Medical University, Anqing, China.
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
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Li J, Zhang H, Bei T, Wang Y, Ma F, Wang S, Li H, Qu J. Advanced diffusion-weighted MRI models for preoperative prediction of lymph node metastasis in resectable gastric cancer. Abdom Radiol (NY) 2025; 50:1057-1068. [PMID: 39254709 DOI: 10.1007/s00261-024-04559-3] [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: 07/18/2024] [Revised: 08/20/2024] [Accepted: 08/29/2024] [Indexed: 09/11/2024]
Abstract
OBJECTIVE To investigate the potential of six advanced diffusion-weighted imaging (DWI) models for preoperative prediction of lymph node metastasis (LNM) in resectable gastric cancer (GC). METHODS Between Nov 2022 and Nov 2023, standard MRI scans were prospectively performed in consecutive patients with endoscopic pathology-confirmed gastric adenocarcinoma who were referred for direct radical gastrectomy. Six DWI models, including fractional order calculus (FROC), continuous-time random walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), the mono-exponential model (MEM) and the stretched exponential model (SEM) were computed. Surgical pathologic diagnosis of LNM was the reference standard, and patients were classified into LNM-positive or LNM-negative groups accordingly. The morphological features and quantitative parameters of the DWI models in different LNM categories were analyzed and compared. Multivariable logistic regression was used to screen significant predictors. Receiver-operating characteristic curves and the area under the curve (AUC) were plotted to evaluate the performances, the Delong test was performed to compare the AUCs. RESULTS In the LNM-positive group, tumor thickness and kurtosis (DKI_K) were significantly higher, while anomalous diffusion coefficient (CTRW_D), diffusivity (DKI_D), diffusion coefficient (FROC_D), pseudodiffusion coefficient (IVIM_D*), perfusion fraction (IVIM_f), and ADC were lower compared to the LNM-negative group. Clinical tumor staging (cT) and CTRW_D were independent predictors. Their combination demonstrated a superior AUC of 0.930, significantly higher than that of individual parameters. CONCLUSIONS Tumor thickness, DKI_K, CTRW_D, DKI_D, FROC_D, IVIM_D*, IVIM_f and ADC were associated with LNM status. The combination of independent predictors of cT and CTRW_D further enhanced the performance.
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Affiliation(s)
- Jing Li
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
| | - Hongkai Zhang
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Tianxia Bei
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Yi Wang
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Fei Ma
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Shaoyu Wang
- MR Research Collaboration, Siemens Healthineers, Shanghai, China
| | - Haocheng Li
- Sanquan College of Xinxiang Medical University, Xinxiang, China
| | - Jinrong Qu
- The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
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Zhou M, Chen M, Chen M, Yan X, Yang G, Huang H. Predictive value of mono-exponential and multiple mathematical models in locally advanced rectal cancer response to neoadjuvant chemoradiotherapy. Abdom Radiol (NY) 2025; 50:1105-1116. [PMID: 39276193 DOI: 10.1007/s00261-024-04588-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Revised: 09/05/2024] [Accepted: 09/09/2024] [Indexed: 09/16/2024]
Abstract
PURPOSE This prospective study aimed to assess the predictive value of mono-exponential and multiple mathematical diffusion-weighted imaging (DWI) models in determining the response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). METHODS The study included 103 LARC patients scheduled for preoperative chemoradiotherapy between December 2021 and June 2023 Magnetic resonance imaging (MRI) scans were performed using a 3.0-T MR scanner, encompassing sagittal, axial, and oblique coronal T2-weighted images without fat saturation, along with DWI perpendicular to the rectum's long axis. Various DWI parameters, including apparent diffusion coefficient (ADC), stretched exponential model (SEM), continuous-time random-walk model (CTRW), and fractional-order calculus model (FROC), were measured. The pathologic complete response (pCR) rate and tumor downstaging (T-downstage) rate were determined. RESULTS After nCRT, SEM-α, SEM-DDC, CTRW-α, CTRW-β, CTRW-D, FROC-β, and ADC values were significantly higher in the pCR group compared to the non-pCR group (all P < 0.05). SEM-DDC, CTRW-α, CTRW-D, FROC-β, FROC-µ, and ADC values were significantly higher in the T-downstage group (ypT0-1) than in the non-T-downstage group (ypT2-4) (P < 0.05). The combination of CTRW (α + β + D) exhibited the best diagnostic performance for assessing pCR after nCRT (AUC = 0.840, P < 0.001). Pre-nCRT CTRW (α + β) demonstrated a predictive AUC of 0.652 (95%CI: 0.552-0.743), 90.3% sensitivity, and 43.1% specificity for pCR. Regarding T-downstage assessment after nCRT, the combination of CTRW (α + D) yielded the best diagnostic performance (AUC = 0.877, P = 0.048). CONCLUSION In LARC patients, imaging markers derived from CTRW show promise in predicting tumor response before nCRT and assessing pCR after nCRT.
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Affiliation(s)
- Mi Zhou
- sichuan provincial orthopedics hospital, Chengdu, China
| | - Mengyuan Chen
- Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Xu Yan
- Siemens Healthineers (China), Pudong, China
| | - Guang Yang
- East China Normal University, Shanghai, China
| | - Hongyun Huang
- Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
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Oliveira BF, Ribeiro CO, de Sá Sousa CM, Lopes MC, Lopes AJ, de Melo PL. Respiratory abnormalities in sarcoidosis: physiopathology and early diagnosis using oscillometry combined with respiratory modeling. BMC Pulm Med 2025; 25:68. [PMID: 39920601 PMCID: PMC11806852 DOI: 10.1186/s12890-025-03510-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 01/20/2025] [Indexed: 02/09/2025] Open
Abstract
BACKGROUND Sarcoidosis is a multisystemic syndrome of uncertain etiology with abnormal respiratory findings in approximately 90% of cases. Spirometry is the most common lung function test used for assessing lung function in diagnosis and monitoring pulmonary health. Respiratory oscillometry allows a simple alternative for the analysis of respiratory abnormalities. Integer-order and fractional-order modeling have increasingly been used to interpret measurements obtained from oscillometry, offering a detailed description of the respiratory system. In this study, we aimed to enhance our understanding of the pathophysiological changes in sarcoidosis and assess the diagnostic accuracy of these models. METHODS This observational study includes 25 controls and 50 individuals with sarcoidosis divided into normal to spirometry (SNS) and abnormal spirometry (SAS). The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). RESULTS The integer-order model showed significant airway and total resistance increases in the SNS and SAS groups. There was a reduction in compliance and an increase in peripheral resistance in the SAS group (p < 0.001). The fractional-order model showed increased energy dissipation and hysteresivity in the SNS and SAS groups. Correlation analysis revealed significant associations among model and spirometric parameters, where the strongest associations were between total resistance and FEV1 (r: -0.600, p = 0.0001). The diagnostic accuracy analysis showed that total resistance and hysteresivity were the best parameters, reaching an AUC = 0.986 and 0.938 in the SNS and SAS groups, respectively. CONCLUSION The studied models provided a deeper understanding of pulmonary mechanical changes in sarcoidosis. The results suggest that parameters obtained through the studied models enhance evaluation and enable better management of these patients. Specifically, total resistance and hysteresivity parameters demonstrated diagnostic potential, which may be beneficial for the early identification of individuals with sarcoidosis, even when spirometry results are within normal ranges.
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Affiliation(s)
- Bruno Falcão Oliveira
- Department of Physiology, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | | | - Mariana Carneiro Lopes
- Faculty of Medical Sciences, State University of Rio de Janeiro, Rio de Janeiro, Brazil
- Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Agnaldo José Lopes
- Pulmonary Function Laboratory, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Pedro Lopes de Melo
- Department of Physiology, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
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Zhou M, Huang H, Bao D, Chen M, Lu F. Assessment of prognostic indicators and KRAS mutations in rectal cancer using a fractional-order calculus MR diffusion model: whole tumor histogram analysis. Abdom Radiol (NY) 2025; 50:569-578. [PMID: 39152230 DOI: 10.1007/s00261-024-04523-1] [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: 06/10/2024] [Revised: 08/04/2024] [Accepted: 08/10/2024] [Indexed: 08/19/2024]
Abstract
PURPOSE This study aims to explore the relationship between apparent diffusion coefficient (ADC) and fractional-order calculus (FROC)-specific parameters with prognostic indicators and Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation status in rectal cancer. METHODS One hundred fifty-eight patients with rectal cancer were retrospectively enrolled. Histogram measurements of ADC, diffusion coefficient (D), intravoxel diffusion heterogeneity (β), and a microstructural quantity (μ) were estimated for the whole-tumor volume. The relationships between histogram measurements and prognostic indicators were evaluated. The efficacy of histogram measurements, both conducted singly and in conjunction, for evaluating different KRAS mutation statuses was also assessed. The performance of mean and median histogram measurements in evaluating various KRAS mutation statuses was assessed using Receiver Operating Characteristic (ROC) curve analysis. A p-value of less than 0.05 was considered statistically significant. RESULTS The histogram measurements of ADC, D, β, and μ differed significantly between well-moderately differentiated groups and poorly differentiated groups, T1-2 and T3-4 subgroups, lymph node metastasis (LNM)-negative and LNM-positive subgroups, extranodal extension (ENE)-negative and ENE-positive subgroups, tumor deposit (TD)-negative and TD-positive subgroups, and lymphovascular invasion (LVI)-negative and LVI-positive subgroups. The combination of Dmean, βmean, and μmean achieved the highest performance [The area under the ROC curve (AUC) = 0.904] in evaluating the KRAS mutation status. CONCLUSION When assessing parameters from the FROC model as potential biomarkers through histograms, they surpass traditional ADC values in distinguishing prognostic indicators and determining KRAS mutation status in rectal cancer.
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Affiliation(s)
- Mi Zhou
- Department of Radiology, Sichuan Provincial Orthpaedics Hospital, Chengdu, 610041, People's Republic of China.
| | - Hongyun Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China
| | - Deying Bao
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China
| | - Meining Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, 200135, China
| | - Fulin Lu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China
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Bi Q, Deng Y, Xu N, Wu S, Zhang H, Huang Y, Zhang S, Wang S, Wu Y, Wu K, Zhang J. Differentiation of early-stage endometrial carcinoma from benign endometrial lesions: a comparative study of six diffusion models. Quant Imaging Med Surg 2025; 15:121-134. [PMID: 39839005 PMCID: PMC11744118 DOI: 10.21037/qims-24-896] [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: 05/03/2024] [Accepted: 09/12/2024] [Indexed: 01/23/2025]
Abstract
Background Accurate differentiation between benign and malignant endometrial lesions holds substantial clinical importance. This study aimed to evaluate the efficacy of various diffusion models in the preoperative diagnosis of early-stage endometrial carcinoma (EC). Methods A total of 72 consecutive patients with benign or malignant endometrial lesions from the First People's Hospital of Yunnan Province were prospectively enrolled between April 2021 and July 2023. Fourteen diffusion parameters derived from monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), stretched exponential model (SEM), continuous-time random walk (CTRW), and fractional order calculus (FROC) models were calculated and compared. Independent predictors of early-stage EC were identified using logistic regression analysis. The performance of the diffusion parameters, both individually and in combination with effective clinical indicators, for differentiating benign and malignant endometrial lesions was evaluated. Results This study consisted of 17 patients with benign endometrial lesions and 55 patients with EC. Significant differences in age and menopausal status were observed between the benign and malignant endometrial groups (P=0.015 and P=0.011, respectively). With the exception of the pseudodiffusion coefficient (D*) and perfusion fraction (f), all other parameters exhibited significant differences between the benign and malignant groups (P<0.05). Mean kurtosis (MK), true diffusion coefficient (D), and temporal diffusion heterogeneity index (αCTRW) were identified as independent predictors of early-stage EC, achieving an area under the curve (AUC) of 0.903 [95% confidence interval (CI): 0.824-0.982], surpassing that of any individual diffusion parameter. The combination of these independent predictors with menopausal status yielded the highest AUC (0.922, 95% CI: 0.845-0.999), accuracy (93.1%), and sensitivity (100.0%). Conclusions MK, D, and αCTRW have the potential to serve as independent predictors in predicting early-stage EC, and the performance can be enhanced when combined with menopausal status.
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Affiliation(s)
- Qiu Bi
- Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Yuchen Deng
- Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Na Xu
- Department of Radiology, Municipal People’s Hospital of Chuxiong, Chuxiong, China
| | - Shan Wu
- Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Hongjiang Zhang
- Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Yichen Huang
- Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Shuni Zhang
- Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Shaoyu Wang
- MR Research Collaboration, Siemens Healthineers, Shanghai, China
| | - Yunzhu Wu
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Kunhua Wu
- Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Jie Zhang
- Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
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Zhu J, Gan M, Yang Y, Pang H, Zhu Z, Hou Z, Hou G, Wang C. Noninvasive assessment of single kidney glomerular filtration rate using multiple diffusion weighted imaging models. Abdom Radiol (NY) 2025; 50:336-345. [PMID: 39373771 DOI: 10.1007/s00261-024-04489-0] [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: 05/31/2024] [Revised: 07/01/2024] [Accepted: 07/02/2024] [Indexed: 10/08/2024]
Abstract
PURPOSE This study aimed to assess single kidney glomerular filtration rate (GFR) using various diffusion weighted imaging (DWI) models. METHODS We reviewed adult patients with kidney diseases who underwent magnetic resonance imaging (MRI) examination from February 2021 to December 2023. DWI with 13 b-values was performed using 3.0-T scanners. Diffusion parameters were calculated with multi-slice ROIs positioned in renal parenchyma using four DWI models, including monoexponential model (MEM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and intravoxel incoherent motion (IVIM). The split GFRs were measured by 99mTc-DTPA scintigraphy using Gates' method. Four different regression algorithms including the linear regression, regression tree, Gaussian regression and support vector machine (SVM) regression were employed to predict the GFR value based on different diffusion parameters. The leave-one-out cross validation was used to evaluate prediction ability of different models, and the performance of each model was quantified using the root mean square error (RMSE) and correlation coefficient. RESULTS Fifteen (male/female, 10/5; age, 41.60±10.83 years) patients were included in this study. Among the four DWI models, the IVIM parameters with SVM regression model achieved the best performance with 0.184 RMSE and 0.789 correlation coefficient ( p < 0.001 ). The parameters combining the four DWI models with SVM regression algorithm achieved the best performance in this study, with 0.171 RMSE and 0.815 correlation coefficient ( p < 0.001 ). CONCLUSION The DWI characteristics are able to serve as imaging biomarkers for assessing the function of single kidney. The integration of DWI into clinical practice could contribute to the advancement of non-invasive diagnostic methodologies.
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Affiliation(s)
- Jianbing Zhu
- Department of Radiology, Affiliated Hospital of Medical School, Suzhou Hospital, Suzhou Research Center of Medical School, Nanjing University, Suzhou, 215153, China
- Tongan Branch Hospital, Affiliated Hospital of Medical School, Suzhou Hospital, Nanjing University, Suzhou, 215100, China
| | - Meng Gan
- School of Computer Engineering, Suzhou Vocational University, Suzhou, 215104, China
| | - Yi Yang
- Department of Nuclear Medicine, Affiliated Hospital of Medical School, Suzhou Hospital, Nanjing University, Suzhou, 215153, China
| | - Hongquan Pang
- Department of Radiology, Affiliated Hospital of Medical School, Suzhou Hospital, Suzhou Research Center of Medical School, Nanjing University, Suzhou, 215153, China
| | - Zhengyang Zhu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210093, China
| | - Zujun Hou
- The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Guocun Hou
- Department of Nephrology, Affiliated Hospital of Medical School, Suzhou Hospital, Nanjing University, Suzhou, 215153, China.
| | - Cong Wang
- School of Electronics and Information Engineering, Suzhou Vocational University, Suzhou, 215104, China.
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Bai B, Cui L, Chu F, Wang Z, Zhao K, Wang S, Wang S, Yan X, Wang M, Kamel IR, Yang G, Qu J. Multiple diffusion models for predicting pathologic response of esophageal squamous cell carcinoma to neoadjuvant chemotherapy. Abdom Radiol (NY) 2024; 49:4216-4226. [PMID: 38954001 DOI: 10.1007/s00261-024-04474-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/22/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND To assess the feasibility and diagnostic performance of the fractional order calculus (FROC), continuous-time random-walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), mono-exponential (MEM) and stretched exponential models (SEM) for predicting response to neoadjuvant chemotherapy (NACT) in patients with esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS This study prospectively included consecutive ESCC patients with baseline and follow up MR imaging and pathologically confirmed cT1-4aN + M0 or T3-4aN0M0 and underwent radical resection after neoadjuvant chemotherapy (NACT) between July 2019 and January 2023. Patients were divided into pCR (TRG 0) and non-pCR (TRG1 + 2 + 3) groups according to tumor regression grading (TRG). The Pre-, Post- and Delta-treatment models were built. 18 predictive models were generated according to different feature categories, based on six models by five-fold cross-validation. Areas under the curve (AUCs) of the models were compared by using DeLong method. RESULTS Overall, 90 patients (71 men, 19 women; mean age, 64 years ± 6 [SD]) received NACT and underwent baseline and Post-NACT esophageal MRI, with 29 patients in the pCR group and 61 patients in the non-pCR group. Among 18 predictive models, The Pre-, Post-, and Delta-CTRW model showed good predictive efficacy (AUC = 0.722, 0.833 and 0.790). Additionally, the Post-FROC model (AUC = 0.907) also exhibited good diagnostic performance. CONCLUSIONS Our study indicates that the CTRW model, along with the Post-FROC model, holds significant promise for the future of NACT efficacy prediction in ESCC patients.
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Affiliation(s)
- Bingmei Bai
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Long Cui
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Funing Chu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Zhaoqi Wang
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Keke Zhao
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Shuting Wang
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Mengzhu Wang
- MR Research Collaboration, Siemens Healthineers Ltd, Beijing, 100000, China
| | - Ihab R Kamel
- Department of Radiology, Anschutz Medical Campus, University of Colorado Denver, 12401 East 17Th Avenue, Aurora, CO, 80045, USA
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Jinrong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China.
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Zhou M, Huang H, Bao D, Chen M. Fractional order calculus model-derived histogram metrics for assessing pathological complete response to neoadjuvant chemotherapy in locally advanced rectal cancer. Clin Imaging 2024; 116:110327. [PMID: 39454478 DOI: 10.1016/j.clinimag.2024.110327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 10/10/2024] [Accepted: 10/10/2024] [Indexed: 10/28/2024]
Abstract
AIM This study evaluates the value of diffusion fractional order calculus (FROC) model for the assessment of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes. MATERIALS AND METHODS Ninety-eight patients were prospectively included. Every patient received MRI scans before and after nCRT using a 3.0-Tesla MRI machine. Parameters of the FROC model, including the anomalous diffusion coefficient (D), intravoxel diffusion heterogeneity (β), spatial parameter (μ), and the standard apparent diffusion coefficient (ADC), were calculated. Changes in median values (ΔX-median) and ratio (rΔX-median) were calculated. Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance. RESULTS Pre-treatmentβ-10th percentile values were significantly lower in the pCR group compared to the non-pCR group (p < 0.001). The Δβ-median showed higher diagnostic accuracy (AUC = 0.870) and sensitivity (76.67 %) for predicting tumor response compared to MRI tumor regression grading (mrTRG) scores (AUC = 0.722; sensitivity = 90.0 %). DISCUSSION The use of FROC alongside comprehensive tumor histogram analysis was found to be practical and effective in evaluating the tumor response to nCRT in LARC patients.
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Affiliation(s)
- Mi Zhou
- Department of Radiology, Sichuan Provincial Orthpaedics Hospital, Chengdu 610041, PR China.
| | - Hongyun Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, PR China
| | - Deying Bao
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, PR China
| | - Meining Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai 200135, PR China
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Damoci CB, Merrill JR, Sun Y, Lyons SK, Olive KP. Addressing Biological Questions with Preclinical Cancer Imaging. Cold Spring Harb Perspect Med 2024; 14:a041378. [PMID: 38503500 PMCID: PMC11529846 DOI: 10.1101/cshperspect.a041378] [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
The broad application of noninvasive imaging has transformed preclinical cancer research, providing a powerful means to measure dynamic processes in living animals. While imaging technologies are routinely used to monitor tumor growth in model systems, their greatest potential lies in their ability to answer fundamental biological questions. Here we present the broad range of potential imaging applications according to the needs of a cancer biologist with a focus on some of the common biological processes that can be used to visualize and measure. Topics include imaging metastasis; biophysical properties such as perfusion, diffusion, oxygenation, and stiffness; imaging the immune system and tumor microenvironment; and imaging tumor metabolism. We also discuss the general ability of each approach and the level of training needed to both acquire and analyze images. The overall goal is to provide a practical guide for cancer biologists interested in answering biological questions with preclinical imaging technologies.
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Affiliation(s)
- Chris B Damoci
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Joseph R Merrill
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Yanping Sun
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Scott K Lyons
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
| | - Kenneth P Olive
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York 10032, USA
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York 10032, USA
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11
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Su Y, Zeng K, Yan Z, Yang X, Yang L, Yang L, Han R, Huang F, Deng H, Duan X. Predicting the Ki-67 proliferation index in cervical cancer: a preliminary comparative study of four non-Gaussian diffusion-weighted imaging models combined with histogram analysis. Quant Imaging Med Surg 2024; 14:7484-7495. [PMID: 39429562 PMCID: PMC11485368 DOI: 10.21037/qims-24-576] [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: 03/21/2024] [Accepted: 08/20/2024] [Indexed: 10/22/2024]
Abstract
Background The prognosis for patients with cervical cancer (CC) is strongly correlated with the Ki-67 proliferation index (PI). However, the Ki-67 PI obtained through biopsy has certain limitations. The non-Gaussian distribution diffusion model of magnetic resonance imaging (MRI) may play an important role in characterizing tissue heterogeneity. At present, there are limited data available concerning the prediction of Ki-67 PI using models based on histogram features of non-Gaussian diffusion distribution. This study aimed to determine whether preoperative histogram features from multiple non-Gaussian models of diffusion-weighted imaging can predict the Ki-67 PI in patients with CC. Methods Our cross-sectional prospective study recruited a total of 53 patients suspected of having CC who underwent 3.0-T MRI at Sun Yat-sen Memorial Hospital of Sun Yat-sen University between January 2022 and January 2023. Fifteen b values (0-4,000 s/mm2) were used for diffusion-weighted imaging. A total of nine parameters from four non-Gaussian diffusion-weighted imaging models, including continuous-time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM), were used. Whole-tumor volumetric histogram analysis of these parameters was then obtained. In logistic regression, significant histogram characteristics were statistically examined across two groups to build the final prediction model. To assess diagnostic parameters of the proposed model in the diagnosis of the Ki-67 PI, along with the sensitivity, specificity, and diagnostic accuracy of these various parameters from the four models, receiver operating feature analysis was applied. Results Among the 53 patients (55.3±9.6 years, ranging from 23 to 79 years) included in the study, 15 had a Ki-67 PI ≤50% and 38 had a Ki-67 PI >50%. Univariable analysis determined that 12 histogram features were statistically different between the two groups. In multivariable logistic regression, we ultimately selected 6 histogram features to construct the final prediction model, with CTRW_α_10th percentile [odds ratio (OR) =0.955; 95% confidence interval (CI): 0.92-0.99; P=0.019], CTRW_α_robust mean absolute deviation (OR =0.893; 95% CI: 0.81-0.99; P=0.028), and CTRW_α_uniformity (OR =0.000, 95% CI: 0.00-0.90, P=0.047) being the independent predictive variables. The area under the curve of the combined prediction model was 0.845 (95% CI: 0.74-0.95), with a sensitivity of 78.9% (95% CI: 0.63-0.90), a specificity of 86.7% (95% CI: 0.60-0.98), an accuracy of 81.1% (95% CI: 0.68-0.91), a positive predictive value of 93.8% (95% CI: 0.79-0.99), and a negative predictive value of 61.9% (95% CI: 0.38-0.82). Conclusions The histogram features of multiple non-Gaussian diffusion-weighted imaging can help to predict the Ki-67 PI of CC, providing a new method for the noninvasive evaluation of critical biological features of CC.
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Affiliation(s)
- Yun Su
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kunjie Zeng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhuoheng Yan
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaojun Yang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lingjie Yang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lu Yang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Riyu Han
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Fengqiong Huang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hong Deng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaohui Duan
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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Wang F, Sun YN, Zhang BT, Yang Q, He AD, Xu WY, Liu J, Liu MX, Li XH, Yu YQ, Zhu J. Value of fractional-order calculus (FROC) model diffusion-weighted imaging combined with simultaneous multi-slice (SMS) acceleration technology for evaluating benign and malignant breast lesions. BMC Med Imaging 2024; 24:190. [PMID: 39075336 PMCID: PMC11285176 DOI: 10.1186/s12880-024-01368-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND This study explores the diagnostic value of combining fractional-order calculus (FROC) diffusion-weighted model with simultaneous multi-slice (SMS) acceleration technology in distinguishing benign and malignant breast lesions. METHODS 178 lesions (73 benign, 105 malignant) underwent magnetic resonance imaging with diffusion-weighted imaging using multiple b-values (14 b-values, highest 3000 s/mm2). Independent samples t-test or Mann-Whitney U test compared image quality scores, FROC model parameters (D,, ), and ADC values between two groups. Multivariate logistic regression analysis identified independent variables and constructed nomograms. Model discrimination ability was assessed with receiver operating characteristic (ROC) curve and calibration chart. Spearman correlation analysis and Bland-Altman plot evaluated parameter correlation and consistency. RESULTS Malignant lesions exhibited lower D, and ADC values than benign lesions (P < 0.05), with higher values (P < 0.05). In SSEPI-DWI and SMS-SSEPI-DWI sequences, the AUC and diagnostic accuracy of D value are maximal, with D value demonstrating the highest diagnostic sensitivity, while value exhibits the highest specificity. The D and combined model had the highest AUC and accuracy. D and ADC values showed high correlation between sequences, and moderate. Bland-Altman plot demonstrated unbiased parameter values. CONCLUSION SMS-SSEPI-DWI FROC model provides good image quality and lesion characteristic values within an acceptable time. It shows consistent diagnostic performance compared to SSEPI-DWI, particularly in D and values, and significantly reduces scanning time.
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Affiliation(s)
- Fei Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218, Jixi Road, Hefei, 230032, China
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Yi-Nan Sun
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Bao-Ti Zhang
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Qing Yang
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - An-Dong He
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Wang-Yan Xu
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Jun Liu
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Meng-Xiao Liu
- MR Research & Marketing Department, Siemens Healthineers Co., Ltd, No.278, Zhouzugong Road, Shanghai, 201318, China
| | - Xiao-Hu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218, Jixi Road, Hefei, 230032, China
| | - Yong-Qiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218, Jixi Road, Hefei, 230032, China.
| | - Juan Zhu
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China.
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Yang L, Hu H, Yang X, Yan Z, Shi G, Yang L, Wang Y, Han R, Yan X, Wang M, Ban X, Duan X. Whole-tumor histogram analysis of multiple non-Gaussian diffusion models at high b values for assessing cervical cancer. Abdom Radiol (NY) 2024; 49:2513-2524. [PMID: 38995401 DOI: 10.1007/s00261-024-04486-3] [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: 05/05/2024] [Revised: 06/26/2024] [Accepted: 06/30/2024] [Indexed: 07/13/2024]
Abstract
PURPOSE To assess the diagnostic potential of whole-tumor histogram analysis of multiple non-Gaussian diffusion models for differentiating cervical cancer (CC) aggressive status regarding of pathological types, differentiation degree, stage, and p16 expression. METHODS Patients were enrolled in this prospective single-center study from March 2022 to July 2023. Diffusion-weighted images (DWI) were obtained including 15 b-values (0 ~ 4000 s/mm2). Diffusion parameters derived from four non-Gaussian diffusion models including continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM) were calculated, and their histogram features were analyzed. To select the most significant features and establish predictive models, univariate analysis and multivariate logistic regression were performed. Finally, we evaluated the diagnostic performance of our models by using receiver operating characteristic (ROC) analyses. RESULTS 89 women (mean age, 55 ± 11 years) with CC were enrolled in our study. The combined model, which incorporated the CTRW, DKI, FROC, and IVIM diffusion models, offered a significantly higher AUC than that from any individual models (0.836 vs. 0.664, 0.642, 0.651, 0.649, respectively; p < 0.05) in distinguishing cervical squamous cell cancer from cervical adenocarcinoma. To distinguish tumor differentiation degree, except the combined model showed a better predictive performance compared to the DKI model (AUC, 0.839 vs. 0.697, respectively; p < 0.05), no significant differences in AUCs were found among other individual models and combined model. To predict the International Federation of Gynecology and Obstetrics (FIGO) stage, only DKI and FROC model were established and there was no significant difference in predictive performance among different models. In terms of predicting p16 expression, the predictive ability of DKI model is significantly lower than that of FROC and combined model (AUC, 0.693 vs. 0.850, 0.859, respectively; p < 0.05). CONCLUSION Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.
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Affiliation(s)
- Lu Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
| | - Huijun Hu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
| | - Xiaojun Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
| | - Zhuoheng Yan
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
| | - Guangzi Shi
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, Guangdong, China
| | - Lingjie Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
| | - Yu Wang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
| | - Riyu Han
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
| | - Xu Yan
- MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China
| | - Mengzhu Wang
- MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China
| | - Xiaohua Ban
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, China.
| | - Xiaohui Duan
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, Guangdong, China.
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Fan Z, Guo J, Zhang X, Chen Z, Wang B, Jiang Y, Li Y, Wang Y, Yang G, Wang X. Non-Gaussian diffusion metrics with whole-tumor histogram analysis for bladder cancer diagnosis: muscle invasion and histological grade. Insights Imaging 2024; 15:138. [PMID: 38853200 PMCID: PMC11162990 DOI: 10.1186/s13244-024-01701-z] [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: 02/16/2024] [Accepted: 04/13/2024] [Indexed: 06/11/2024] Open
Abstract
PURPOSE To investigate the performance of histogram features of non-Gaussian diffusion metrics for diagnosing muscle invasion and histological grade in bladder cancer (BCa). METHODS Patients were prospectively allocated to MR scanner1 (training cohort) or MR2 (testing cohort) for conventional diffusion-weighted imaging (DWIconv) and multi-b-value DWI. Metrics of continuous time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), intravoxel incoherent motion (IVIM), and stretched exponential model (SEM) were simultaneously calculated using multi-b-value DWI. Whole-tumor histogram features were extracted from DWIconv and non-Gaussian diffusion metrics for logistic regression analysis to develop diffusion models diagnosing muscle invasion and histological grade. The models' performances were quantified by area under the receiver operating characteristic curve (AUC). RESULTS MR1 included 267 pathologically-confirmed BCa patients (median age, 67 years [IQR, 46-82], 222 men) and MR2 included 83 (median age, 65 years [IQR, 31-82], 73 men). For discriminating muscle invasion, CTRW achieved the highest testing AUC of 0.915, higher than DWIconv's 0.805 (p = 0.014), and similar to the combined diffusion model's AUC of 0.885 (p = 0.076). For differentiating histological grade of non-muscle-invasion bladder cancer, IVIM outperformed a testing AUC of 0.897, higher than DWIconv's 0.694 (p = 0.020), and similar to the combined diffusion model's AUC of 0.917 (p = 0.650). In both tasks, DKI, FROC, and SEM failed to show diagnostic superiority over DWIconv (p > 0.05). CONCLUSION CTRW and IVIM are two potential non-Gaussian diffusion models to improve the MRI application in assessing muscle invasion and histological grade of BCa, respectively. CRITICAL RELEVANCE STATEMENT Our study validates non-Gaussian diffusion imaging as a reliable, non-invasive technique for early assessment of muscle invasion and histological grade in BCa, enhancing accuracy in diagnosis and improving MRI application in BCa diagnostic procedures. KEY POINTS Muscular invasion largely determines bladder salvageability in bladder cancer patients. Evaluated non-Gaussian diffusion metrics surpassed DWIconv in BCa muscle invasion and histological grade diagnosis. Non-Gaussian diffusion imaging improved MRI application in preoperative diagnosis of BCa.
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Affiliation(s)
- Zhichang Fan
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Junting Guo
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaoyue Zhang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Zeke Chen
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Bin Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yueluan Jiang
- Department of MR Research Collaboration, Siemens Healthineers, Beijing, China
| | - Yan Li
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yongfang Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Guoqiang Yang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaochun Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
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Tang C, Li F, He L, Hu Q, Qin Y, Yan X, Ai T. Comparison of continuous-time random walk and fractional order calculus models in characterizing breast lesions using histogram analysis. Magn Reson Imaging 2024; 108:47-58. [PMID: 38307375 DOI: 10.1016/j.mri.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/11/2023] [Accepted: 01/22/2024] [Indexed: 02/04/2024]
Abstract
OBJECTIVE To compare the diagnostic performance of different mathematical models for DWI and explore whether parameters reflecting spatial and temporal heterogeneity can demonstrate better diagnostic accuracy than the diffusion coefficient parameter in distinguishing benign and malignant breast lesions, using whole-tumor histogram analysis. METHODS This retrospective study was approved by the institutional ethics committee and included 104 malignant and 42 benign cases. All patients underwent breast magnetic resonance imaging (MRI) with a 3.0 T MR scanner using the simultaneous multi-slice (SMS) readout-segment ed echo-planar imaging (rs-EPI). Histogram metrics of Mono- apparent diffusion coefficient (ADC), CTRW, and FROC-derived parameters were compared between benign and malignant breast lesions, and the diagnostic performance of each diffusion parameter was evaluated. Statistical analysis was performed using Mann-Whitney U test and receiver operating characteristic (ROC) curve. RESULTS The DFROC-median exhibited the highest AUC for distinguishing benign and malignant breast lesions (AUC = 0.965). The temporal heterogeneity parameter αCTRW-median generated a statistically higher AUC compared to the spatial heterogeneity parameter βCTRW-median (AUC = 0.850 and 0.741, respectively; p = 0.047). Finally, the combination of median values of CTRW parameters displayed a slightly higher AUC than that of FROC parameters, with no significant difference however (AUC = 0.971 and 0.965, respectively; p = 0.172). CONCLUSIONS The diffusion coefficient parameter exhibited superior diagnostic performance in distinguishing breast lesions when compared to the temporal and spatial heterogeneity parameters.
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Affiliation(s)
- Caili Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Feng Li
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei 441021, China
| | - Litong He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Qilan Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yanjin Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xu Yan
- MR Research Collaboration Team, Siemens Healthineers Ltd, 278, Zhouzhu Road, Nanhui, Shanghai 201318, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
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Wu D, Kang L, Li H, Ba R, Cao Z, Liu Q, Tan Y, Zhang Q, Li B, Yuan J. Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging. Neuroimage 2024; 290:120553. [PMID: 38403092 DOI: 10.1016/j.neuroimage.2024.120553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024] Open
Abstract
Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.
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Affiliation(s)
- Dan Wu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Innovation Center for Smart Medical Technologies & Devices, Binjiang Institute of Zhejiang University, Hangzhou, China.
| | - Liyi Kang
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Innovation Center for Smart Medical Technologies & Devices, Binjiang Institute of Zhejiang University, Hangzhou, China
| | - Haotian Li
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Ruicheng Ba
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Zuozhen Cao
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Qian Liu
- United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Yingchao Tan
- United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Qinwei Zhang
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Bo Li
- United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Jianmin Yuan
- United Imaging Healthcare Co., Ltd, Shanghai, China
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Mao C, Hu L, Jiang W, Qiu Y, Yang Z, Liu Y, Wang M, Wang D, Su Y, Lin J, Yan X, Cai Z, Zhang X, Shen J. Discrimination between human epidermal growth factor receptor 2 (HER2)-low-expressing and HER2-overexpressing breast cancers: a comparative study of four MRI diffusion models. Eur Radiol 2024; 34:2546-2559. [PMID: 37672055 DOI: 10.1007/s00330-023-10198-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: 01/05/2023] [Revised: 06/13/2023] [Accepted: 07/08/2023] [Indexed: 09/07/2023]
Abstract
OBJECTIVES To determine the value of conventional DWI, continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in discriminating human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC). METHODS This prospective study included 158 women who underwent DWI, CTRW, FROC, and SEM and were pathologically categorized into the HER2-zero-expressing group (n = 10), HER2-low-expressing group (n = 86), and HER2-overexpressing group (n = 62). Nine diffusion parameters, namely ADC, αCTRW, βCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM of the primary tumor, were derived from four diffusion models. These diffusion metrics and clinicopathologic features were compared between groups. Logistic regression was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the HER2-expressing statuses. Receiver operating characteristic (ROC) curves were used to evaluate their discriminative ability. RESULTS The estrogen receptor (ER) status, progesterone receptor (PR) status, and tumor size differed between HER2-low-expressing and HER2-overexpressing groups (p < 0.001 to p = 0.009). The αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM were significantly lower in HER2-low-expressing BCs than those in HER2-overexpressing BCs (p < 0.001 to p = 0.01). Further multivariable logistic regression analysis showed that the αCTRW was the single best discriminative metric, with an area under the curve (AUC) being higher than that of ADC (0.802 vs. 0.610, p < 0.05); the addition of ER status, PR status, and tumor size to the αCTRW improved the AUC to 0.877. CONCLUSIONS The αCTRW could help discriminate the HER2-low-expressing and HER2-overexpressing BCs. CLINICAL RELEVANCE STATEMENT Human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer (BC) might also benefit from the HER2-targeted therapy. Prediction of HER2-low-expressing BC or HER2-overexpressing BC is crucial for appropriate management. Advanced continuous-time random walk diffusion MRI offers a solution to this clinical issue. KEY POINTS • Human epidermal receptor 2 (HER2)-low-expressing BC had lower αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM values compared with HER2-overexpressing breast cancer. • The αCTRW was the single best diffusion metric (AUC = 0.802) for discrimination between the HER2-low-expressing and HER2-overexpressing breast cancers. • The addition of αCTRW to the clinicopathologic features (estrogen receptor status, progesterone receptor status, and tumor size) further improved the discriminative ability.
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Affiliation(s)
- Chunping Mao
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Lanxin Hu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Wei Jiang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Ya Qiu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yeqing Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Department of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthcare, Guangzhou, Guangdong, China
| | - Dongye Wang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yun Su
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jinru Lin
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, Guangzhou, Guangdong, China
| | - Zhaoxi Cai
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, Guangdong, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
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Borni M, Abdelmouleh S, Tallah M, Blibeche H, Elouni E, Boudawara MZ. Extra-axial desmoplastic/nodular medulloblastoma in adult mimicking cerebellar metastasis: reappraisal of this rare presentation with literature review. Ann Med Surg (Lond) 2024; 86:1124-1130. [PMID: 38333306 PMCID: PMC10849342 DOI: 10.1097/ms9.0000000000001617] [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: 11/02/2023] [Accepted: 12/04/2023] [Indexed: 02/10/2024] Open
Abstract
Introduction and importance Medulloblastomas are the most common malignant intra-axial brain tumour in paediatric patients and represent 35-40% of posterior fossa tumour types in children between 3 and 9 years of age. Medulloblastomas may also be found in adulthood. These tumours are classified into two groups according to its molecular characteristics and histological type. The desmoplastic/nodular subtype is the second common subtype after the classic one. Only three cases of desmoplastic/nodular extra-axial medelloblastoma have been previously reported in the literature originating from to the cerebellopontine angle. Case presentation The authors report a new case of an extra-axial desmoplastic/nodular cerebellar medulloblastoma originating outside the cerebellopontine angle and mimicking a solitary cerebellar metastasis in a 49-year-old female patient who presented for a raised intracranial pressure and cerebellar syndrome. Clinical discussion Medulloblastoma is a malignant embryonal intra-axial tumour of the cerebellum or posterior brain stem that occurs mainly in children. Medulloblastomas may also be found in adulthood. Desmoplastic/nodular medulloblastoma is the second most common type of all medulloblastomas. The intra-axial form is always predominant. Only three cases of extra-axial desmoplastic/nodular medulloblastoma have been reported in the literature. The authors will go through the literature to dissect this rare entity. Conclusion Although considered a common paediatric intra-axial tumour, there are increasing numbers of solitary cases reporting an extra-axial presentation in different locations of the posterior cerebral fossa even in adulthood. These rare and unusual presentations and locations may mislead the correct diagnosis and delay treatment.
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Affiliation(s)
- Mehdi Borni
- Department of Neurosurgery, UHC Habib Bourguiba
| | | | | | | | - Emna Elouni
- Department of Neurosurgery, UHC Habib Bourguiba, Sfax (Tunisia)
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19
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Song M, Wang Q, Feng H, Wang L, Zhang Y, Liu H. Preoperative Grading of Rectal Cancer with Multiple DWI Models, DWI-Derived Biological Markers, and Machine Learning Classifiers. Bioengineering (Basel) 2023; 10:1298. [PMID: 38002422 PMCID: PMC10669695 DOI: 10.3390/bioengineering10111298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/05/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
Background: this study aimed to utilize various diffusion-weighted imaging (DWI) techniques, including mono-exponential DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for the preoperative grading of rectal cancer. Methods: 85 patients with rectal cancer were enrolled in this study. Mann-Whitney U tests or independent Student's t-tests were conducted to identify DWI-derived parameters that exhibited significant differences. Spearman or Pearson correlation tests were performed to assess the relationships among different DWI-derived biological markers. Subsequently, four machine learning classifier-based models were trained using various DWI-derived parameters as input features. Finally, diagnostic performance was evaluated using ROC analysis with 5-fold cross-validation. Results: With the exception of the pseudo-diffusion coefficient (Dp), IVIM-derived and DKI-derived parameters all demonstrated significant differences between low-grade and high-grade rectal cancer. The logistic regression-based machine learning classifier yielded the most favorable diagnostic efficacy (AUC: 0.902, 95% Confidence Interval: 0.754-1.000; Specificity: 0.856; Sensitivity: 0.925; Youden Index: 0.781). Conclusions: utilizing multiple DWI-derived biological markers in conjunction with a strategy employing multiple machine learning classifiers proves valuable for the noninvasive grading of rectal cancer.
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Affiliation(s)
- Mengyu Song
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Qi Wang
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Hui Feng
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Lijia Wang
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
| | - Yunfei Zhang
- Central Research Institute, United Imaging Healthcare, Shanghai 201800, China
| | - Hui Liu
- Department of Radiology, Fourth Hospital of Hebei Medical University, No.12 Jiankang Road, Shijiazhuang 050000, China
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Zhong Z, Ryu K, Mao J, Sun K, Dan G, Vasanawala SS, Zhou XJ. Accelerating High b-Value Diffusion-Weighted MRI Using a Convolutional Recurrent Neural Network (CRNN-DWI). Bioengineering (Basel) 2023; 10:864. [PMID: 37508891 PMCID: PMC10376839 DOI: 10.3390/bioengineering10070864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/05/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
PURPOSE To develop a novel convolutional recurrent neural network (CRNN-DWI) and apply it to reconstruct a highly undersampled (up to six-fold) multi-b-value, multi-direction diffusion-weighted imaging (DWI) dataset. METHODS A deep neural network that combines a convolutional neural network (CNN) and recurrent neural network (RNN) was first developed by using a set of diffusion images as input. The network was then used to reconstruct a DWI dataset consisting of 14 b-values, each with three diffusion directions. For comparison, the dataset was also reconstructed with zero-padding and 3D-CNN. The experiments were performed with undersampling rates (R) of 4 and 6. Standard image quality metrics (SSIM and PSNR) were employed to provide quantitative assessments of the reconstructed image quality. Additionally, an advanced non-Gaussian diffusion model was employed to fit the reconstructed images from the different approaches, thereby generating a set of diffusion parameter maps. These diffusion parameter maps from the different approaches were then compared using SSIM as a metric. RESULTS Both the reconstructed diffusion images and diffusion parameter maps from CRNN-DWI were better than those from zero-padding or 3D-CNN. Specifically, the average SSIM and PSNR of CRNN-DWI were 0.750 ± 0.016 and 28.32 ± 0.69 (R = 4), and 0.675 ± 0.023 and 24.16 ± 0.77 (R = 6), respectively, both of which were substantially higher than those of zero-padding or 3D-CNN reconstructions. The diffusion parameter maps from CRNN-DWI also yielded higher SSIM values for R = 4 (>0.8) and for R = 6 (>0.7) than the other two approaches (for R = 4, <0.7, and for R = 6, <0.65). CONCLUSIONS CRNN-DWI is a viable approach for reconstructing highly undersampled DWI data, providing opportunities to reduce the data acquisition burden.
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Affiliation(s)
- Zheng Zhong
- Departments of Radiology, Stanford University, Stanford, CA 94305, USA
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
| | - Kanghyun Ryu
- Departments of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Jonathan Mao
- Henry M. Gunn High School, Palo Alto, CA 94306, USA
| | - Kaibao Sun
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
| | - Guangyu Dan
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
| | | | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
- Department of Radiology, Neurosurgery and Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60607, USA
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Qi LP, Zhong Z, Sun YS, Li XT, Tang L, Zhou XJ. Optimal selection of b-values for differential diagnosis of mediastinal lymph nodes using diffusion-weighted imaging. Heliyon 2023; 9:e16702. [PMID: 37484276 PMCID: PMC10360569 DOI: 10.1016/j.heliyon.2023.e16702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 04/16/2023] [Accepted: 05/24/2023] [Indexed: 07/25/2023] Open
Abstract
This study proposed to investigate the optimal selection of b-values in diffusion-weighted imaging for distinguishing malignant from benign mediastinal lymph nodes. Diffusion-weighted imaging with six b-values was performed on 35 patients at 1.5 T. Image quality score, signal-to-noise ratio, and relative contrast ratio of lymph node to chest muscle were compared between the diffusion-weighted images with a b-value up to 800 and 1000 s/mm2. Using a lower and an upper b-value in the range of 0-1000 s/mm2, eight apparent diffusion coefficient maps were obtained from a mono-exponential model. Receiver operating characteristic analysis was employed to evaluate the performance of the apparent diffusion coefficients for distinguishing malignant from benign mediastinal lymph nodes by using the area under the curve as a criterion. The mean image quality score and the relative contrast ratio showed no difference between b-values of 800 and 1000 s/mm2. In the receiver operating characteristic analysis, the areas under the curve of apparent diffusion coefficient with b-value pairs of (0, 800), (0, 1000), and (50, 800) s/mm2 were significantly higher than those from the other b-value pairs. No significant difference was observed among the three b-value pairs. Apparent diffusion coefficient obtained from b-value pairs of (0, 800), (0, 1000), and (50, 800) s/mm2 showed superior diagnostic performance compared to the other b-value combinations. Based on several practical considerations, the b-value pair of (50, 800) s/mm2 is recommended for differential diagnosis of mediastinal lymph nodes.
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Affiliation(s)
- Li-Ping Qi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Zheng Zhong
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, USA
- Department of Biomedcial Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China
| | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA
- Department of Biomedcial Engineering, University of Illinois at Chicago, Chicago, IL, USA
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22
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Li C, Wen Y, Xie J, Chen Q, Dang Y, Zhang H, Guo H, Long L. Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study. Front Oncol 2023; 13:1167209. [PMID: 37305565 PMCID: PMC10248416 DOI: 10.3389/fonc.2023.1167209] [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: 02/16/2023] [Accepted: 05/15/2023] [Indexed: 06/13/2023] Open
Abstract
Background Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis. Purpose To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC. Methods 86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance. Results Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively). Conclusion DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.
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Affiliation(s)
- Chenhui Li
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yan Wen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jinhuan Xie
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qianjuan Chen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yiwu Dang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare Ltd., Wuhan, Hubei, China
| | - Hu Guo
- MR Application, Siemens Healthcare Ltd., Changsha, Hunan, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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23
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Mehta R, Bu Y, Zhong Z, Dan G, Zhong PS, Zhou C, Hu W, Zhou XJ, Xu M, Wang S, Karaman MM. Characterization of breast lesions using multi-parametric diffusion MRI and machine learning. Phys Med Biol 2023; 68:085006. [PMID: 36808921 DOI: 10.1088/1361-6560/acbde0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/21/2023] [Indexed: 02/23/2023]
Abstract
Objective. To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm.Approach. With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters,Dm,α, andβand three IVIM parametersDdiff,Dperf, andfwere estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers.Main Results. The 75% quantile, and median ofDm; 75% quantile off;mean, median, and skewness ofβ;kurtosis ofDperf; and 75% quantile ofDdiffwere the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers.Significance. Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.
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Affiliation(s)
- Rahul Mehta
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Yangyang Bu
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Zheng Zhong
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Ping-Shou Zhong
- Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Changyu Zhou
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Weihong Hu
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Xiaohong Joe Zhou
- Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Maosheng Xu
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Shiwei Wang
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - M Muge Karaman
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
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Wang C, Wang G, Zhang Y, Dai Y, Yang D, Wang C, Li J. Differentiation of benign and malignant breast lesions using diffusion-weighted imaging with a fractional-order calculus model. Eur J Radiol 2023; 159:110646. [PMID: 36577184 DOI: 10.1016/j.ejrad.2022.110646] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 11/25/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE To assess the feasibility of using three diffusion parameters (D, β, and μ) derived from fractional-order calculus (FROC) diffusion model for improving the differentiation between benign and malignant breast lesions. METHOD In this prospective study, 103 patients with breast lesions were enrolled. All subjects underwent diffusion-weighted imaging (DWI) with 12b values. Inter-observer agreement with respect to quantification of parameters by two radiologists was assessed using intraclass coefficient. Conventional apparent diffusion coefficient (ADC) and three FROC model parameters D, β, and μ were compared between the benign lesion and malignant lesion groups using the Mann-Whitney U test. Then, a comprehensive prediction model was created by using binary logistic regression. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the parameters using histopathological diagnosis as the reference standard. RESULTS The FROC parameters and ADC all exhibited significant differences between benign lesions and malignant lesions (P＜0.001). Among the individual parameters, the sensitivity of μ was higher than ADC (95.92% for μ vs 91.84% for ADC), and the specificity of β was higher than ADC (72.22% for β vs 70.37% for ADC). The combination of ADC and FROC parameters (D and β) generated the largest area under the ROC curve (0.841) when compared with individual parameters, indicating an improved performance for differentiating benign lesions from malignant lesions. CONCLUSIONS This study demonstrated the feasibility of using the FROC diffusion model to improve the accuracy of identifying malignant breast lesions.
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Affiliation(s)
- Chunhong Wang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
| | - Guanying Wang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
| | - Yunfei Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Yongming Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Dan Yang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
| | - Changfu Wang
- Imaging department, Huaihe Hospital, Henan University, Kaifeng, 475000, Henan, China
| | - Jianhong Li
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China.
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Spennato P, De Martino L, Russo C, Errico ME, Imperato A, Mazio F, Miccoli G, Quaglietta L, Abate M, Covelli E, Donofrio V, Cinalli G. Tumors of Choroid Plexus and Other Ventricular Tumors. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1405:175-223. [PMID: 37452939 DOI: 10.1007/978-3-031-23705-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Tumors arising inside the ventricular system are rare but represent a difficult diagnostic and therapeutic challenge. They usually are diagnosed when reaching a big volume and tend to affect young children. There is a wide broad of differential diagnoses with significant variability in anatomical aspects and tumor type. Differential diagnosis in tumor type includes choroid plexus tumors (papillomas and carcinomas), ependymomas, subependymomas, subependymal giant cell astrocytomas (SEGAs), central neurocytomas, meningiomas, and metastases. Choroid plexus tumors, ependymomas of the posterior fossa, and SEGAs are more likely to appear in childhood, whereas subependymomas, central neurocytomas, intraventricular meningiomas, and metastases are more frequent in adults. This chapter is predominantly focused on choroid plexus tumors and radiological and histological differential diagnosis. Treatment is discussed in the light of the modern acquisition in genetics and epigenetics of brain tumors.
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Affiliation(s)
- Pietro Spennato
- Department of Pediatric Neurosurgery, Santobono-Pausilipon Children's Hospital, Via Mario Fiore 6, 80121, Naples, Italy.
| | - Lucia De Martino
- Department of Pediatric Oncology, Santobono-Pausilipon Pediatric Hospital, Naples, Italy
| | - Carmela Russo
- Department of Neuroradiology, Santobono-Pausilipon Pediatric Hospital, Naples, Italy
| | - Maria Elena Errico
- Department of Pathology, Santobono-Pausilipon Pediatric Hospital, Naples, Italy
| | - Alessia Imperato
- Department of Pediatric Neurosurgery, Santobono-Pausilipon Children's Hospital, Via Mario Fiore 6, 80121, Naples, Italy
| | - Federica Mazio
- Department of Neuroradiology, Santobono-Pausilipon Pediatric Hospital, Naples, Italy
| | - Giovanni Miccoli
- Department of Pediatric Neurosurgery, Santobono-Pausilipon Children's Hospital, Via Mario Fiore 6, 80121, Naples, Italy
| | - Lucia Quaglietta
- Department of Pediatric Oncology, Santobono-Pausilipon Pediatric Hospital, Naples, Italy
| | - Massimo Abate
- Department of Pediatric Oncology, Santobono-Pausilipon Pediatric Hospital, Naples, Italy
| | - Eugenio Covelli
- Department of Neuroradiology, Santobono-Pausilipon Pediatric Hospital, Naples, Italy
| | - Vittoria Donofrio
- Department of Pathology, Santobono-Pausilipon Pediatric Hospital, Naples, Italy
| | - Giuseppe Cinalli
- Department of Pediatric Neurosurgery, Santobono-Pausilipon Children's Hospital, Via Mario Fiore 6, 80121, Naples, Italy
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Preoperative assessment of microvascular invasion of hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging with a fractional order calculus model: A pilot study. Magn Reson Imaging 2023; 95:110-117. [PMID: 34506910 DOI: 10.1016/j.mri.2021.09.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 09/05/2021] [Accepted: 09/05/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE To assess the clinical potential of a set of new diffusion parameters (D, β, and μ) derived from fractional order calculus (FROC) diffusion model in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC). MATERIALS AND METHODS Between January 2019 to November 2020, a total of 63 patients with HCC were enrolled in this study. Diffusion-weighted images were acquired by using ten b-values (0-2000 s/mm2). The FROC model parameters including diffusion coefficient (D), fractional order parameter (β), a microstructural quantity (μ) together with a conventional apparent diffusion coefficient (ADC) were calculated. Intraclass coefficients were calculated for assessing the agreement of parameters quantified by two radiologists. The differences of these values between the MVI-positive and MVI-negative HCC groups were compared by using independent sample t-test or the Mann-Whitney U test. Then the parameters showing significant differences between subgroups, including the β and D, were integrated to develop a comprehensive predictive model via binary logistic regression. The diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS Among all the studied diffusion parameters, significant differences were found in D, β, and ADC between the MVI-positive and MVI-negative groups. MVI-positive HCCs showed significantly higher β values (0.65 ± 0.17 vs. 0.51 ± 0.13, P = 0.001), along with lower D values (0.84 ± 0.11 μm2/ms vs. 1.03 ± 0.13 μm2/ms, P < 0.001) and lower ADC values (1.38 ± 0.46 μm2/ms vs. 2.09 ± 0.70 μm2/ms, P < 0.001) than those of MVI-negative HCCs. According to the ROC analysis, the combination of D and β demonstrated the largest area under the ROC curve (0.920) compared with individual parameters (D: 0.912; β: 0.733; and ADC: 0.831) for differentiating MVI-positive from MVI-negative HCCs. CONCLUSIONS The FROC parameters can be used as noninvasive quantitative imaging markers for preoperatively predicting the MVI status of HCCs.
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Xu J, Ren Y, Zhao X, Wang X, Yu X, Yao Z, Zhou Y, Feng X, Zhou XJ, Wang H. Incorporating multiple magnetic resonance diffusion models to differentiate low- and high-grade adult gliomas: a machine learning approach. Quant Imaging Med Surg 2022; 12:5171-5183. [PMID: 36330178 PMCID: PMC9622457 DOI: 10.21037/qims-22-145] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 08/07/2022] [Indexed: 08/13/2023]
Abstract
BACKGROUND Accurate grading of gliomas is a challenge in imaging diagnosis. This study aimed to evaluate the performance of a machine learning (ML) approach based on multiparametric diffusion-weighted imaging (DWI) in differentiating low- and high-grade adult gliomas. METHODS A model was developed from an initial cohort containing 74 patients with pathology-confirmed gliomas, who underwent 3 tesla (3T) diffusion magnetic resonance imaging (MRI) with 21 b values. In all, 112 histogram features were extracted from 16 parameters derived from seven diffusion models [monoexponential, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), continuous-time random walk (CTRW), stretched-exponential, and statistical]. Feature selection and model training were performed using five randomly permuted five-fold cross-validations. An internal test set (15 cases of the primary dataset) and an external cohort (n=55) imaged on a different scanner were used to validate the model. The diagnostic performance of the model was compared with that of a single DWI model and DWI radiomics using accuracy, sensitivity, specificity, and the area under the curve (AUC). RESULTS Seven significant multiparametric DWI features (two from the stretched-exponential and FROC models, and three from the CTRW model) were selected to construct the model. The multiparametric DWI model achieved the highest AUC (0.84, versus 0.71 for the single DWI model, P<0.05), an accuracy of 0.80 in the internal test, and both AUC and accuracy of 0.76 in the external test. CONCLUSIONS Our multiparametric DWI model differentiated low- (LGG) from high-grade glioma (HGG) with better generalization performance than the established single DWI model. This result suggests that the application of an ML approach with multiple DWI models is feasible for the preoperative grading of gliomas.
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Affiliation(s)
- Junqi Xu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yan Ren
- Radiology Department, Hua Shan Hospital, Fudan University, Shanghai, China
| | - Xueying Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiaoqing Wang
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuchen Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zhenwei Yao
- Radiology Department, Hua Shan Hospital, Fudan University, Shanghai, China
| | - Yan Zhou
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoyuan Feng
- Radiology Department, Hua Shan Hospital, Fudan University, Shanghai, China
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
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Li W, Gordon AC, Mouli SK. Editorial for "Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous-Time Random-Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis". J Magn Reson Imaging 2022. [PMID: 36205703 DOI: 10.1002/jmri.28476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 11/08/2022] Open
Affiliation(s)
- Weiguo Li
- Department of Radiology, Northwestern University, Chicago, Illinois, USA.,Department of Biomedical engineering, University of Illinois at Chicago, Chicago, Illinois, USA.,Research Resource Center, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Andrew C Gordon
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - Samdeep K Mouli
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
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Zhang A, Hu Q, Song J, Dai Y, Wu D, Chen T. Value of non-Gaussian diffusion imaging with a fractional order calculus model combined with conventional MRI for differentiating histological types of cervical cancer. Magn Reson Imaging 2022; 93:181-188. [PMID: 35988835 DOI: 10.1016/j.mri.2022.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE This study aimed to evaluate the value of a fractional order calculus (FROC) model combined with conventional magnetic resonance imaging (MRI) for differentiating cervical adenocarcinoma (CAC) from squamous cell carcinoma (SCC). METHODS Diffusion-weighted imaging (DWI) with 9 b values (0-2000s/mm2) was carried out in 57 cervical cancer patients. Diffusion coefficient (D), fractional order parameter (β), and microstructural quantity (μ) together with apparent diffusion coefficient (ADC) were calculated and compared between the CAC and SCC groups. Conventional MRI features included T2WI signal intensity (SI), unenhanced-T1WI SI, enhanced-T1WI SI, and ∆T1WI SI, which were also compared between the two groups. Receiver operating characteristic (ROC) analysis was employed to assess the performance of FROC parameters, ADC, and conventional MRI features in differentiating CAC from SCC. RESULTS β was significantly lower in the CAC group than in the SCC group (0.682 ± 0.054 vs. 0.723 ± 0.084, P = 0.035), while D and μ were not significantly different between the two groups (D, P = 0.171; μ, P = 0.127). There was no significant difference in the ADC value between the two groups (P = 0.053). In conventional MRI features, enhanced-T1WI SI was significantly higher in the SCC group than in the CAC group (985.78 ± 130.83 vs. 853.92 ± 149.65, P = 0.002). The area under the curve (AUC) of β, ADC, and enhanced-T1WI SI was 0.700, 0.683, and 0.799, respectively. The combination of β, ADC, and enhanced-T1WI SI revealed optimal diagnostic performance in differentiating CAC from SCC (AUC = 0.930), followed by β + enhanced-T1WI SI (AUC = 0.869), ADC+ enhanced-T1WI SI (AUC = 0.817), and β + ADC (AUC = 0.761). CONCLUSION The FROC model can serve as a noninvasive and quantitative imaging technique for differentiating CAC from SCC. β combined with ADC and enhanced-T1WI SI had the highest diagnostic efficiency.
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Affiliation(s)
- Aining Zhang
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qiming Hu
- Department of Obstetrics & Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiacheng Song
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yongming Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, China.
| | - Ting Chen
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Luo Y, Jiang H, Meng N, Huang Z, Li Z, Feng P, Fang T, Fu F, Yuan J, Wang Z, Yang Y, Wang M. A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types. Front Oncol 2022; 12:907860. [PMID: 35936757 PMCID: PMC9351313 DOI: 10.3389/fonc.2022.907860] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/28/2022] [Indexed: 11/24/2022] Open
Abstract
Objective To evaluate the application value of monoexponential, fractional order calculus (FROC) diffusion models and PET imaging to distinguish between benign and malignant solitary pulmonary lesions (SPLs) and malignant SPLs with different pathological types and explore the correlation between each parameter and Ki67 expression. Methods A total of 112 patients were enrolled in this study. Prior to treatment, all patients underwent a dedicated thoracic 18F-FDG PET/MR examination. Five parameters [including apparent diffusion coefficient (ADC) derived from the monoexponential model; diffusion coefficient (D), a microstructural quantity (μ), and fractional order parameter (β) derived from the FROC model and maximum standardized uptake value (SUVmax) derived from PET] were compared between benign and malignant SPLs and different pathological types of malignant SPLs. Independent sample t test, Mann-Whitney U test, DeLong test and receiver operating characteristic (ROC) curve analysis were used for statistical evaluation. Pearson correlation analysis was used to calculate the correlations between Ki-67 and ADC, D, μ, β, and SUVmax. Results The ADC and D values were significantly higher and the μ and SUVmax values were significantly lower in the benign group [1.57 (1.37, 2.05) μm2/ms, 1.59 (1.52, 1.72) μm2/ms, 5.06 (3.76, 5.66) μm, 5.15 ± 2.60] than in the malignant group [1.32 (1.03, 1.51) μm2/ms, 1.43 (1.29, 1.52) μm2/ms, 7.06 (5.87, 9.45) μm, 9.85 ± 4.95]. The ADC, D and β values were significantly lower and the μ and SUVmax values were significantly higher in the squamous cell carcinoma (SCC) group [1.29 (0.66, 1.42) μm2/ms, 1.32 (1.02, 1.42) μm2/ms, 0.63 ± 0.10, 9.40 (7.76, 15.38) μm, 11.70 ± 5.98] than in the adenocarcinoma (AC) group [1.40 (1.28, 1.67) μm2/ms, 1.52 (1.44, 1.64) μm2/ms, 0.70 ± 0.10, 5.99 (4.54, 6.87) μm, 8.76 ± 4.18]. ROC curve analysis showed that for a single parameter, μ exhibited the best AUC value in discriminating between benign and malignant SPLs groups and AC and SCC groups (AUC = 0.824 and 0.911, respectively). Importantly, the combination of monoexponential, FROC models and PET imaging can further improve diagnostic performance (AUC = 0.872 and 0.922, respectively). The Pearson correlation analysis showed that Ki67 was positively correlated with μ value and negatively correlated with ADC and D values (r = 0.402, -0.346, -0.450, respectively). Conclusion The parameters D and μ derived from the FROC model were superior to ADC and SUVmax in distinguishing benign from malignant SPLs and adenocarcinoma from squamous cell carcinoma, in addition, the combination of multiple parameters can further improve diagnostic performance. The non-Gaussian FROC diffusion model is expected to become a noninvasive quantitative imaging technique for identifying SPLs.
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Affiliation(s)
- Yu Luo
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Han Jiang
- Department of Medical Imaging, Xinxiang Medical University & Henan Provincial People’s Hospital, Xinxiang, Henan, China
| | - Nan Meng
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhun Huang
- Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Ziqiang Li
- Department of Medical Imaging, Xinxiang Medical University & Henan Provincial People’s Hospital, Xinxiang, Henan, China
| | - Pengyang Feng
- Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Ting Fang
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Fangfang Fu
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
- *Correspondence: Meiyun Wang,
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Sheng R, Zhang Y, Sun W, Ji Y, Zeng M, Yao X, Dai Y. Staging Chronic Hepatitis B Related Liver Fibrosis with a Fractional Order Calculus Diffusion Model. Acad Radiol 2022; 29:951-963. [PMID: 34429260 DOI: 10.1016/j.acra.2021.07.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/06/2021] [Accepted: 07/07/2021] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES Accurately staging liver fibrosis is of great clinical significance. We aimed to evaluate the clinical potential of the non-Gaussian fractional order calculus (FROC) diffusion model in staging liver fibrosis. MATERIALS AND METHODS A total of 82 patients with chronic hepatitis B (CHB) were included in this prospective study. Diffusion weighted imaging (DWI)-derived parameters including the diffusion coefficient (D), fractional order parameter (β) and microstructural quantity (μ) sourced from FROC-DWI, and apparent diffusion coefficient (ADC) derived from mono-exponential DWI, as well as the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) were calculated. Their correlations with fibrosis stages and the diagnostic efficacy in predicting liver fibrosis were assessed and compared. RESULTS D (r = -0.667), β (r = -0.671), μ (r = -0.481), and ADC (r = -0.665) displayed significant correlations with fibrosis stages (p < 0.001). D, β and ADC (p < 0.01) were independently associated with fibrosis; and compared to inflammatory activity, fibrosis was the independent factor significantly correlated with D, β and ADC (p < 0.001). There were no significant differences between the area under curves of D, β, μ or their combinations and ADC for predicting different fibrosis stages (p > 0.05). The diagnostic performance of the combined index with four diffusion metrics was better than D, β, μ or ADC used alone (p < 0.05) as well as APRI or FIB-4 (p < 0.01) in fibrosis staging. CONCLUSION FROC-DWI was valuable in staging liver fibrosis in patients with CHB, but there were no significant differences between the FROC-DWI parameters and the classical ADC. However, the combined DWI-derived index including D, β, μ and ADC offered the best diagnostic efficacy and may serve as a reliable tool for fibrosis evaluation, superior to APRI and FIB-4.
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Affiliation(s)
- Ruofan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, China
| | - Yunfei Zhang
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, China
| | - Yuan Ji
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, China.
| | - Xiuzhong Yao
- Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, Shanghai, China
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Li W. Non-Gaussian Diffusion MRI for Evaluating Hepatic Fibrosis. Acad Radiol 2022; 29:964-966. [PMID: 35597754 DOI: 10.1016/j.acra.2022.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 04/16/2022] [Accepted: 04/21/2022] [Indexed: 11/01/2022]
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Guo Y, Chen J, Zhang Y, Guo Y, Jiang M, Dai Y, Yao X. Differentiating Cytokeratin 19 expression of hepatocellular carcinoma by using multi-b-value diffusion-weighted MR imaging with mono-exponential, stretched exponential, intravoxel incoherent motion, diffusion kurtosis imaging and fractional order calculus models. Eur J Radiol 2022; 150:110237. [DOI: 10.1016/j.ejrad.2022.110237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 02/02/2022] [Accepted: 03/03/2022] [Indexed: 12/25/2022]
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Shao X, An L, Liu H, Feng H, Zheng L, Dai Y, Yu B, Zhang J. Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings. Front Oncol 2022; 12:851677. [PMID: 35480091 PMCID: PMC9036957 DOI: 10.3389/fonc.2022.851677] [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: 01/10/2022] [Accepted: 03/03/2022] [Indexed: 11/13/2022] Open
Abstract
Objective The objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma. Methods Sixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann–Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. P-value <0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons. Results Only the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes (P = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, D, and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones (P = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829). Conclusion The parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma.
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Affiliation(s)
- Xian Shao
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China
| | - Li An
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China
| | - Hui Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Hui Feng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Liyun Zheng
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Yongming Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Bin Yu
- Department of Emergency, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jin Zhang
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China
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Yang Q, Reutens DC, Vegh V. Generalisation of continuous time random walk to anomalous diffusion MRI models with an age-related evaluation of human corpus callosum. Neuroimage 2022; 250:118903. [PMID: 35033674 DOI: 10.1016/j.neuroimage.2022.118903] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/07/2021] [Accepted: 01/10/2022] [Indexed: 12/22/2022] Open
Abstract
Diffusion MRI measures of the human brain provide key insight into microstructural variations across individuals and into the impact of central nervous system diseases and disorders. One approach to extract information from diffusion signals has been to use biologically relevant analytical models to link millimetre scale diffusion MRI measures with microscale influences. The other approach has been to represent diffusion as an anomalous transport process and infer microstructural information from the different anomalous diffusion equation parameters. In this study, we investigated how parameters of various anomalous diffusion models vary with age in the human brain white matter, particularly focusing on the corpus callosum. We first unified several established anomalous diffusion models (the super-diffusion, sub-diffusion, quasi-diffusion and fractional Bloch-Torrey models) under the continuous time random walk modelling framework. This unification allows a consistent parameter fitting strategy to be applied from which meaningful model parameter comparisons can be made. We then provided a novel way to derive the diffusional kurtosis imaging (DKI) model, which is shown to be a degree two approximation of the sub-diffusion model. This link between the DKI and sub-diffusion models led to a new robust technique for generating maps of kurtosis and diffusivity using the sub-diffusion parameters βSUB and DSUB. Superior tissue contrast is achieved in kurtosis maps based on the sub-diffusion model. 7T diffusion weighted MRI data for 65 healthy participants in the age range 19-78 years was used in this study. Results revealed that anomalous diffusion model parameters α and β have shown consistent positive correlation with age in the corpus callosum, indicating α and β are sensitive to tissue microstructural changes in ageing.
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Affiliation(s)
- Qianqian Yang
- School of Mathematical Sciences, Faculty of Science, Queensland University of Technology, Brisbane 4000, Australia.
| | - David C Reutens
- Centre for Advanced Imaging, University of Queensland, Brisbane 4072, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, Brisbane 4072, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, University of Queensland, Brisbane 4072, Australia; ARC Training Centre for Innovation in Biomedical Imaging Technology, Brisbane 4072, Australia
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Shi B, Xue K, Yin Y, Xu Q, Shi B, Wu D, Ye J. Grading of clear cell renal cell carcinoma using diffusion MRI with a fractional order calculus model. Acta Radiol 2022; 64:421-430. [PMID: 35040361 DOI: 10.1177/02841851211072482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND The fractional order calculus (FROC) model has been developed to describe restrained motion of water molecules as well as microstructural heterogeneity, providing a novel tool for non-invasive tumor grading. PURPOSE To evaluate the role of the FROC model in characterizing clear cell renal cell carcinoma (ccRCC) grades. MATERIAL AND METHODS A total of 59 patients diagnosed with ccRCC were included in this prospective study. The diffusion metrics derived from the mono-exponential model (apparent diffusion coefficient [ADC]), intra-voxel incoherent motion [IVIM] model [D, D*, f], and FROC model [Dfroc, β, μ]) were calculated and compared between low- and high-grade ccRCCs. Binary logistic regression analysis was performed to establish the diagnostic models. Receiver operating characteristic (ROC) analysis and DeLong test were performed to evaluate and compare the diagnostic performance of metrics in grading ccRCC. RESULTS All the metrics except D* and f exhibited statistical differences between low- and high-grade ccRCCs. ROC analysis showed individual FROC parameters, μ, Dfroc, and β, outperformed ADC and IVIM parameters in grading ccRCC. For single parameter, μ demonstrated the highest AUC value, sensitivity, and diagnostic accuracy in discriminating the two ccRCC groups while β exhibited the optimal specificity. Importantly, the combination of Dfroc, μ, and β could further improve the diagnostic performance. CONCLUSION The FROC parameters were superior to ADC and IVIM parameters in grading ccRCC, indicating the great potential of the FROC model in distinguishing low- and high-grade ccRCCs.
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Affiliation(s)
- Bowen Shi
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Ke Xue
- Central Research Institute, United Imaging Healthcare, Shanghai, PR China
| | - Yili Yin
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Qing Xu
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Binbin Shi
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, PR China
| | - Jing Ye
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
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The relationship between diffusion heterogeneity and microstructural changes in high-grade gliomas using Monte Carlo simulations. Magn Reson Imaging 2021; 85:108-120. [PMID: 34653578 DOI: 10.1016/j.mri.2021.10.001] [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: 06/24/2021] [Revised: 09/17/2021] [Accepted: 10/07/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE Diffusion-weighted imaging (DWI) may aid accurate tumor grading. Decreased diffusivity and increased diffusion heterogeneity measures have been observed in high-grade gliomas using the non-monoexponential models for DWI. However, DWI measures concerning tissue characteristics in terms of pathophysiological and structural changes are yet to be established. Thus, this study aims to investigate the relationship between the diffusion measurements and microstructural changes in the presence of high-grade gliomas using a three-dimensional Monte Carlo simulation with systematic changes of microstructural parameters. METHODS Water diffusion was simulated in a microenvironment along with changes associated with the presence of high-grade gliomas, including increases in cell density, nuclear volume, extracellular volume (VFex), and extracellular tortuosity (λex), and changes in membrane permeability (Pmem). DWI signals were simulated using a pulsed gradient spin-echo sequence. The sequence parameters, including the maximum gradient strength and diffusion time, were set to be comparable to those of clinical scanners and advanced human MRI systems. The DWI signals were fitted using the gamma distribution and diffusional kurtosis models with b-values up to 6000 and 2500 s/mm2, respectively. RESULTS The diffusivity measures (apparent diffusion coefficients (ADC), Dgamma of the gamma distribution model and Dapp of the diffusional kurtosis model) decreased with increases in cell density and λex, and a decrease in Pmem. These diffusivity measures increased with increases in nuclear volume and VFex. The diffusion heterogeneity measures (σgamma of the gamma distribution model and Kapp of the diffusional kurtosis model) increased with increases in cell density or nuclear volume at the low Pmem, and a decrease in Pmem. Increased σgamma was also associated with an increase in VFex. CONCLUSION Among simulated microstructural changes, only increases in cell density at low Pmem or decreases in Pmem corresponded to both the decreased diffusivity and increased diffusion heterogeneity measures. The results suggest that increases in cell density at low Pmem or decreases in Pmem may be associated with the diffusion changes observed in high-grade gliomas.
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Predicting the aggressiveness of peripheral zone prostate cancer using a fractional order calculus diffusion model. Eur J Radiol 2021; 143:109913. [PMID: 34464907 DOI: 10.1016/j.ejrad.2021.109913] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/01/2021] [Accepted: 08/12/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE To evaluate the performance of parameters D, β, μ from the Fractional Order Calculus (FROC) model at differentiating peripheral zone (PZ) prostate cancer (PCa) MATERIAL AND METHODS: 75 patients who underwent targeted MRI-guided TRUS prostate biopsy within 6 months of MRI were reviewed retrospectively. Regions of interest (ROI) were placed on suspicious lesions on MRI scans. ROIs were then correlated to pathological results based on core biopsy location. The final tumor count is a total: 23 of GS 6 (3 + 3), 36 of GS 7 (3 + 4), 18 of GS 7 (4 + 3), and 19 of GS ≥ 8. Diffusion-weighted imaging (DWI) scans were fitted into the FROC and monoexponential model to calculate ADC and FROC parameters: anomalous diffusion coefficient D, intravoxel diffusion heterogeneity β, and spatial parameter μ. The performance of FROC parameters and ADC at differentiating PCa grade was evaluated with receiver operating characteristic (ROC) analysis. RESULTS In differentiating low (GS 6) vs. intermediate (GS 7) risk PZ PCa, combination of (D, β) provides the best performance with AUC of 0.829 with significance of p = 0.018 when compared to ADC (AUC of 0.655). In differentiating clinically significant (GS 6) vs. clinically significant (GS ≥ 7) PCa, combination of (D, β, μ) provides highest AUC of 0.802 when compared to ADC (AUC of 0.671) with significance of p = 0.038. Stratification of intermediate (GS 7) and high (GS ≥ 8) risk PCa with FROC did not reach a significant difference when compared to ADC. CONCLUSION Combination of FROC parameters shows greater performance than ADC at differentiating low vs. intermediate risk and clinically insignificant vs. significant prostate cancers in peripheral zone lesions. The FROC diffusion model holds promise as a quantitative imaging technique for non-invasive evaluation of PZ PCa.
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Feng C, Wang Y, Dan G, Zhong Z, Karaman MM, Li Z, Hu D, Zhou XJ. Evaluation of a fractional-order calculus diffusion model and bi-parametric VI-RADS for staging and grading bladder urothelial carcinoma. Eur Radiol 2021; 32:890-900. [PMID: 34342693 DOI: 10.1007/s00330-021-08203-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/30/2021] [Accepted: 06/30/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To evaluate the feasibility of high b-value diffusion-weighted imaging (DWI) for distinguishing non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC) and low- from high-grade bladder urothelial carcinoma using a fractional-order calculus (FROC) model as well as a combination of FROC DWI and bi-parametric Vesical Imaging-Reporting and Data System (VI-RADS). METHODS Fifty-eight participants with bladder urothelial carcinoma were included in this IRB-approved prospective study. Diffusion-weighted images, acquired with 16 b-values (0-3600 s/mm2), were analyzed using the FROC model. Three FROC parameters, D, β, and μ, were used for delineating NMIBC from MIBC and for tumor grading. A receiver operating characteristic (ROC) analysis was performed based on the individual FROC parameters and their combinations, followed by comparisons with apparent diffusion coefficient (ADC) and bi-parametric VI-RADS based on T2-weighted images and DWI. RESULTS D and μ were significantly lower in the MIBC group than in the NMIBC group (p = 0.001 for each), and D, β, and μ all exhibited significantly lower values in the high- than in the low-grade tumors (p ≤ 0.011). The combination of D, β, and μ produced the highest specificity (85%), accuracy (78%), and the area under the ROC curve (AUC, 0.782) for distinguishing NMIBC and MIBC, and the best sensitivity (89%), specificity (86%), accuracy (88%), and AUC (0.892) for tumor grading, all of which outperformed the ADC. The combination of FROC parameters with bi-parametric VI-RADS improved the AUC from 0.859 to 0.931. CONCLUSIONS High b-value DWI with a FROC model is useful in distinguishing NMIBC from MIBC and grading bladder tumors. KEY POINTS • Diffusion parameters derived from a FROC diffusion model may differentiate NMIBC from MIBC and low- from high-grade bladder urothelial carcinomas. • Under the condition of a moderate sample size, higher AUCs were achieved by the FROC parameters D (0.842) and μ (0.857) than ADC (0.804) for bladder tumor grading with p ≤ 0.046. • The combination of the three diffusion parameters from the FROC model can improve the specificity over ADC (85% versus 67%, p = 0.031) for distinguishing NMIBC and MIBC and enhance the performance of bi-parametric VI-RADS.
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Affiliation(s)
- Cui Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, China.,Center for MR Research, University of Illinois at Chicago, MC-707, Suite 1A, 1801 West Taylor Street, Chicago, IL, 60612, USA
| | - Yanchun Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, China
| | - Guangyu Dan
- Center for MR Research, University of Illinois at Chicago, MC-707, Suite 1A, 1801 West Taylor Street, Chicago, IL, 60612, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Zheng Zhong
- Center for MR Research, University of Illinois at Chicago, MC-707, Suite 1A, 1801 West Taylor Street, Chicago, IL, 60612, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - M Muge Karaman
- Center for MR Research, University of Illinois at Chicago, MC-707, Suite 1A, 1801 West Taylor Street, Chicago, IL, 60612, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, China.
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, MC-707, Suite 1A, 1801 West Taylor Street, Chicago, IL, 60612, USA. .,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA. .,Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA. .,Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA.
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Li Y, Kim MM, Wahl DR, Lawrence TS, Parmar H, Cao Y. Survival Prediction Analysis in Glioblastoma With Diffusion Kurtosis Imaging. Front Oncol 2021; 11:690036. [PMID: 34336676 PMCID: PMC8316991 DOI: 10.3389/fonc.2021.690036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/24/2021] [Indexed: 11/13/2022] Open
Abstract
SIMPLE SUMMARY Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Diffusion kurtosis imaging (DKI) has characterized non-Gaussian diffusion behaviors in brain normal tissue and gliomas, but there are very limited efforts in investigating treatment responses of kurtosis in GBM. This study aimed to investigate whether any parameter derived from the DKI is a significant predictor of overall survival (OS). We found that the large mean, 80 and 90 percentile kurtosis values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1-weighted images pre-RT were significantly associated with reduced OS. In the multivariate Cox model, the mean kurtosis Gd-GTV pre-RT after considering effects of age, extent of surgery, and methylation were significant predictors of OS. In addition, the 80 and 90 percentile kurtosis values in Gd-GTV post RT were significantly associated with progression free survival (PFS). The DKI model demonstrates the potential to predict outcomes in the patients with GBM. PURPOSE Non-Gaussian diffusion behaviors in gliomas have been characterized by diffusion kurtosis imaging (DKI). But there are very limited efforts in investigating the kurtosis in glioblastoma (GBM) and its prognostic and predictive values. This study aimed to investigate whether any of the diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival. METHODS AND MATERIALS Thirty-three patients with GBM had pre-radiation therapy (RT) and mid-RT diffusion weighted (DW) images. Kurtosis and diffusion coefficient (DC) values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1 weighted images pre-RT and mid-RT were calculated. Univariate and multivariate Cox models were used to evaluate the DKI parameters and clinical factors for prediction of OS and PFS. RESULTS The large mean kurtosis values in the Gd-GTV pre-RT were significantly associated with reduced OS (p = 0.02), but the values at mid-RT were not (p > 0.8). In the multivariate Cox model, the mean kurtosis in the Gd-GTV pre-RT (p = 0.009) was still a significant predictor of OS after adjusting effects of age, O6-Methylguanine-DNA Methyl transferase (MGMT) methylation and extent of resection. In Gd-GTV post-RT, 80 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS). CONCLUSION The DKI model demonstrates the potential to predict OS and PFS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.
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Affiliation(s)
- Yuan Li
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Michelle M. Kim
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Daniel R. Wahl
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Theodore S. Lawrence
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Hemant Parmar
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Yue Cao
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
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Liu G, Lu Y, Dai Y, Xue K, Yi Y, Xu J, Wu D, Wu G. Comparison of mono-exponential, bi-exponential, kurtosis, and fractional-order calculus models of diffusion-weighted imaging in characterizing prostate lesions in transition zone. Abdom Radiol (NY) 2021; 46:2740-2750. [PMID: 33388809 DOI: 10.1007/s00261-020-02903-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 12/01/2020] [Accepted: 12/06/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE To compare various models of diffusion-weighted imaging including mono-exponential, bi-exponential, diffusion kurtosis (DK) and fractional-order calculus (FROC) models in diagnosing prostate cancer (PCa) in transition zone (TZ) and distinguish the high-grade PCa [Gleason score (GS) ≥ 7] lesions from the total of low-grade PCa (GS ≤ 6) lesions and benign prostatic hyperplasia (BPH) in TZ. METHODS 80 Patients with 103 lesions were included in this study. Nine metrics [including apparent diffusion coefficient (ADC) derived from mono-exponential model, slow diffusion coefficient (Ds), fast diffusion coefficient (Df),, and f (the fraction of fast diffusion) from bi-exponential model; mean diffusivity (MD) and mean kurtosis (MK) from DK model; diffusion coefficient (D), fractional-order derivative in space (β), and spatial metric (μ) from FROC model] were calculated. Comparisons between BPH and PCa lesions as well as between clinically significant PCa (CsPCa) (GS ≥ 7, n = 31) and clinically insignificant lesions (Cins) (GS ≤ 6 and BPH, n = 72) of these metrics were conducted. Mann-Whitney U-test and receiver operating characteristic (ROC) analysis were used for statistical evaluations. RESULTS The areas under the ROC curve (AUC) values of β derived from FROC model were 0.778 and 0.853 in differentiating PCa from BPH and in differentiating CS (GS ≥ 7) from Cins (GS ≤ 6 and BPH), both were the highest compared to other metrics. The AUC value of β was significantly higher than that of ADC (P = 0.009) in differentiating CS from Cins, while the differentiation between BPH and PCa did not reach the statistical significance when comparing with ADC (P = 0.089). CONCLUSION Although no significant difference was found in distinguishing PCa from BPH, the metric β derived from FROC model was superior to other diffusion metrics in differentiation between CS and Cins in TZ.
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Affiliation(s)
- Guiqin Liu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, 160 Pujian Road, Shanghai, 200127, China
| | - Yang Lu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, 160 Pujian Road, Shanghai, 200127, China
| | | | - Ke Xue
- United Imaging Healthcare, Shanghai, China
| | | | - Jianrong Xu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, 160 Pujian Road, Shanghai, 200127, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, 3663 N. Zhongshan Road, Shanghai, 200062, China.
| | - Guangyu Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, 160 Pujian Road, Shanghai, 200127, China.
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Zhang H, Yong X, Ma X, Zhao J, Shen Z, Chen X, Tian F, Chen W, Wu D, Zhang Y. Differentiation of low- and high-grade pediatric gliomas with amide proton transfer imaging: added value beyond quantitative relaxation times. Eur Radiol 2021; 31:9110-9119. [PMID: 34047848 DOI: 10.1007/s00330-021-08039-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/06/2021] [Accepted: 05/03/2021] [Indexed: 01/11/2023]
Abstract
OBJECTIVES To evaluate whether amide proton transfer (APT) MRI can be used to characterize gliomas in pediatric patients and whether it provides added value beyond relaxation times. METHODS In this prospective study, APT imaging and relaxation time mapping were performed in 203 pediatric patients suspected of gliomas from February 2018 to December 2019. The region of interest (ROI) in the tumor was automatically generated with artifact detection and ROI-shrinking algorithms. Several APT-related metrics (CESTR, CESTRnr, MTRRex, AREX, and APT#) and quantitative T1 and T2 were compared between low-grade and high-grade gliomas using the student's t-test or Mann-Whitney U-test. The performance of these parameters was assessed using the receiver operating characteristic (ROC) analysis. A stepwise multivariate logistic regression model was used to combine the imaging parameters. RESULTS Forty-eight patients (mean age: 6 ± 4 years; 23 males and 25 females) were included in the final analysis. All the APT-related metrics except APT# had significantly (p < 0.05) higher values in the high-grade group than the low-grade group. Under different ROI-shrinking cutoffs, the quantitative T1 (p = 0.045-0.200) and T2 (p = 0.037-0.171) values of high-grade gliomas were typically lower than those of low-grade ones. The stepwise multivariate logistic regression revealed that CESTRnr and APT# were combined significant predictors of glioma grades (p < 0.05), with an area under the ROC curve (AUC) of 0.86 substantially larger than those of T1 (AUC = 0.69) and T2 (AUC = 0.68). CONCLUSIONS APT imaging can be used to differentiate high-grade and low-grade gliomas in pediatric patients and provide added value beyond quantitative relaxation times. KEY POINTS • Amide proton transfer (APT) MRI showed significantly (p < 0.05) higher values in pediatric patients with high-grade gliomas than those with low-grade ones. • The area under the curve was 0.86 for APT MRI to differentiate low-grade and high-grade gliomas in pediatric patients, which was substantially higher than that for quantitative T1 (0.69) and T2 (0.68). • APT MRI demonstrated added value beyond quantitative T1 and T2 mapping in characterizing pediatric gliomas.
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Affiliation(s)
- Hongxi Zhang
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xingwang Yong
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaohui Ma
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianjiang Zhao
- Kangqiao Street Community Health Service Center, Gongshu District, Hangzhou, Zhejiang, China
| | - Zhipeng Shen
- Department of Neurosurgery, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xinchun Chen
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Fengyu Tian
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | | | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China. .,Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China.
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Momeni F, Abedi-Firouzjah R, Farshidfar Z, Taleinezhad N, Ansari L, Razmkon A, Banaei A, Mehdizadeh A. Differentiating Between Low- and High-grade Glioma Tumors Measuring Apparent Diffusion Coefficient Values in Various Regions of the Brain. Oman Med J 2021; 36:e251. [PMID: 33936779 PMCID: PMC8077446 DOI: 10.5001/omj.2021.59] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 08/31/2020] [Indexed: 11/03/2022] Open
Abstract
Objectives Our study aimed to apply the apparent diffusion coefficient (ADC) values to quantify the differences between low- and high-grade glioma tumors. Methods We conducted a multicenter, retrospective study between September to December 2019. Magnetic resonance imaging (MRI) diffusion-weighted images (DWIs), and the pathologic findings of 56 patients with glioma tumors (low grade = 28 and high grade = 28) were assessed to measure the ADC values in the tumor center, tumor edema, boundary area between tumor with normal tissue, and inside the healthy hemisphere. These values were compared between the two groups, and cut-off values were calculated using the receiver operating characteristic curve. Results We saw significant differences between the mean ADC values measured in the tumor center and edema between high- and low-grade tumors (p< 0.005). The ADC values in the boundary area between tumors with normal tissue and inside healthy hemisphere did not significantly differ in the groups. The ADC values at tumor center and edema were higher than 1.12 × 10-3 mm2/s (sensitivity = 100% and specificity = 96.0%) and 1.15 × 10-3 mm2/s (sensitivity = 75.0% and specificity = 64.0%), respectively, could be classified as low-grade tumors. Conclusions The ADC values from the MRI DWIs in the tumor center and edema could be used as an appropriate method for investigating the differences between low- and high-grade glioma tumors. The ADC values in the boundary area and healthy tissues had no diagnostic values in grading the glioma tumors.
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Affiliation(s)
- Farideh Momeni
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.,Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Razzagh Abedi-Firouzjah
- Department of Medical Physics, Radiobiology and Radiation Protection, Babol University of Medical Sciences, Babol, Iran
| | - Zahra Farshidfar
- Radiology Technology Department, School of Paramedicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nastaran Taleinezhad
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Leila Ansari
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ali Razmkon
- Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amin Banaei
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.,Department of Radiology, Faculty of Paramedical Sciences, AJA University of Medical Sciences, Tehran, Iran
| | - Alireza Mehdizadeh
- Medical Physics and Biomedical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.,Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
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Mokry T, Pantke J, Mlynarska-Bujny A, Hasse FC, Kuder TA, Schlemmer HP, Kauczor HU, Rom J, Bickelhaupt S. Diffusivity mapping of the ovaries: Variability of apparent diffusion and kurtosis variables over the menstrual cycle and influence of oral contraceptives. Magn Reson Imaging 2021; 80:50-57. [PMID: 33905830 DOI: 10.1016/j.mri.2021.04.006] [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: 11/18/2020] [Revised: 04/14/2021] [Accepted: 04/21/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE We aimed to investigate whether quantitative diffusivity variables of healthy ovaries vary during the menstrual cycle and to evaluate alterations in women using oral contraceptives (OC). METHODS This prospective study (S-339/2016) included 30 healthy female volunteers, with (n = 15) and without (n = 15) intake of OC between 07/2017 and 09/2019. Participants underwent 3T diffusion-weighted MRI (b-values 0-2000 s/mm2) three times during a menstrual cycle (T1 = day 1-5; T2 = day 7-12; T3 = day 19-24). Both ovaries were manually three-dimensionally segmented on b = 1500 s/mm2; apparent diffusion coefficient (ADC) calculation and kurtosis fitting (Dapp, Kapp) were performed. Differences in ADC, Dapp and Kapp between time points and groups were compared using repeated measures ANOVA and t-test after Shapiro-Wilk and Brown-Forsythe test for normality and equal variance. RESULTS In women with a natural menstrual cycle, ADC and kurtosis variables showed significant changes in ovaries with the dominant follicle between T1 vs T2 and T1 vs T3, whilst no differences were observed between T2 vs T3: ADC ± SD for T1 1.524 ± 0.160, T2 1.737 ± 0.160, and T3 1.747 ± 0.241 μm2/ms (p = 0.01 T2 vs T1; p = 1.0 T2 vs T3, p = 0.003 T3 vs T1); Dapp ± SD for T1 2.018 ± 0.140, T2 2.272 ± 0.189, and T3 2.230 ± 0.256 μm2/ms (p = 0.003 T2 vs T1, p = 1.0 T2 vs T3, p = 0.02 T3 vs T1); Kapp ± SD for T1 0.614 ± 0.0339, T2 0.546 ± 0.0637, and T3 0.529 ± 0.0567 (p < 0.001 T2 vs T1, p = 0.86 T2 vs T3, p < 0.001 T3 vs T1). No significant differences were found in the contralateral ovaries or in females taking OC. CONCLUSION Physiological cycle-dependent changes in quantitative diffusivity variables of ovaries should be considered especially when interpreting radiomics analyses in reproductive women.
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Affiliation(s)
- Theresa Mokry
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany.
| | - Judith Pantke
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Anna Mlynarska-Bujny
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany; Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Germany
| | - Felix Christian Hasse
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Tristan Anselm Kuder
- Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
| | | | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Joachim Rom
- Hospital for General Obstetrics and Gynecology, Hospital Frankfurt Hoechst, Frankfurt, Germany
| | - Sebastian Bickelhaupt
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany; Junior Group Medical Imaging and Radiology - Cancer Prevention, German Cancer Research Center, Heidelberg, Germany; Institute of Radiology, Erlangen University Hospital, Erlangen, Germany
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Li Y, Kim M, Lawrence TS, Parmar H, Cao Y. Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma. ACTA ACUST UNITED AC 2021; 6:34-43. [PMID: 32280748 PMCID: PMC7138521 DOI: 10.18383/j.tom.2020.00018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Apparent diffusion coefficient has limits to differentiate solid tumor from normal tissue or edema in glioblastoma (GBM). This study investigated a microstructure model (MSM) in GBM using a clinically available diffusion imaging technique. The MSM was modified to integrate with bi-polar diffusion gradient waveforms, and applied to 30 patients with newly diagnosed GBM. Diffusion-weighted (DW) images acquired on a 3 T scanner with b-values from 0 to 2500 s/mm2 were fitted in volumes of interest (VOIs) of solid tumor to obtain the apparent restriction size of intracellular water (ARS), the fractional volume of intracellular water (Vin), and extracellular (Dex) water diffusivity. The parameters in solid tumor were compared with those of other tissue types by Students’ t test. For comparison, DW images were fitted by conventional mono-exponential and bi-exponential models. ARS, Dex, and Vin from the MSM in tumor VOIs were significantly greater than those in WM, GM, and edema (P values of .01–.001). ARS values in solid tumors (from 21.6 to 34.5 um) had absolutely no overlap with those in all other tissue types (from 0.9 to 3.5 um). Vin values showed a descending order from solid tumor (from 0.32 to 0.52) to WM, GM, and edema (from 0.05 to 0.25), consisting with the descending cellularity in these tissue types. The parameters from mono-exponential and bi-exponential models could not significantly differentiate solid tumor from all other tissue types, particularly from edema. Further development and histopathological validation of the MSM will warrant its role in clinical management of GBM.
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Affiliation(s)
- Yuan Li
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Michelle Kim
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Theodore S Lawrence
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Hemant Parmar
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Yue Cao
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
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Karaman MM, Zhang J, Xie KL, Zhu W, Zhou XJ. Quartile histogram assessment of glioma malignancy using high b-value diffusion MRI with a continuous-time random-walk model. NMR IN BIOMEDICINE 2021; 34:e4485. [PMID: 33543512 DOI: 10.1002/nbm.4485] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/15/2021] [Indexed: 06/12/2023]
Abstract
The purpose of this study is to investigate the feasibility of using a continuous-time random-walk (CTRW) diffusion model, together with a quartile histogram analysis, for assessing glioma malignancy by probing tissue heterogeneity as well as cellularity. In this prospective study, 91 patients (40 females, 51 males) with histopathologically proven gliomas underwent MRI at 3 T. The cohort included 42 grade II (GrII), 19 grade III (GrIII) and 29 grade IV (GrIV) gliomas. Echo-planar diffusion-weighted imaging was conducted using 17 b-values (0-4000 s/mm2 ). Three CTRW model parameters, including an anomalous diffusion coefficient Dm , and two parameters related to temporal and spatial diffusion heterogeneity α and β, respectively, were obtained. The mean parameter values within the tumor regions of interest (ROIs) were computed by utilizing the first quartile of the histograms as well as the full ROI for comparison. A Bonferroni-Holm-corrected Mann-Whitney U-test was used for the group comparisons. Individual and combinations of the CTRW parameters were evaluated for the characterization of gliomas with a receiver operating characteristic analysis. All first-quartile mean CTRW parameters yielded significant differences (p-values < 0.05) between pair-wise comparisons of GrII (Dm : 1.14 ± 0.37 μm2 /ms; α: 0.904 ± 0.03, β: 0.913 ± 0.06), GrIII (Dm : 0.88 ± 0.21 μm2 /ms; α: 0.888 ± 0.01, β: 0.857 ± 0.06) and GrIV gliomas (Dm : 0.73 ± 0.22 μm2 /ms; α: 0.878 ± 0.01; β: 0.791 ± 0.07). The highest sensitivity, specificity, accuracy and area-under-the-curve of using the combinations of the first-quartile parameters were 84.2%, 78.5%, 75.4% and 0.76 for GrII and GrIII classification; 86.2%, 89.4%, 75% and 0.76 for GrIII and GrIV classification; and 86.2%, 85.7%, 84.5% and 0.90 for GrII and GrIV classification, respectively. Quartile-based analysis produced higher accuracy and area-under-the-curve than the full ROI-based analysis in all classifications. The CTRW diffusion model, together with a quartile-based histogram analysis, offers a new way for probing tumor structural heterogeneity at a subvoxel level, and has potential for in vivo assessment of glioma malignancy to complement histopathology.
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Affiliation(s)
- M Muge Karaman
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Jiaxuan Zhang
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Karen L Xie
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA
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Karaman MM, Tang L, Li Z, Sun Y, Li JZ, Zhou XJ. In vivo assessment of Lauren classification for gastric adenocarcinoma using diffusion MRI with a fractional order calculus model. Eur Radiol 2021; 31:5659-5668. [PMID: 33616764 DOI: 10.1007/s00330-021-07694-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 12/21/2020] [Accepted: 01/18/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate the performance of a fractional order calculus (FROC) diffusion model for imaging-based assessment of Lauren classification in gastric adenocarcinoma. METHODS In this study, 43 patients (15 females, 28 males) with gastric adenocarcinoma underwent MRI at 1.5 T. According to pathology-based Lauren classification, 10 patients had diffuse-type, 20 had intestinal-type, and 13 had mixed-type lesions. The diffuse and mixed types were combined as diffuse-and-mixed type to be differentiated from the intestinal type using diffusion MRI. Diffusion-weighted images were acquired by using eleven b-values (0-2000 s/mm2). Three FROC model parameters comprising diffusion coefficient D, intravoxel diffusion heterogeneity β, and a microstructural quantity μ, together with a conventional apparent diffusion coefficient (ADC), were estimated. The mean parameter values in the tumour were computed by using a percentile histogram analysis. Individual or linear combinations of the mean parameters in the tumour were used to differentiate the diffuse-and-mixed type from the intestinal type using descriptive statistics and receiver operating characteristic (ROC) analyses. RESULTS Significant differences were observed between diffuse-and-mixed-type and intestinal-type lesions in D (0.99 ± 0.20 μm2/ms vs. 1.11 ± 0.23 μm2/ms; p = 0.036), β (0.37 ± 0.08 vs. 0.43 ± 0.11; p = 0.043), μ (7.92 ± 2.79 μm vs. 9.87 ± 1.52 μm; p = 0.038), and ADC (0.81 ± 0.34 μm2/ms vs. 0.96 ± 0.19 μm2/ms; p = 0.033). Among the individual parameters, μ produced the largest area under the ROC curve (0.739). The combinations of (D, β, μ) and (β and μ) produced the best overall performance with a sensitivity of 0.739, specificity of 0.750, accuracy of 0.744, and area under the curve of 0.793 (95% confidence interval: 0.657-0.929). CONCLUSION Diffusion MRI with the FROC model holds promise for non-invasive assessment of Lauren classification for gastric adenocarcinoma. KEY POINTS • High b-value diffusion MRI with a FROC model that is sensitive to tissue microstructures can differentiate the diffuse-and-mixed type from intestinal type of gastric adenocarcinoma. • The combination of FROC parameters produced the best result for distinguishing the diffuse-and-mixed type from the intestinal type with an area under the receiver operating characteristic curve of 0.793. • The FROC model parameters, individually or conjointly, hold promise for repeated, non-invasive evaluations of gastric adenocarcinoma at various time points throughout disease progression or regression to complement conventional Lauren classification.
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Affiliation(s)
- M Muge Karaman
- Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA.,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Lei Tang
- Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ziyu Li
- Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing, China
| | - Yu Sun
- Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jia-Zheng Li
- Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiaohong Joe Zhou
- Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA. .,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA. .,Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA. .,Center for Magnetic Resonance Research, University of Illinois at Chicago, 2242 West Harrison Street, Suite 103, M/C 831, Chicago, IL, 60612, USA.
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Wagner MW, Hainc N, Khalvati F, Namdar K, Figueiredo L, Sheng M, Laughlin S, Shroff MM, Bouffet E, Tabori U, Hawkins C, Yeom KW, Ertl-Wagner BB. Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors. AJNR Am J Neuroradiol 2021; 42:759-765. [PMID: 33574103 DOI: 10.3174/ajnr.a6998] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 10/23/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation. MATERIALS AND METHODS In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance. RESULTS The training cohort consisted of 94 pediatric patients with low-grade gliomas (mean age, 9.4 years; 45 boys), and the external validation cohort comprised 21 pediatric patients with low-grade gliomas (mean age, 8.37 years; 12 boys). A 4-fold cross-validation scheme predicted BRAF status with an area under the curve of 0.75 (SD, 0.12) (95% confidence interval, 0.62-0.89) on the internal validation cohort. By means of the optimal hyperparameters determined by 4-fold cross-validation, the area under the curve for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status (P values = .04 and <.001, respectively). Sex was not a significant predictor (P value = .96). CONCLUSIONS Radiomics-based prediction of BRAF status in pediatric low-grade gliomas appears feasible in this bi-institutional exploratory study.
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Affiliation(s)
- M W Wagner
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - N Hainc
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.).,Department of Neuroradiology (N.H.), Zurich University Hospital, University of Zurich, Zurich, Switzerland
| | - F Khalvati
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - K Namdar
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - L Figueiredo
- Division of Neuroradiology, Neurooncology (L.F., E.B., U.T.)
| | - M Sheng
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - S Laughlin
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - M M Shroff
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
| | - E Bouffet
- Division of Neuroradiology, Neurooncology (L.F., E.B., U.T.)
| | - U Tabori
- Division of Neuroradiology, Neurooncology (L.F., E.B., U.T.)
| | - C Hawkins
- Paediatric Laboratory Medicine (C.H.), Division of Pathology, The Hospital for Sick Children and Department of Medical Imaging, University of Toronto, Ontario, Canada
| | - K W Yeom
- Department of Radiology (K.W.Y.), Stanford University School of Medicine, Lucile Packard Children's Hospital, Palo Alto, California
| | - B B Ertl-Wagner
- From the Departments of Diagnostic Imaging (M.W.W., N.H., F.K., K.N., M.S., S.L., M.M.S., B.B.E.-W.)
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Testud B, Brun G, Varoquaux A, Hak JF, Appay R, Le Troter A, Girard N, Stellmann JP. Perfusion-weighted techniques in MRI grading of pediatric cerebral tumors: efficiency of dynamic susceptibility contrast and arterial spin labeling. Neuroradiology 2021; 63:1353-1366. [PMID: 33506349 DOI: 10.1007/s00234-021-02640-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 01/06/2021] [Indexed: 01/23/2023]
Abstract
PURPOSE Dynamic susceptibility contrast (DSC) and arterial spin labeling (ASL) perfusion MRI are applied in pediatric brain tumor grading, but their value for clinical daily practice remains unclear. We explored the ability of ASL and DSC to distinguish low- and high-grade lesions, in an unselected cohort of pediatric cerebral tumors. METHODS We retrospectively compared standard perfusion outcomes including blood volume, blood flow, and time parameters from DSC and ASL at 1.5T or 3T MRI scanners of 46 treatment-naive patients by drawing ROI via consensus by two neuroradiologists on the solid portions of every tumor. The discriminant abilities of perfusion parameters were evaluated by receiver operating characteristic (ROC) over the entire cohort and depending on the tumor location and the magnetic field. RESULTS ASL and DSC parameters showed overall low to moderate performances to distinguish low- and high-grade tumors (area under the curve: between 0.548 and 0.697). Discriminant abilities were better for tumors located supratentorially (AUC between 0.777 and 0.810) than infratentorially, where none of the metrics reached significance. We observed a better differentiation between low- and high-grade cancers at 3T than at 1.5-T. For infratentorial tumors, time parameters from DSC performed better than the commonly used metrics (AUC ≥ 0.8). CONCLUSION DSC and ASL show moderate abilities to distinguish low- and high-grade brain tumors in an unselected cohort. Absolute value of K2, TMAX, tMIP, and normalized value of TMAX of the DSC appear as an alternative to conventional parameters for infratentorial tumors. Three Tesla evaluation should be favored over 1.5-Tesla.
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Affiliation(s)
- B Testud
- Department of Diagnostic and Interventional Neuroradiology, APHM La Timone, 264 Saint Pierre Street, 13385, CEDEX 05, Marseille, France.
| | - G Brun
- Department of Diagnostic and Interventional Neuroradiology, APHM La Timone, 264 Saint Pierre Street, 13385, CEDEX 05, Marseille, France
| | - A Varoquaux
- APHM La Conception, Department of Medical Imaging, Aix Marseille Université, Marseille, France
| | - J F Hak
- Department of Diagnostic and Interventional Neuroradiology, APHM La Timone, 264 Saint Pierre Street, 13385, CEDEX 05, Marseille, France
| | - R Appay
- Department of Pathology and Neuropathology, APHM La Timone, Marseille, France.,Aix-Marseille Univ, CNRS, INP, Inst Neurophysiopathol, Marseille, France
| | - A Le Troter
- Aix-Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France.,APHM La Timone, CEMEREM, Marseille, France
| | - N Girard
- Department of Diagnostic and Interventional Neuroradiology, APHM La Timone, 264 Saint Pierre Street, 13385, CEDEX 05, Marseille, France.,Aix-Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - J P Stellmann
- Aix-Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France.,APHM La Timone, CEMEREM, Marseille, France
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Tuza FADA, de Sá PM, Castro HA, Lopes AJ, de Melo PL. Combined forced oscillation and fractional-order modeling in patients with work-related asthma: a case-control study analyzing respiratory biomechanics and diagnostic accuracy. Biomed Eng Online 2020; 19:93. [PMID: 33298072 PMCID: PMC7724713 DOI: 10.1186/s12938-020-00836-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 11/23/2020] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Fractional-order (FrOr) models have a high potential to improve pulmonary science. These models could be useful for biomechanical studies and diagnostic purposes, offering accurate models with an improved ability to describe nature. This paper evaluates the performance of the Forced Oscillation (FO) associated with integer (InOr) and FrOr models in the analysis of respiratory alterations in work-related asthma (WRA). METHODS Sixty-two individuals were evaluated: 31 healthy and 31 with WRA with mild obstruction. Patients were analyzed pre- and post-bronchodilation. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). To evaluate how well do the studied models correspond to observed data, we analyzed the mean square root of the sum (MSEt) and the relative distance (Rd) of the estimated model values to the measured resistance and reactance measured values. RESULTS AND DISCUSSION Initially, the use of InOr and FrOr models increased our understanding of the WRA physiopathology, showing increased peripheral resistance, damping, and hysteresivity. The FrOr model (AUC = 0.970) outperformed standard FO (AUC = 0.929), as well as InOr modeling (AUC = 0.838) in the diagnosis of respiratory changes, achieving high accuracy. FrOr improved the curve fitting (MSEt = 0.156 ± 0.340; Rd = 3.026 ± 1.072) in comparison with the InOr model (MSEt = 0.367 ± 0.991; Rd = 3.363 ± 1.098). Finally, we demonstrated that bronchodilator use increased dynamic compliance, as well as reduced damping and peripheral resistance. CONCLUSIONS Taken together, these results show clear evidence of the utility of FO associated with fractional-order modeling in patients with WRA, improving our knowledge of the biomechanical abnormalities and the diagnostic accuracy in this disease.
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Affiliation(s)
- Fábio Augusto D Alegria Tuza
- Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Haroldo Lisboa da Cunha Pavilion Number 104 and 105, São Francisco Xavier Street 524 Maracanã, Rio de Janeiro, RJ, 20550-013, Brazil
- BioVasc Research Laboratory, Institute of Biology, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Paula Morisco de Sá
- Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Haroldo Lisboa da Cunha Pavilion Number 104 and 105, São Francisco Xavier Street 524 Maracanã, Rio de Janeiro, RJ, 20550-013, Brazil
- BioVasc Research Laboratory, Institute of Biology, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Hermano A Castro
- National School of Public Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Agnaldo José Lopes
- School of Medical Sciences, Pulmonary Function Testing Laboratory, State University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Rehabilitation Sciences Post-Graduation Program, Augusto Motta University Centre, Rio de Janeiro, Brazil
| | - Pedro Lopes de Melo
- Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Haroldo Lisboa da Cunha Pavilion Number 104 and 105, São Francisco Xavier Street 524 Maracanã, Rio de Janeiro, RJ, 20550-013, Brazil.
- BioVasc Research Laboratory, Institute of Biology, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
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