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|Title: ||Neurofuzzy modeling to determine recurrence risk following radical cystectomy for nonmetastatic urothelial carcinoma of the bladder|
|Authors: ||Catto, JWF|
|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.|
|Appears in Collections:||Electronic and Computer Engineering|
Dept of Electronic and Computer Engineering Research Papers
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