Statistical Analysis of Survival Models Using Feature Quantification on Prostate Cancer Histopathological Images.

ABSTRACT:

Background

Grading of prostatic adenocarcinoma is based on the Gleason scoring system and the more recently established prognostic grade groups. Typically, prostate cancer grading is performed by pathologists based on the morphology of the tumor on hematoxylin and eosin (H and E) slides. In this study, we investigated the histopathological image features with various survival models and attempted to study their correlations.

Methods

Three texture methods (speeded-up robust features, histogram of oriented gradient, and local binary pattern) and two convolutional neural network (CNN)-based methods were applied to quantify histopathological image features. Five survival models were assessed on those image features in the context with other prostate clinical prognostic factors,

SUBMITTER: Ren J 

PROVIDER: S-EPMC6788183 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature