Xiaopan Xu1, Xi Zhang1, Qiang Tian2, Huanjun Wang3, Long-Biao Cui4,5, Shurong Li3, Xing Tang4, Baojuan Li1, Jose Dolz6, Ismail Ben Ayed6, Zhengrong Liang7, Jing Yuan8, Peng Du1, Hongbing Lu1, Yang Liu1. 1. School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, PR China. 2. Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, PR China. 3. Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, Guangdong, PR China. 4. Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, PR China. 5. School of Medical Psychology, Fourth Military Medical University, Xi'an, Shaanxi, PR China. 6. LIVIA Laboratory, École de technologie supérieure (ETS), Montreal, QC, Canada. 7. Department of Radiology, School of Computer Science and Biomedical Engineering, State University of New York, Stony Brook, New York, USA. 8. Mathematics and Statistics School Xidian University, Xi'an, Shaanxi, PR China.
Abstract
BACKGROUND: Preoperative discrimination between nonmuscle-invasive bladder carcinomas (NMIBC) and the muscle-invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). PURPOSE: To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. STUDY TYPE: Retrospective, radiomics. POPULATION: Fifty-four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. FIELD STRENGTH/SEQUENCE: 3.0T MRI/T2 -weighted (T2 W) and multi-b-value diffusion-weighted (DW) sequences. ASSESSMENT: A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T2 W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. STATISTICAL TESTS: Chi-square test and Student's t-test were applied on clinical characteristics to analyze the significant differences between patient groups. RESULTS: Of the 1104 features, an optimal subset involving 19 features was selected from T2 W and DW sequences, which outperformed the other two subsets selected from T2 W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM-RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. DATA CONCLUSION: The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T2 W sequence or DW sequence only. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489-1498.
BACKGROUND: Preoperative discrimination between nonmuscle-invasive bladder carcinomas (NMIBC) and the muscle-invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). PURPOSE: To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. STUDY TYPE: Retrospective, radiomics. POPULATION: Fifty-four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. FIELD STRENGTH/SEQUENCE: 3.0T MRI/T2 -weighted (T2 W) and multi-b-value diffusion-weighted (DW) sequences. ASSESSMENT: A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T2 W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. STATISTICAL TESTS: Chi-square test and Student's t-test were applied on clinical characteristics to analyze the significant differences between patient groups. RESULTS: Of the 1104 features, an optimal subset involving 19 features was selected from T2 W and DW sequences, which outperformed the other two subsets selected from T2 W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM-RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. DATA CONCLUSION: The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T2 W sequence or DW sequence only. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489-1498.