CT-based radiomics signature of visceral adipose tissue for prediction of early recurrence in patients with NMIBC: a multicentre cohort study

Introduction: The objective of this study is to investigate the predictive ability of abdominal fat features derived from computed tomography (CT) to predict early recurrence within a year following the initial transurethral resection of bladder tumor (TURBT) in patients with non-muscle-invasive bladder cancer (NMIBC). A predictive model is constructed in combination with clinical factors to aid in the evaluation of the risk of early recurrence among patients with NMIBC after initial TURBT. Methods: This retrospective study enrolled 325 NMIBC patients from three centers. Machine learning–based visceral adipose tissue (VAT) radiomics models (VAT-RM) and subcutaneous adipose tissue (SAT) radiomics models (SAT-RM) were constructed to identify patients with early recurrence. A combined model integrating VAT-RM and clinical factors was established. The predictive performance of each variable and model was analyzed using the area under the receiver operating characteristic curve (AUC). The net benefit of each variable and model was presented through decision curve analysis (DCA). The calibration was evaluated utilizing the Hosmer–Lemeshow test. Findings: The VAT-RM demonstrated satisfactory performance in the training cohort (AUC = 0.853; 95% CI: 0.768–0.937), test cohort 1 (AUC = 0.823; 95% CI: 0.730–0.916), and test cohort 2 (AUC = 0.808; 95% CI: 0.681–0.935). Across all cohorts, the AUC values of the VAT-RM were higher than those of the SAT-RM ( P < 0.001). The DCA curves further confirmed that the clinical net profit of the VAT-RM was superior to that of the SAT-RM. In multivariate logistic regression analysis, the VAT-RM emerged as the most significant independent predictor [odds ratio (OR) = 0.295; 95% CI: 0.141–0.508; P < 0.001]. The fusion model exhibited excellent AUC values of 0.938, 0.909, and 0.905 across three cohorts. The fusion model surpassed the traditional risk assessment frameworks in both predictive efficacy and clinical net benefit. Interpretation: VAT serves as a crucial factor in early postoperative recurrence in NMIBC patients. The VAT-RM can accurately identify high-risk patients with early postoperative recurrence, offering significant advantages over SAT-RM. The new predictive model constructed by integrating the VAT-RM and clinical factors exhibits excellent predictive performance, clinical net benefits, and calibration accuracy.