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  • Title: MRI-Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer.
    Author: Ma S, Xie H, Wang H, Han C, Yang J, Lin Z, Li Y, He Q, Wang R, Cui Y, Zhang X, Wang X.
    Journal: J Magn Reson Imaging; 2019 Dec; 50(6):1914-1925. PubMed ID: 31062459.
    Abstract:
    BACKGROUND: Radiomics approaches based on multiparametric MRI (mp-MRI) have shown high accuracy in prostate cancer (PCa) management. However, there is a need to apply radiomics to the preoperative prediction of extracapsular extension (ECE). PURPOSE: To develop and validate a radiomics signature to preoperatively predict the probability of ECE for patients with PCa, compared with the radiologists' interpretations. STUDY TYPE: Retrospective. POPULATION: In total, 210 patients with pathology-confirmed ECE status (101 positive, 109 negative) were enrolled. FIELD STRENGTH/SEQUENCE: T2 -weighted imaging (T2 WI), diffusion-weighted imaging, and dynamic contrast-enhanced imaging were performed on two 3.0T MR scanners. ASSESSMENT: A radiomics signature was constructed to predict the probability of ECE prior to radical prostatectomy (RP). In all, 17 stable radiomics features of 1619 extracted features based on T2 WI were selected. The same images were also evaluated by three radiologists. The predictive performance of the radiomics signature was validated and compared with radiologists' interpretations. STATISTICAL TESTS: A radiomics signature was developed by a least absolute shrinkage and selection operator (LASSO) regression algorithm. Samples enrolled were randomly divided into two groups (143 for training and 67 for validation). Discrimination, calibration, and clinical usefulness were validated by analysis of the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve, respectively. The predictive performance was then compared with visual assessments of three radiologists. RESULTS: The radiomics signature yielded an AUC of 0.902 and 0.883 in the training and validation cohort, respectively, and outperformed the visual assessment (AUC: 0.600-0.697) in the validation cohort. Pairwise comparisons demonstrated that the radiomics signature was more sensitive than the radiologists (75.00% vs. 46.88%-50.00%, all P < 0.05), but obtained comparable specificities (91.43% vs. (88.57%-94.29%); P ranged from 0.64-1.00). DATA CONCLUSION: A radiomics signature was developed and validated that outperformed the radiologists' visual assessments in predicting ECE status. LEVEL OF EVIDENCE: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1914-1925.
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