AB012. Automated histologic subtyping of thymic epithelial tumors with deep learning

ABSTRACT:

Background

Rare tumors are diagnostic challenges for pathologists. Thymic epithelial tumors (TETs) are heterogenous and their treatment strategies vary according to histological subgroup. Previous work has shown that a second pathological opinion may result in a change in diagnosis for more than half of cases, with a potential treatment shift in 44%. The aim of this study is to assess the feasibility of using artificial intelligence and deep learning to classify TETs, which could be used to help improve pathologist diagnostic consistency for these challenging tumors.

Methods

Digital diagnostic hematoxylin and eosin (H&E) stained slides of tumors for 103 patients with thymoma type A, AB, B1, B2, and B3 were downloaded from The Cancer Genome Atlas (TCGA). An Xception-based

SUBMITTER: Dolezal J 

PROVIDER: S-EPMC9792820 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature