Yaping Su1, Xiaolin Xu2, Panli Zuo3, Yuwei Xia3, Xiaoxia Qu1, Qinghua Chen1, Jian Guo1, Wenbin Wei4, Junfang Xian5. 1. Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China; Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China. 2. Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China; Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Stree, Dongcheng District, Beijing, 100730, China; Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing 100730, China. 3. Huiying Medical Technology Co., Ltd., Beijing, 100192, China. 4. Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China; Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Stree, Dongcheng District, Beijing, 100730, China; Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing 100730, China. Electronic address: weiwenbintr@163.com. 5. Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China; Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China. Electronic address: cjr.xianjunfang@vip.163.com.
Abstract
PURPOSE: To assess the performance of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics analysis for discriminating between uveal melanoma (UM) and other intraocular masses. METHODS: This retrospective study analyzed 245 patients with intraocular masses (165 UMs and 80 other intraocular masses). Radiomics features were extracted from T1WI, T2WI, and contrast enhanced T1-weighted images (CET1WI), respectively. The intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features. The training and test sets consisted of 195 and 50 cases. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM). The performance of classifiers was evaluated by ROC analysis, and was compared to the performance of visual assessment by DeLong test. RESULTS: The optimal radiomics feature set was 10, 15, 15, and 24 for T1W, T2W, CET1W, and joint T2W and CET1W images, respectively. The accuracy of T1WI, T2WI, CET1WI, and the joint T2WI and CET1WI models ranged from 72.0 %-78.0 %, from 79.6 %-81.6 %, from 74.0 %-82.0 %, and from 76.0 %-86.0 % in the test set. In the test set, the AUC for T1WI, T2WI, CET1WI, joint T2WI, and CET1WI models ranged from 0.775 to 0.829, 0.816 to 0.826, 0.836 to 0.861, and 0.870 to 0.877, respectively. In the combined model, the performance of ML classifiers was better than the performance of visual assessment in the training set and in all patients (p＜0.05). CONCLUSIONS: Radiomics analysis represents a promising tool in separating UM from other intraocular masses.
PURPOSE: To assess the performance of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics analysis for discriminating between uveal melanoma (UM) and other intraocular masses. METHODS: This retrospective study analyzed 245 patients with intraocular masses (165 UMs and 80 other intraocular masses). Radiomics features were extracted from T1WI, T2WI, and contrast enhanced T1-weighted images (CET1WI), respectively. The intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features. The training and test sets consisted of 195 and 50 cases. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM). The performance of classifiers was evaluated by ROC analysis, and was compared to the performance of visual assessment by DeLong test. RESULTS: The optimal radiomics feature set was 10, 15, 15, and 24 for T1W, T2W, CET1W, and joint T2W and CET1W images, respectively. The accuracy of T1WI, T2WI, CET1WI, and the joint T2WI and CET1WI models ranged from 72.0 %-78.0 %, from 79.6 %-81.6 %, from 74.0 %-82.0 %, and from 76.0 %-86.0 % in the test set. In the test set, the AUC for T1WI, T2WI, CET1WI, joint T2WI, and CET1WI models ranged from 0.775 to 0.829, 0.816 to 0.826, 0.836 to 0.861, and 0.870 to 0.877, respectively. In the combined model, the performance of ML classifiers was better than the performance of visual assessment in the training set and in all patients (p＜0.05). CONCLUSIONS: Radiomics analysis represents a promising tool in separating UM from other intraocular masses.
Authors: Victor I J Strijbis; Christiaan M de Bloeme; Robin W Jansen; Hamza Kebiri; Huu-Giao Nguyen; Marcus C de Jong; Annette C Moll; Merixtell Bach-Cuadra; Pim de Graaf; Martijn D Steenwijk Journal: Sci Rep Date: 2021-07-16 Impact factor: 4.379