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Wang J, Jing S, Yang Z, Tan W, Liu Y. The role of multiparametric MRI in predicting lymphovascular invasion in breast cancer patients. Future Oncol 2024:1-10. [PMID: 39268927 DOI: 10.1080/14796694.2024.2396273] [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: 03/18/2024] [Accepted: 08/21/2024] [Indexed: 09/15/2024] Open
Abstract
Background: This study aims to investigate the efficacy of multifactorial MRI in diagnosing breast cancer, specifically in the context of predicting lymphovascular invasion (LVI).Materials & methods: The patients were stratified into two groups: the primary group (100 patients) and the validation group (100 patients), based on essential characteristics. Multifactorial MRI, encompassing tumor size evaluation, diffusion coefficient assessment and dynamic contrast enhancement, was employed for patient examination.Results: Statistically significant differences were observed in tumor size, diffusion coefficient and dynamic contrast enhancement between groups with LVI (LVI+) and those without (LVI-). Key parameters were identified for predicting the degree of invasion.Conclusion: The results affirm the effectiveness of multifactorial MRI in forecasting LVI.
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Affiliation(s)
- Jinhua Wang
- Medical Image Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- The Third of Clinical Medicine, Southern Medical University, Shenzhen, China
| | - Siqing Jing
- Medical Image Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- The Third of Clinical Medicine, Southern Medical University, Shenzhen, China
| | - Zhongxian Yang
- Medical Image Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- The Third of Clinical Medicine, Southern Medical University, Shenzhen, China
| | - Wanchang Tan
- Department of Radiology, The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, China
| | - Yubao Liu
- Medical Image Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- The Third of Clinical Medicine, Southern Medical University, Shenzhen, China
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Ma Q, Lu X, Chen Q, Gong H, Lei J. Multiphases DCE-MRI Radiomics Nomogram for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer. Acad Radiol 2024:S1076-6332(24)00365-9. [PMID: 39107190 DOI: 10.1016/j.acra.2024.06.007] [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: 02/15/2024] [Revised: 06/03/2024] [Accepted: 06/03/2024] [Indexed: 08/09/2024]
Abstract
RATIONALE AND OBJECTIVES Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics had been used to evaluate lymphovascular invasion (LVI) in patients with breast cancer. However, no studies had explored the associations between features from delayed phase as well as multiphases DCE-MRI and the LVI status. Thus, we aimed to develop an efficient nomogram based on multiphases DCE-MRI to predict the LVI status in invasive (IBC) breast cancer patients. MATERIALS AND METHODS A retrospective analysis was conducted on preoperative clinical, pathological, and DCE-MRI data of 173 breast cancer patients. All patients were randomly assigned into training set (n=121) and validation set (n=52) in 7:3 ratio. The clinical, pathologic, and conventional MRI characteristics were then subjected to univariate and multivariate logistic regression analysis, and the clinical risk factors with P < 0.05 in the multivariate logistic regression were used to build clinical models. Different single-phase models (early phase, peak phase, and terminal phase), as well as a multiphases model integrating radiomics features from multiple phases, were established. Furthermore, a preoperative radiomics nomogram model was constructed by combining the rad-score of the multiphases model with clinicopathologic independent risk factors. Finally, the performance of the multiphases model, clinical model, and rad-score was compared using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, and decision curve analysis (DCA). The clinical utility of the rad-score was evaluated using calibration curves, and Delong test was used to compare the differences in AUC values among the different models. RESULTS The axillary lymph nodes (ALN) status and Ki-67 had been identified as clinicopathologic independent predictors and a clinical model had been constructed. Image features that were extracted from the terminal phase of the DCE-MRI exhibited notably superior predictive performances compared to features from the other single phases. Particularly, in the multiphases model, terminal phase features were identified as potentially providing more predictive information. Among the nine features that were found to be associated with LVI in the multiphase model, one was derived from the early phase, two from the peak phase, and six from the terminal phase, indicating that terminal phase features contributed significantly more information towards predicting LVI. Evaluation of the nomogram performance revealed promising results in both the training set (AUCs: clinical model vs. multiphase model vs. nomogram=0.734 vs. 0.840 vs. 0.876) and the validation set (AUCs: clinical model vs. multiphase model vs. nomogram=0.765 vs. 0.753 vs. 0.832). CONCLUSION The DCE-MRI-based radiomics model demonstrated utility in predicting LVI status, features of the terminal phase offered more valuable information particularly. The preoperative radiomics nomogram enhanced the diagnostic capability of identifying LVI status in IBC patients, and might aid clinicians in making personalized treatment decisions.
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Affiliation(s)
- Qinqin Ma
- Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou 730000, China; The First Clinical Medicine School of Lanzhou University, Lanzhou 730000, China
| | - Xingru Lu
- Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China
| | - Qitian Chen
- The General Hospital of Gansu Province in the Chinese Armed Police Force, Lanzhou 730000, China
| | - Hengxin Gong
- The First Clinical Medicine School of Lanzhou University, Lanzhou 730000, China
| | - Junqiang Lei
- The First Clinical Medicine School of Lanzhou University, Lanzhou 730000, China; Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China.
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Zhang M, Zha H, Pan J, Liu X, Zong M, Du L, Du Y. Development of an Ultrasound-based Nomogram for Predicting Pathologic Complete Response and Axillary Response in Node-Positive Patients with Triple- Negative Breast Cancer. Clin Breast Cancer 2024; 24:e485-e494.e1. [PMID: 38627192 DOI: 10.1016/j.clbc.2024.03.012] [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: 11/05/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND The accurate prediction of pathological complete response (pCR) in the breast and axillary lymph nodes (ALN) before neoadjuvant chemotherapy (NAC) is of utmost importance for the development of treatment strategies. We aim to construct a nomogram on ultrasound (US) and clinical-pathologic factors to predict breast and ALN pCR in node-positive triple-negative breast cancers (TNBCs). METHODS Patients identified with TNBCs from institution 1 (n = 328) were used for training cohort and those from institution 2 (n = 192) were for validation cohort. US was conducted before and after NAC, and characteristics were obtained from medical records. Univariate and multivariate regression analysis were performed to identify US and clinical-pathologic factors associated with breast and ALN pCR in the training cohort. The assessment of predictive performance was conducted using the receiving operating characteristic curve (ROC), discrimination, and calibration. RESULTS Overall, 34.6% of patients achieved breast pCR and 48.1% of patients achieved ALN pCR. The nomogram 1 used for predicting pCR in the breast (AUC, 0.84; 95% CI: 0.79, 0.88) outperformed the clinical (AUC, 0.73; 95% CI: 0.68, 0.78) and US models (AUC, 0.79; 95% CI: 0.74, 0.83). The nomogram 2 used for predicting pCR in the axllia (AUC, 0.83; 95% CI: 0.78, 0.87) also outperformed the clinical (AUC, 0.64; 95% CI: 0.58, 0.69) and US models (AUC, 0.80; 95% CI: 0.75, 0.84). The calibration curve and discrimination curve indicate that the nomogram has good calibration performance and clinical applicability. CONCLUSION The nomogram showed promising predictive performance for predicting breast and ALN pCR in patients with TNBCs.
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Affiliation(s)
- Manqi Zhang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hailing Zha
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiazhen Pan
- Department of Ultrasound, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Min Zong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Liwen Du
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Yu Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Zha H, Wu T, Zhang M, Cai M, Diao X, Li F, Wu R, Du Y. Combining Potential Strain Elastography and Radiomics for Diagnosing Breast Lesions in BI-RADS 4: Construction and Validation a Predictive Nomogram. Acad Radiol 2024; 31:3106-3116. [PMID: 38378324 DOI: 10.1016/j.acra.2024.01.038] [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: 12/22/2023] [Revised: 01/21/2024] [Accepted: 01/27/2024] [Indexed: 02/22/2024]
Abstract
RATIONALE AND OBJECTIVES To develop a nomogram by integrating B-mode ultrasound (US), strain ratio (SR), and radiomics signature (RS) effectively differentiating between benign and malignant lesions in the Breast Imaging Reporting and Data System (BI-RADS) 4. MATERIALS AND METHODS We retrospectively recruited 709 consecutive patients who were assigned a BI-RADS 4 and underwent curative resection or biopsy between 2017 and 2022. US images were collected before surgery. A RS was developed through a multistep feature selection and construction process. Histology findings served as the gold standard. Univariate and multivariate regression analysis were employed to analyze the clinical and US characteristics and identify variables for developing a nomogram. The calibration and discrimination of the nomogram were conducted to evaluate its performance. RESULTS The study included a total of 709 patients, with 497 in the training set and 212 in the validation set. In the training set, the B-mode US had an AUC of 0.84 (95% confidence interval [CI], 0.80, 0.87). The SR demonstrated an AUC of 0.78 (95% CI, 0.74, 0.82), while the RS showed an AUC of 0.85 (95% CI, 0.81, 0.88). Notably, the nomogram exhibited superior performance compared to the conventional US, SR, and RS (AUC=0.93, both p < 0.05, as per the Delong test). The clinical usefulness of the nomogram was favorable. CONCLUSION The calibrated nomogram can be specifically designed to predict the malignancy of breast lesions in the BI-RADS 4 category.
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Affiliation(s)
- Hailing Zha
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tingting Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Manqi Zhang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mengjun Cai
- Department of Ultrasound, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xuehong Diao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Xu M, Yang H, Sun J, Hao H, Li X, Liu G. Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer. Acad Radiol 2024; 31:1748-1761. [PMID: 38097466 DOI: 10.1016/j.acra.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 05/12/2024]
Abstract
RATIONALE AND OBJECTIVES This study aimed to create a nomogram model that combines clinical factors with radiomics analysis of both intra- and peritumoral regions extracted from preoperative digital breast tomosynthesis (DBT) images, in order to develop a reliable method for predicting the lymphovascular invasion (LVI) status in invasive breast cancer (IBC) patients. MATERIALS AND METHODS A total of 178 patients were randomly split into a training dataset (N = 124) and a validation dataset (N = 54). Comprehensive clinical data, encompassing DBT features, were gathered for all cases. Radiomics features were extracted and selected from intra- and peritumoral region to establish radiomics signature (Radscore). To construct the clinical model and nomogram model, univariate and multivariate logistic regression analyses were utilized to identify independent risk factors. To assess and validate these models, various analytical methods were employed, including receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI). RESULTS The clinical model is constructed based on two independent risk factors: tumor margin and the DBT-reported lymph node metastasis (DBT_reported_LNM). Incorporating Radscore_Combine (utilizing both intra- and peritumoral radiomics features), tumor margin, and DBT_reported_LNM into the nomogram achieved a reliable predictive performance, with area under the curve (AUC) values of 0.906 and 0.905 in both datasets, respectively. The significant improvement demonstrated by the NRI and IDI indicates that the Radscore_Combine could be a valuable biomarker for effectively predicting the status of LVI. CONCLUSION The nomogram demonstrated a reliable ability to predict LVI in IBC patients.
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Affiliation(s)
- Maolin Xu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun 130033, China (M.X., J.S., H.H., X.L., G.L.)
| | - Huimin Yang
- Department of Radiology, Linfen Central Hospital, Linfen 041000, China (H.Y.)
| | - Jia Sun
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun 130033, China (M.X., J.S., H.H., X.L., G.L.)
| | - Haifeng Hao
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun 130033, China (M.X., J.S., H.H., X.L., G.L.)
| | - Xiaojing Li
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun 130033, China (M.X., J.S., H.H., X.L., G.L.)
| | - Guifeng Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun 130033, China (M.X., J.S., H.H., X.L., G.L.).
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Zhang D, Zhou W, Lu WW, Qin XC, Zhang XY, Wang JL, Wu J, Luo YH, Duan YY, Zhang CX. Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study. Acad Radiol 2024:S1076-6332(24)00217-4. [PMID: 38658211 DOI: 10.1016/j.acra.2024.04.010] [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: 02/22/2024] [Revised: 03/21/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC). MATERIALS AND METHODS In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness. RESULTS The DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI. CONCLUSION The DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.
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Affiliation(s)
- Di Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.)
| | - Wang Zhou
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.)
| | - Wen-Wu Lu
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.)
| | - Xia-Chuan Qin
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.); Department of Ultrasound, Beijing Anzhen Hospital Nanchong Hospital, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nan Chong, Sichuan 637000, China (X.C.Q.)
| | - Xian-Ya Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China (X.Y.Z.)
| | - Jun-Li Wang
- Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People's Hospital, WuHu), Wuhu, Anhui 241001, China (J.L.W.)
| | - Jun Wu
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China (J.W.)
| | - Yan-Hong Luo
- The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, Hefei, Anhui 230061, China (Y.H.L.)
| | - Ya-Yang Duan
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.)
| | - Chao-Xue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (D.Z., W.Z., W.W.L., X.C.Q., Y.Y.D., C.X.Z.).
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Wu WT, Lin CY, Shu YC, Shen PC, Lin TY, Chang KV, Özçakar L. The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2023; 13:3280. [PMID: 37892101 PMCID: PMC10606315 DOI: 10.3390/diagnostics13203280] [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: 09/26/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
Background: Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy for which ultrasound imaging has recently emerged as a valuable diagnostic tool. This meta-analysis aims to investigate the role of ultrasound radiomics in the diagnosis of CTS and compare it with other diagnostic approaches. Methods: We conducted a comprehensive search of electronic databases from inception to September 2023. The included studies were assessed for quality using the Quality Assessment Tool for Diagnostic Accuracy Studies. The primary outcome was the diagnostic performance of ultrasound radiomics compared to radiologist evaluation for diagnosing CTS. Results: Our meta-analysis included five observational studies comprising 840 participants. In the context of radiologist evaluation, the combined statistics for sensitivity, specificity, and diagnostic odds ratio were 0.78 (95% confidence interval (CI), 0.71 to 0.83), 0.72 (95% CI, 0.59 to 0.81), and 9 (95% CI, 5 to 15), respectively. In contrast, the ultrasound radiomics training mode yielded a combined sensitivity of 0.88 (95% CI, 0.85 to 0.91), a specificity of 0.88 (95% CI, 0.84 to 0.92), and a diagnostic odds ratio of 58 (95% CI, 38 to 87). Similarly, the ultrasound radiomics testing mode demonstrated an aggregated sensitivity of 0.85 (95% CI, 0.78 to 0.89), a specificity of 0.80 (95% CI, 0.73 to 0.85), and a diagnostic odds ratio of 22 (95% CI, 12 to 41). Conclusions: In contrast to assessments by radiologists, ultrasound radiomics exhibited superior diagnostic performance in detecting CTS. Furthermore, there was minimal variability in the diagnostic accuracy between the training and testing sets of ultrasound radiomics, highlighting its potential as a robust diagnostic tool in CTS.
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Affiliation(s)
- Wei-Ting Wu
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 10048, Taiwan;
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Bei-Hu Branch, Taipei 10845, Taiwan
| | - Che-Yu Lin
- Institute of Applied Mechanics, College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (C.-Y.L.); (Y.-C.S.)
| | - Yi-Chung Shu
- Institute of Applied Mechanics, College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (C.-Y.L.); (Y.-C.S.)
| | - Peng-Chieh Shen
- Department of Physical Medicine and Rehabilitation, Lo-Hsu Medical Foundation, Inc., Lotung Poh-Ai Hospital, Yilan 26546, Taiwan; (P.-C.S.); (T.-Y.L.)
| | - Ting-Yu Lin
- Department of Physical Medicine and Rehabilitation, Lo-Hsu Medical Foundation, Inc., Lotung Poh-Ai Hospital, Yilan 26546, Taiwan; (P.-C.S.); (T.-Y.L.)
| | - Ke-Vin Chang
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 10048, Taiwan;
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Bei-Hu Branch, Taipei 10845, Taiwan
- Center for Regional Anesthesia and Pain Medicine, Wang-Fang Hospital, Taipei Medical University, Taipei 11600, Taiwan
| | - Levent Özçakar
- Department of Physical and Rehabilitation Medicine, Hacettepe University Medical School, Ankara 06100, Turkey;
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