Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence.

ABSTRACT:

Background

Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm.

Methods

A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent featur

SUBMITTER: Zhou X 

PROVIDER: S-EPMC11658449 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature