Probabilistic neural network analysis of quantitative nuclear features in predicting the risk of cancer recurrence at different follow-up times

Abstract

This paper explores the prognostic significance of automatically generated features from biopsies of superficial transitional cell carcinomas (TCCs) of urinary bladder in predicting the time interval in which the tumor is more likely to recur. Clinical material comprised 73 patients diagnosed with superficial TCC and followed-up for at least 60 months. Patients' data set was separated into three prognostic groups in respect to time interval in which the tumor may recur. For each case, 40 descriptive quantitative features related to nuclear characteristics were generated. The prognostic value of the estimated features was analyzed by means of a probabilistic neural network (PNN) classifier. To find best vector combination leading to the smallest classification error, an exhaustive search procedure in feature space was utilized. Classifier performance was evaluated by means of the leave-one-out method. Throughout the analysis of the prognostic feature combinations, four features, two describing nuclear texture, and two related to shape distribution of nuclei in the sample, were identified as the important markers for patients' outcome prediction. The overall predictive accuracy was 73%. Intermediate and long-term recurrent cases were identified with an accuracy of 76%. For the short-term group the predictive accuracy was 63%. The detection of recurrences in certain future time seems feasible by analyzing the prognostic information of nuclear features and incorporating the PNN model. The improvement in prognostic ability may be clinically important for patient follow-up, and therapeutic treatment.