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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/3837

Title: Neurofuzzy modeling to determine recurrence risk following radical cystectomy for nonmetastatic urothelial carcinoma of the bladder
Authors: Catto, JWF
Abbod, MF
Linkens, DA
Larre, S
Rosario, DJ
Hamdy, FC
Publication Date: 2009
Publisher: American Association for Cancer Research
Citation: Clinical Cancer Research. 15 (9) 3150-3155
Abstract: 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 neurofuzzymodel (NFM) to predict disease recurrence following RC and PLNDin 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 NFMidentified recurrence with an accuracy of 0.84 (concordance index 0.92, sensitivity 0.81, and specificity 0.85) and an excellent calibration.Thiswas better than two predictive nomograms (0.72 and 0.74 accuracies). The Predictor NFMs identified the timing of tumor recurrencewith 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 predictivemethods and could be used to identify patientswho would potentially benefit from adjuvant chemotherapy.
URI: http://bura.brunel.ac.uk/handle/2438/3837
Appears in Collections:School of Engineering and Design Research papers
Cancer

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