ArticleNature cancer2024
A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.
Article in Nature cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 1 of them a synthesis that pooled it.
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Who cites it
92 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Artificial intelligence in onco-anaesthesia: Current applications, challenges, and future directions.World journal of methodology · 2026Review
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- DeepPathway: predicting pathway expression from histopathology images.Bioinformatics (Oxford, England) · 2026Article
- Mycoplasma infection drives EGFR-TKI resistance through convergent transcriptomic reprogramming in non-small cell lung cancer.iScience · 2026Article
- ALPaCA: Adapting Llama for Pathology Context Analysis to enable slide-level question answering.Nature communications · 2026Article
- Generalizable AI predicts immunotherapy outcomes across cancers and treatments.Nature medicine · 2026Article
- SPP1-driven spatial niche remodelling defines aggressive progression in the basal subtype of upper tract urothelial carcinoma.Clinical and translational medicine · 2026Article
- Prognostic RNA-splicing archetypes in breast cancer identified by extended pre-training of histopathology foundation models.Nature communications · 2026Article
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Single cell spatial transcriptomics track the evolutionary hierarchy and microenvironment remodeling during breast carcinoma invasion.Nature communications · 2026Article
- Causal inference and digital twins: a roadmap for the future of clinical trials.NPJ digital medicine · 2026Review
- Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma.Journal of ovarian research · 2026Article
- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis.NPJ digital medicine · 2026Article
- Applications and prospects of artificial intelligence and digital medicine in pediatric nasal skull base tumors.Pediatric investigation · 2026Review
- Spatially resolved transcriptomic and proteomic profiling reveals cell interaction programs that predict Barrett's esophagus progression.bioRxiv : the preprint server for biology · 2026Article
- Prediction of OncotypeDX recurrence score using hematoxylin and eosin-stained whole slide images.NPJ breast cancer · 2026Article
- Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Systematically decoding pathological morphologies and molecular profiles with unified multimodal embedding.Nature methods · 2026Article
32 more citing papers are in PubMed but not listed here.
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31 authors.
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Abstract
Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.