Prediction of non-muscle invasive bladder cancer recurrence using machine learning of quantitative nuclear features.

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

SUBMITTER: Tokuyama N 

PROVIDER: S-EPMC8964412 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature