Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types.

ABSTRACT:

Background

Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with PARP inhibitors (PARPi). Despite its clinical significance, HRD testing is highly complex. Here, we investigated in a proof-of-concept study whether Deep Learning (DL) can predict HRD status solely based on routine hematoxylin & eosin (H&E) histology images across nine different cancer types.

Methods

We developed a deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. As part of our approach, we calculated a genomic scar HRD score by combining loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transit

SUBMITTER: Loeffler CML 

PROVIDER: S-EPMC11462727 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature