ReviewGenome medicine2024
Deep learning in cancer genomics and histopathology.
Review in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
59 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Research on machine learning-based clinical prediction models: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
- Advancements in Image-Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review.BioMed research international · 2025Pooled it
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
- Gene-structured histology for deriving and predicting pancreatic cancer molecular subtypes.Signal transduction and targeted therapy · 2026Article
- Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.Cancer research · 2026Article
- Artificial Intelligence-Powered Histopathology in Stem Cell Research: Bridging Morphology, Function, and Omics.Current issues in molecular biology · 2026Review
- Experimental Detection Methods and Clinical Translational Challenges of Disulfidptosis in Cancer.Cells · 2026Review
- Awareness, Educational Needs, and Curriculum Preferences Regarding AI and Medical Big Data Education Among Clinical Medicine Undergraduates: Cross-Sectional Survey Study.JMIR formative research · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Automatic segmentation-based radiomics and deep learning combined with clinical parameters for precise differentiation of lipid-poor adrenal adenomas and metastases.Abdominal radiology (New York) · 2026Article
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- TF-GateNet: An Interpretable and Biologically Guided Framework for Primary-Metastatic State Prediction from Somatic Genomic Alterations.Biomolecules · 2026Article
- Hybrid Deep Learning-Machine Learning Fusion of Clinical, Radiomic and Deep Learning Features for Preoperative Differentiation of Solitary Pulmonary Mucinous Adenocarcinoma.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Artificial Intelligence Tools in Precision Lung Cancer Care: From Early Detection to Clinical Decision Support.Cancers · 2026Review
- Inferring signaling pathway abnormalities from histopathological images via logic-constrained gene-pathway heterogeneous knowledge graph.npj biomedical innovations · 2026Article
- Gene-Morphology Alignment via Graph-Constrained Latent Modeling for Molecular Subtype Prediction from Histopathology in Pancreatic Cancer.medRxiv : the preprint server for health sciences · 2026Article
- DeepGene-BC: Deep Learning-Based Breast Cancer Subtype Prediction via Somatic Point Mutation Profiles.Cancers · 2026Article
- Prediction of malignancy and metastasis of thyroid cancer by combined feature sets through advanced machine learning.BMC medical informatics and decision making · 2026Article
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Histopathology and genomic profiling are cornerstones of precision oncology and are routinely obtained for patients with cancer. Traditionally, histopathology slides are manually reviewed by highly trained pathologists. Genomic data, on the other hand, is evaluated by engineered computational pipelines. In both applications, the advent of modern artificial intelligence methods, specifically machine learning (ML) and deep learning (DL), have opened up a fundamentally new way of extracting actionable insights from raw data, which could augment and potentially replace some aspects of traditional evaluation workflows. In this review, we summarize current and emerging applications of DL in histopathology and genomics, including basic diagnostic as well as advanced prognostic tasks. Based on a growing body of evidence, we suggest that DL could be the groundwork for a new kind of workflow in oncology and cancer research. However, we also point out that DL models can have biases and other flaws that users in healthcare and research need to know about, and we propose ways to address them.
Indexed as
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What OpenQuestion holds
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.