ReviewJournal of personalized medicine2022
Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review.
Review in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Artificial intelligence-assisted spatial omics-based biomimetic nanoplatform for intelligent and precise intervention in the immunosuppressive core region of ovarian cancer.NPJ precision oncology · 2026Review
- Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.European journal of nuclear medicine and molecular imaging · 2026Article
- Optimized CNN framework with VGG19, EfficientNet, and Bayesian optimization for early colon cancer detection.Scientific reports · 2026Article
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- Non-invasive quantification of viability in liver spheroids using deep learning.Frontiers in bioengineering and biotechnology · 2026Article
- A multimodal framework to identify molecular mechanisms driving patient group-associated morphology through the integration of spatial transcriptomics and whole slide imaging.NPJ artificial intelligence · 2026Article
- ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images.Nature communications · 2025Article
- Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach.NPJ precision oncology · 2025Article
- An international study of stain variability in histopathology using qualitative and quantitative analysis.Journal of pathology informatics · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- A review on federated learning in computational pathology.Computational and structural biotechnology journal · 2024Review
- Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images.Breast cancer research : BCR · 2024Article
- Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.Nature communications · 2024Article
- Identifying Patients with CSF-Venous Fistula Using Brain MRI: A Deep Learning Approach.AJNR. American journal of neuroradiology · 2024Article
- Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.Frontiers in immunology · 2024Article
- Immune Biomarkers in Triple-Negative Breast Cancer: Improving the Predictivity of Current Testing Methods.Journal of personalized medicine · 2023Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.
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Registered trials
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.