A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer.

ABSTRACT:

Background

The current study aimed to develop a deep learning (DL) model for prediction of lymph node metastasis (LNM) based on hematoxylin and eosin (HE)-stained histopathological images of endometrial cancer (EC). The model was validated using external data.

Methods

A total of 2,104 whole slide image (WSI) from 564 patients with pathologically confirmed LNM status were collated from West China Second University Hospital. An artificial intelligence (AI) model was built on the multiple instance-learning (MIL) framework for automatic prediction of the probability of LNM and its performance compared with "Mayo criteria". An additional external data source comprising 533 WSI was collected from two independent medical institutions to validate the model's robustness. Heatmaps wer

SUBMITTER: Feng M 

PROVIDER: S-EPMC10006118 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature