Prediction of Non-Muscle Invasive Bladder Cancer Recurrence using Machine Learning of Quantitative Nuclear Features

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Publication Date: 10/29/2021

Event: Modern Pathology

Reference: pp. 1-6, 2021

Authors: Naoto Tokuyama, Tokyo Medical University; Akira Saito, Tokyo Medical University; Ryu Muraoka, Tokyo Medical University; Shuya Matsubara, Tokyo Medical University; Takeshi Hashimoto, Tokyo Medical University; Naoya Satake, Tokyo Medical University; Jun Matsubayashi, Tokyo Medical University.; Toshitaka Nagao, Tokyo Medical University.; Aashiq H. Mirza, Tokyo Medical University, Weill Cornell Medicine; Hans Peter Graf, NEC Laboratories America, Inc.; Eric Cosatto, NEC Laboratories America, Inc.; Chin-Lee Wu, Massachusetts General Hospital; Masahiko Kuroda, Tokyo Medical University, Tokyo Medical University; Yoshio Ohno, Tokyo Medical University

Abstract: Non-muscle invasive bladder cancer (NMIBC) generally has a good prognosis, however, recurrence after transurethral resection (TUR), the standard primary treatment, is a major problem. Clinical management after TUR has been based on risk classification using clinicopathological factors, but these classifications are not complete. In this study, we attempted to predict early recurrence of NMIBC based on machine learning of quantitative morphological features. In general, structural, cellular, and nuclear atypia are evaluated to determine cancer atypia. However, since it is difficult to accurately quantify structural atypia from TUR specimens, in this study, we used only nuclear atypia and analyzed it using feature extraction followed by classification using Support Vector Machine and Random Forest machine learning algorithms. For the analysis, 125 patients diagnosed with NMIBC were used, data from 95 patients were randomly selected for the training set, and data from 30 patients were randomly selected for the test set. The results showed that the support vector machine-based model predicted recurrence within 2 years after TUR with a probability of 90% and the random forest-based model with probability of 86.7%. In the future, the system can be used to objectively predict NMIBC recurrence after TUR.

Publication Link: https://www.nature.com/articles/s41379-021-00955-y