Towards Digital Quantification of Ploidy from Pan-Cancer Digital Pathology Slides using Deep Learning.

ABSTRACT: Abnormal DNA ploidy, found in numerous cancers, is increasingly being recognized as a contributor in driving chromosomal instability, genome evolution, and the heterogeneity that fuels cancer cell progression. Furthermore, it has been linked with poor prognosis of cancer patients. While next-generation sequencing can be used to approximate tumor ploidy, it has a high error rate for near-euploid states, a high cost and is time consuming, motivating alternative rapid quantification methods. We introduce PloiViT, a transformer-based model for tumor ploidy quantification that outperforms traditional machine learning models, enabling rapid and cost-effective quantification directly from pathology slides. We trained PloiViT on a dataset of fifteen cancer types from The Cancer Genome Atlas and va

SUBMITTER: Carrillo-Perez F 

PROVIDER: S-EPMC11370345 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature