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PURPOSE: Bladder cancer recurrence occurs in 40% of patients following radical cystectomy (RC) and pelvic lymphadenectomy (PLND). Although recurrence can be reduced with adjuvant chemotherapy, the toxicity and low response rates of this treatment restrict its use to patients at highest risk. We developed a neurofuzzy model (NFM) to predict disease recurrence following RC and PLND in patients who are not usually administered adjuvant chemotherapy. EXPERIMENTAL DESIGN: The study comprised 1,034 patients treated with RC and PLND for bladder urothelial carcinoma. Four hundred twenty-five patients were excluded due to lymph node metastases and/or administration of chemotherapy. For the remaining 609 patients, we obtained complete clinicopathologic data relating to their tumor. We trained, tested, and validated two NFMs that predicted risk (Classifier) and timing (Predictor) of post-RC recurrence. We measured the accuracy of our model at various postoperative time points. RESULTS: Cancer recurrence occurred in 172 (28%) patients. With a median follow-up of 72.7 months, our Classifier NFM identified recurrence with an accuracy of 0.84 (concordance index 0.92, sensitivity 0.81, and specificity 0.85) and an excellent calibration. This was better than two predictive nomograms (0.72 and 0.74 accuracies). The Predictor NFMs identified the timing of tumor recurrence with a median error of 8.15 months. CONCLUSIONS: We have developed an accurate and well-calibrated model to identify disease recurrence following RC and PLND in patients with nonmetastatic bladder urothelial carcinoma. It seems superior to other available predictive methods and could be used to identify patients who would potentially benefit from adjuvant chemotherapy.

Original publication




Journal article


Clin Cancer Res

Publication Date





3150 - 3155


Adult, Aged, Aged, 80 and over, Carcinoma, Transitional Cell, Cystectomy, Female, Fuzzy Logic, Humans, Male, Middle Aged, Models, Statistical, Neoplasm Recurrence, Local, Neoplasm Staging, Neural Networks, Computer, Prognosis, Risk Assessment, Risk Factors, Treatment Outcome, Urinary Bladder Neoplasms