Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches.

ABSTRACT:

Background

One of the most challenging tasks for bladder cancer diagnosis is to histologically differentiate two early stages, non-invasive Ta and superficially invasive T1, the latter of which is associated with a significantly higher risk of disease progression. Indeed, in a considerable number of cases, Ta and T1 tumors look very similar under microscope, making the distinction very difficult even for experienced pathologists. Thus, there is an urgent need for a favoring system based on machine learning (ML) to distinguish between the two stages of bladder cancer.

Methods

A total of 1177 images of bladder tumor tissues stained by hematoxylin and eosin were collected by pathologists at University of Rochester Medical Center, which included 460 non-invasive (stage Ta) and 7

SUBMITTER: Yin PN 

PROVIDER: S-EPMC7367328 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature