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Yang Y, Cheng J, Peng Z, Yi L, Lin Z, He A, Jin M, Cui C, Liu Y, Zhong Q, Zuo M. Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study. Acad Radiol 2024; 31:1615-1628. [PMID: 37949702 DOI: 10.1016/j.acra.2023.10.018] [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: 08/31/2023] [Revised: 10/04/2023] [Accepted: 10/07/2023] [Indexed: 11/12/2023]
Abstract
RATIONALE AND OBJECTIVES This study aims to evaluate the feasibility and effectiveness of deep transfer learning (DTL) and clinical-radiomics in differentiating thymoma from thymic cysts. MATERIALS AND METHODS Clinical and imaging data of 196 patients pathologically diagnosed with thymoma and thymic cysts were retrospectively collected from center 1. (training cohort: n = 137; internal validation cohort: n = 59). An independent external validation cohort comprised 68 thymoma and thymic cyst patients from center 2. Region of interest (ROI) delineation was performed on contrast-enhanced chest computed tomography (CT) images, and eight DTL models including Densenet 169, Mobilenet V2, Resnet 101, Resnet 18, Resnet 34, Resnet 50, Vgg 13, Vgg 16 were constructed. Radiomics features were extracted from the ROI on the CT images of thymoma and thymic cyst patients, and feature selection was performed using intra-observer correlation coefficient (ICC), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) algorithm. Univariate analysis and multivariable logistic regression (LR) were used to select clinical-radiological features. Six machine learning classifiers, including LR, support vector machine (SVM), k-nearest neighbors (KNN), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP), were used to construct Radiomics and Clinico-radiologic models. The selected features from the Radiomics and Clinico-radiologic models were fused to build a Combined model. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical utility of the models, respectively. The Delong test was used to compare the AUC between different models. K-means clustering was used to subdivide the lesions of thymomas or thymic cysts into subregions, and traditional radiomics methods were used to extract features and compare the ability of Radiomics and DTL models to reflect intratumoral heterogeneity using correlation analysis. RESULTS The Densenet 169 based on DTL performed the best, with AUC of 0.933 (95% CI: 0.875-0.991) in the internal validation cohort and 0.962 (95% CI: 0.923-1.000) in the external validation cohort. The AdaBoost classifier achieved AUC of 0.965 (95% CI: 0.923-1.000) and 0.959 (95% CI: 0.919-1.000) in the internal and external validation cohorts, respectively, for the Radiomics model. The LightGBM classifier achieved AUC of 0.805 (95% CI: 0.690-0.920) and 0.839 (95% CI: 0.736-0.943) in the Clinico-radiologic model. The AUC of the Combined model in the internal and external validation cohorts was 0.933 (95% CI: 0.866-1.000) and 0.945 (95% CI: 0.897-0.994), respectively. The results of the Delong test showed that the Radiomics model, DTL model, and Combined model outperformed the Clinico-radiologic model in both internal and external validation cohorts (p-values were 0.002, 0.004, and 0.033 in the internal validation cohort, while in the external validation cohort, the p-values were 0.014, 0.006, and 0.015, respectively). But there was no statistical difference in performance among the three models (all p-values <0.05). Correlation analysis showed that radiomics performed better than DTL in quantifying intratumoral heterogeneity differences between thymoma and thymic cysts. CONCLUSION The developed DTL model and the Combined model based on radiomics and clinical-radiologic features achieved excellent diagnostic performance in differentiating thymic cysts from thymoma. They can serve as potential tools to assist clinical decision-making, particularly when endoscopic biopsy carries a high risk.
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Affiliation(s)
- Yuhua Yang
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.)
| | - Jia Cheng
- Department of Radiology, the First Affiliated Hospital of Gannan Medical University, Ganzhou, China (J.C.)
| | - Zhiwei Peng
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.)
| | - Li Yi
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.)
| | - Ze Lin
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.)
| | - Anjing He
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.)
| | - Mengni Jin
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.)
| | - Can Cui
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.)
| | - Ying Liu
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.)
| | - QiWen Zhong
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.)
| | - Minjing Zuo
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China (Y.Y., Z.P., L.Y., Z.L., A.H., M.J., C.C., Y.L., Q.Z., M.Z.).
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Zhou Z, Qu Y, Zhou Y, Wang B, Hu W, Cao Y. Development and Validation of a CT-Based Radiomics Nomogram in Patients With Anterior Mediastinal Mass: Individualized Options for Preoperative Patients. Front Oncol 2022; 12:869253. [PMID: 35875092 PMCID: PMC9304864 DOI: 10.3389/fonc.2022.869253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/09/2022] [Indexed: 11/20/2022] Open
Abstract
Background To improve the preoperative diagnostic accuracy and reduce the non-therapeutic thymectomy rate, we established a comprehensive predictive nomogram based on radiomics data and computed tomography (CT) features and further explored its potential use in clinical decision-making for anterior mediastinal masses (AMMs). Methods A total of 280 patients, including 280 with unenhanced CT (UECT) and 241 with contrast-enhanced CT (CECT) scans, all of whom had undergone thymectomy for AMM with confirmed histopathology, were enrolled in this study. A total of 1,288 radiomics features were extracted from each labeled mass. The least absolute shrinkage and selection operator model was used to select the optimal radiomics features in the training set to construct the radscore. Multivariate logistic regression analysis was conducted to establish a combined clinical radiographic radscore model, and an individualized prediction nomogram was developed. Results In the UECT dataset, radscore and the UECT ratio were selected for the nomogram. The combined model achieved higher accuracy (AUC: 0.870) than the clinical model (AUC: 0.752) for the prediction of therapeutic thymectomy probability. In the CECT dataset, the clinical and combined models achieved higher accuracy (AUC: 0.851 and 0.836, respectively) than the radscore model (AUC: 0.618) for the prediction of therapeutic thymectomy probability. Conclusions In patients who underwent UECT only, a nomogram integrating the radscore and the UECT ratio achieved good accuracy in predicting therapeutic thymectomy in AMMs. However, the use of radiomics in patients with CECT scans did not improve prediction performance; therefore, a clinical model is recommended.
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Affiliation(s)
- Zhou Zhou
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yanjuan Qu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yurong Zhou
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Binchen Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Weidong Hu
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yiyuan Cao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
- *Correspondence: Yiyuan Cao,
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Zhang C, Yang Q, Lin F, Ma H, Zhang H, Zhang R, Wang P, Mao N. CT-Based Radiomics Nomogram for Differentiation of Anterior Mediastinal Thymic Cyst From Thymic Epithelial Tumor. Front Oncol 2021; 11:744021. [PMID: 34956869 PMCID: PMC8702557 DOI: 10.3389/fonc.2021.744021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
ObjectivesThis study aimed to distinguish preoperatively anterior mediastinal thymic cysts from thymic epithelial tumors via a computed tomography (CT)-based radiomics nomogram.MethodsThis study analyzed 74 samples of thymic cysts and 116 samples of thymic epithelial tumors as confirmed by pathology examination that were collected from January 2014 to December 2020. Among the patients, 151 cases (scanned at CT 1) were selected as the training cohort, and 39 cases (scanned at CT 2 and 3) served as the validation cohort. Radiomics features were extracted from pre-contrast CT images. Key features were selected by SelectKBest and least absolute shrinkage and selection operator and then used to build a radiomics signature (Rad-score). The radiomics nomogram developed herein via multivariate logistic regression analysis incorporated clinical factors, conventional CT findings, and Rad-score. Its performance in distinguishing the samples of thymic cysts from those of thymic epithelial tumors was assessed via discrimination, calibration curve, and decision curve analysis (DCA).ResultsThe radiomics nomogram, which incorporated 16 radiomics features and 3 conventional CT findings, including lesion edge, lobulation, and CT value, performed better than Rad-score, conventional CT model, and the clinical judgment by radiologists in distinguishing thymic cysts from thymic epithelial tumors. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.980 [95% confidence interval (CI), 0.963–0.993] in the training cohort and 0.992 (95% CI, 0.969–1.000) in the validation cohort. The calibration curve and the results of DCA indicated that the nomogram has good consistency and valuable clinical utility.ConclusionThe CT-based radiomics nomogram presented herein may serve as an effective and convenient tool for differentiating thymic cysts from thymic epithelial tumors. Thus, it may aid in clinical decision-making.
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Affiliation(s)
- Chengzhou Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Qinglin Yang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Ran Zhang
- Collaboration Department, Huiying Medical Technology, Beijing, China
| | - Ping Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
- *Correspondence: Ping Wang, ; Ning Mao,
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
- *Correspondence: Ping Wang, ; Ning Mao,
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Liu L, Lu F, Pang P, Shao G. Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas? Biomed Eng Online 2020; 19:89. [PMID: 33246468 PMCID: PMC7694435 DOI: 10.1186/s12938-020-00833-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 11/17/2020] [Indexed: 01/04/2023] Open
Abstract
Background Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas. Methods A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve. Results Two radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model). Conclusions The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.
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Affiliation(s)
- Lulu Liu
- Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Zhejiang Cancer Hospital, No. 1 Banshan Street, Gongshu District, Hangzhou, 321022, Zhejiang, People's Republic of China
| | - Fangxiao Lu
- Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Zhejiang Cancer Hospital, No. 1 Banshan Street, Gongshu District, Hangzhou, 321022, Zhejiang, People's Republic of China
| | - Peipei Pang
- Life Sciences, GE Healthcare, Hangzhou, 310000, Zhejiang, China
| | - Guoliang Shao
- Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China. .,Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, Hangzhou, China. .,Department of Radiology, Zhejiang Cancer Hospital, No. 1 Banshan Street, Gongshu District, Hangzhou, 321022, Zhejiang, People's Republic of China.
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Park JW, Jeong WG, Lee JE, Lee HJ, Ki SY, Lee BC, Kim HO, Kim SK, Heo SH, Lim HS, Shin SS, Yoon W, Jeong YY, Kim YH. Pictorial Review of Mediastinal Masses with an Emphasis on Magnetic Resonance Imaging. Korean J Radiol 2020; 22:139-154. [PMID: 32783412 PMCID: PMC7772375 DOI: 10.3348/kjr.2019.0897] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 05/03/2020] [Accepted: 05/09/2020] [Indexed: 12/19/2022] Open
Abstract
Magnetic resonance imaging (MRI) has become a crucial tool for evaluating mediastinal masses considering that several lesions that appear indeterminate on computed tomography and radiography can be differentiated on MRI. Using a three-compartment model to localize the mass and employing a basic knowledge of MRI, radiologists can easily diagnose mediastinal masses. Here, we review the use of MRI in evaluating mediastinal masses and present the images of various mediastinal masses categorized using the International Thymic Malignancy Interest Group's three-compartment classification system. These masses include thymic hyperplasia, thymic cyst, pericardial cyst, thymoma, mediastinal hemangioma, lymphoma, mature teratoma, bronchogenic cyst, esophageal duplication cyst, mediastinal thyroid carcinoma originating from ectopic thyroid tissue, mediastinal liposarcoma, mediastinal pancreatic pseudocyst, neurogenic tumor, meningocele, and plasmacytoma.
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Affiliation(s)
- Jin Wang Park
- Department of Radiology, Chonnam National University Hospital, Gwangju, Korea
| | - Won Gi Jeong
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea.
| | - Jong Eun Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, Korea
| | - Hyo Jae Lee
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | - So Yeon Ki
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | - Byung Chan Lee
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | - Hyoung Ook Kim
- Department of Radiology, Chonnam National University Hospital, Gwangju, Korea
| | - Seul Kee Kim
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea.,Department of Radiology, Chonnam National University Medical School, Gwangju, Korea
| | - Suk Hee Heo
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea.,Department of Radiology, Chonnam National University Medical School, Gwangju, Korea
| | - Hyo Soon Lim
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea.,Department of Radiology, Chonnam National University Medical School, Gwangju, Korea
| | - Sang Soo Shin
- Department of Radiology, Chonnam National University Hospital, Gwangju, Korea.,Department of Radiology, Chonnam National University Medical School, Gwangju, Korea
| | - Woong Yoon
- Department of Radiology, Chonnam National University Hospital, Gwangju, Korea.,Department of Radiology, Chonnam National University Medical School, Gwangju, Korea
| | - Yong Yeon Jeong
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea.,Department of Radiology, Chonnam National University Medical School, Gwangju, Korea
| | - Yun Hyeon Kim
- Department of Radiology, Chonnam National University Hospital, Gwangju, Korea.,Department of Radiology, Chonnam National University Medical School, Gwangju, Korea
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