ReviewClinical and molecular hepatology2022
Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.
Review in Clinical and molecular hepatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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
22 citing papers in PubMed.
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Profiling of extracellular vesicles from primary hepatocytes, organoids, and mash patients identifies cell injury-specific signatures.Scientific reports · 2026Article
- Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images.Diagnostics (Basel, Switzerland) · 2026Article
- Comparative Analysis of Deep Learning Approaches for Histopathology-Based Survival Prediction in Hepatocellular Carcinoma.Cancers · 2026Article
- Organ cross-talk: molecular mechanisms, biological functions, and therapeutic interventions for diseases.Signal transduction and targeted therapy · 2026Review
- Understanding Liver and Digestive Diseases: A Paved Road to Improve Diagnosis, Management, and Treatment.Exploration of digestive diseases · 2026Article
- Explainable deep learning approaches and clinical insights for cancer biomarker identification.Frontiers in oncology · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- AI-Driven Digital Pathology: Deep Learning and Multimodal Integration for Precision Oncology.International journal of molecular sciences · 2025Review
- Deep Learning Model-Based Architectures for Lung Tumor Mutation Profiling: A Systematic Review.Cancers · 2025Review
- Recent advances and challenges in colorectal cancer: From molecular research to treatment.World journal of gastroenterology · 2025Review
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer.Annals of medicine · 2024Article
- Correspondence to editorial on "Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma".Clinical and molecular hepatology · 2024Article
- Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.Frontiers in immunology · 2024Article
- Article
- High-content microscopy reveals a morphological signature of bortezomib resistance.bioRxiv : the preprint server for biology · 2023Article
- Hepatic arterial infusion chemotherapy and immune checkpoint inhibitors, alone or in combination, in advanced hepatocellular carcinoma with macrovascular invasion: a single-centre experience in Taiwan.Journal of gastrointestinal oncology · 2023Article
- Article
- A Single Nucleotide Polymorphism rs1010816 Predicts Sorafenib Therapeutic Outcomes in Advanced Hepatocellular Carcinoma.International journal of molecular sciences · 2023Article
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
Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of key molecular features in cancer tissues. With the accumulation of data and technical advances, the performance of the DL system could be improved in the near future. Therefore, we expect that DL could provide cost- and time-effective alternative tools for patient stratification in the era of precision oncology.
Indexed as
Identifiers
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.