A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

BACKGROUND: Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients. PURPOSE: To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk. STUDY TYPE: Retrospective. POPULATION: Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21). FIELD STRENGTH/SEQUENCE: W), multi-b-value diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences. ASSESSMENT: W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine-based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle-invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics-clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve. STATISTICAL TESTS: Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction. RESULTS: Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics-clinical nomogram than using the radiomics or clinical model alone. DATA CONCLUSION: The proposed radiomics-clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893-1904.