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

作者：Nengfeng Yu, Jiali Li, Dan Cao, Xingbei Chen, Dong Yang, Nan Jiang, Junhui Wu, Chenkai Zhao, Yichun Zheng, Yi‐Cheng Chen, Xiaodong Jin · 发表于：International Journal of Surgery · 年份：2025 · DOI：10.1097/js9.0000000000003140 · 被引用次数：3 · 研究领域：Bladder and Urothelial Cancer Treatments、Radiomics and Machine Learning in Medical Imaging、Cardiovascular Disease and Adiposity

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 furthe...