CT-derived tumor extracellular volume fraction for predicting recurrence in non-muscle-invasive bladder cancer.

PURPOSE: To investigate the predictive value of CT-derived tumor extracellular volume fraction (ECV) for recurrence in non-muscle-invasive bladder cancer (NMIBC) and to develop a comprehensive clinical prediction model.

METHODS

This retrospective study included 97 patients with pathologically confirmed NMIBC treated between July 2017 and December 2022. All patients underwent pre-treatment contrast-enhanced CT scans, including unenhanced and delayed phases. Tumor ECV was independently measured by two radiologists. Clinical variables were retrieved from medical records. The primary outcome was tumor recurrence, confirmed by cystoscopy or imaging during follow-up. Univariable analysis identified candidate predictors (P < 0.1), and correlation testing was used to exclude multicollinearity. A logistic regression model was developed using 5-fold stratified cross-validation. Model performance was assessed using the area under the ROC curve (AUC), calibration curves, and recurrence-free survival estimated by Kaplan-Meier analysis.

RESULTS

The recurrence rate was 34.0% over a median follow-up of 38.8 months. ECV values were significantly higher in recurrent cases (35.1 ± 11.4%) than in non-recurrent cases (30.7 ± 7.1%, P = 0.024). ECV independently predicted recurrence (OR = 1.072, 95% CI: 1.008-1.140; P = 0.028). Five variables-gender, tumor location, ECV, resection type, and intravesical chemotherapy-were identified as significant predictors. The model achieved an AUC of 0.722 ± 0.051 with good calibration. Cox proportional hazards regression demonstrated that higher ECV was significantly associated with recurrence-free survival in transurethral resection of bladder tumor patients (HR = 1.031, 95% CI: 1.002-1.061, P = 0.036).

CONCLUSION

CT-derived ECV is a promising imaging biomarker for recurrence prediction in NMIBC. The integrated model provides potential risk stratification and supports personalized clinical decision-making.