Transcriptomics
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Entries
ENLIGHT-DeepPT
PUBMED_LINK
FULL NAME
ENLIGHT-DeepPT — Deep-Learning Framework for Cancer Treatment Response from Histopathology Images
DESCRIPTION
ENLIGHT-DeepPT (Deep Phenotyping of Tumors) is a deep-learning framework (ResNet50 + MLP) that predicts genome-wide tumor mRNA expression from routine H&E histopathology images across 16 TCGA cancer types. The imputed transcriptomics then drive treatment response prediction, achieving odds ratio of 2.28 across 5 independent treatment cohorts. Directly links medical imaging (histopathology) with genomics/transcriptomics via AI, enabling precision oncology from standard pathology slides.
TITLE
A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.
Main citation
Hoang DT, Shulman ED, Shuaib M, Nguyen JD, Maqbool HH, Nguyen Q, Iyer P, Liu S, Ruppin E, Stone EA. (2024) A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics. Nature Cancer, 5(9):1305-1317. doi:10.1038/s43018-024-00793-2. PMID 38961276
ABSTRACT
Predicting cancer treatment response from routinely collected clinical material is a central challenge in precision oncology. Here we present ENLIGHT-DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from routine H&E histopathology images. Using a two-stage approach (image-to-transcriptomics via ResNet50 + MLP, then transcriptomics-to-treatment response), ENLIGHT-DeepPT achieves an odds ratio of 2.28 across 5 independent treatment cohorts spanning multiple cancer types and drug classes.
DOI
10.1038/s43018-024-00793-2