Characteristics of Abdominal Fat Based on CT Measurements to Predict Early Recurrence After Initial Surgery of NMIBC in Stage Ta/T1.

INTRODUCTION

This study aimed to assess the predictive value of abdominal fat characteristics measured by computed tomography (CT) in identifying early recurrence within one year post-initial transurethral resection of bladder tumor (TURBT) in patients with nonmuscle-invasive bladder cancer (NMIBC). A predictive model integrating fat features and clinical factors was developed to guide individualized treatment.

MATERIALS AND

METHODS

A retrospective analysis of 203 NMIBC patients from two medical centers was conducted. Abdominal CT images were analyzed using 3D Slicer software. Spearman correlation, logistic regression, and the Lasso algorithm were employed for data analysis. Predictive efficacy was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) and decision curve analysis (DCA). Calibration was evaluated using the Hosmer-Lemeshow test.

RESULTS

Significant differences in abdominal fat characteristics were found between the recurrence and nonrecurrence groups. All fat features positively correlated with body mass index (BMI), with bilateral perirenal fat thickness (PrFT) showing superior predictive performance. Multivariate logistic regression identified independent predictors of early recurrence, including tumor number, early perfusion chemotherapy, left and right PrFT, and visceral fat area (VFA) at umbilical and renal hilum levels. The Lasso-based model achieved an AUC of 0.904, outperforming existing models.

CONCLUSION

Abdominal fat characteristics, especially bilateral PrFT, strongly correlate with early recurrence in NMIBC. The Lasso-based model, integrating fat and clinical factors, offers superior predictive efficacy and could improve individualized treatment strategies.