Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks.

ABSTRACT:

Background

Ovarian cancer causes 151,900 deaths per year worldwide. Treatment and prognosis are primarily determined by the histopathologic interpretation in combination with molecular diagnosis. However, the relationship between histopathology patterns and molecular alterations is not fully understood, and it is difficult to predict patients' chemotherapy response using the known clinical and histological variables.

Methods

We analyzed the whole-slide histopathology images, RNA-Seq, and proteomics data from 587 primary serous ovarian adenocarcinoma patients and developed a systematic algorithm to integrate histopathology and functional omics findings and to predict patients' response to platinum-based chemotherapy.

Results

Our convolutional neural networks identified

SUBMITTER: Yu KH 

PROVIDER: S-EPMC7433108 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature