Development and validation of MRI-based models to predict lymph node metastasis in bladder cancer: a multi-center study.

BACKGROUND: Accurate preoperative evaluation of lymph node (LN) status is crucial for guiding precise treatment. Although several methods have been developed for preoperative evaluation of LN status, accurate evaluation remains a challenge. OBJECTIVE: To develop and validate MRI-based clinical prediction models for the preoperative assessment of lymph node metastasis (LNM) in patients with very high risk non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). MATERIALS AND METHODS: A total of 468 eligible patients with very high risk NMIBC and MIBC who underwent radical cystectomy and pelvic lymph node dissection from four hospitals were enrolled (derivation cohort, n=247; internal validation cohort, n=106; external validation cohort, n=115). Preoperative clinical characteristics were reviewed to identify significant predictors for constructing a nomogram and an integer-based model. The diagnostic performance of the nomogram and the integer-based model was compared using accuracy, sensitivity, specificity, and area under the curve (AUC). Kaplan–Meier survival curves were conducted to analyze the prognosis of patients. RESULTS: Multivariable analysis revealed that Node-RADS score and VI-RADS score were independent predictive factors for LNM. The nomogram based on these variables achieved an AUC of 0.885 for predicting LNM. An integer-based scoring system (NV score) was established, with an AUC of 0.883. Delong’s test indicated no significant difference between two models (P=0.535). The NV score demonstrated excellent discrimination in both internal (AUC: 0.930) and external (AUC: 0.924) validation cohorts. The optimal cutoff value of the NV score for predicting LNM in all patients was determined to be >3. Patients with high-LNM risk had significantly worse overall survival (HR=3.473) and recurrence-free survival (HR=4.400). CONCLUSION: The MRI-based NV score can preoperatively assess LNM status and predict postoperative prognosis in BCa patients.