ArticleTheranostics2020
Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer.
Article in Theranostics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers, 4 of them syntheses that pooled it.
What it found
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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.
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Who cites it
147 citing papers in PubMed, 4 syntheses or guidelines pooled it, 252 citations in OpenAlex.
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Artificial intelligence networks for assessing the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis.Frontiers in oncology · 2024Pooled it
- Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Development and Interpretation of a Clinicopathological-Based Model for the Identification of Microsatellite Instability in Colorectal Cancer.Disease markers · 2023Trial
- Deep Learning-Based Multi-Cancer Analysis for Predicting Disease-Free Survival Across Multiple Cancer Types.Cancers · 2026Article
- Artificial Intelligence-Based Prediction of Molecular Alterations in Colorectal Cancer Using Routine H&E Whole-Slide Images.International journal of molecular sciences · 2026Review
- Dual modal pathomics model for colorectal cancer early recurrence prediction and mutation landscape analysis.iScience · 2026Article
- Review
- Interpretable machine learning models for bladder cancer overall survival prediction development and external validation via SEER database and Chinese cohort analysis.Discover oncology · 2026Article
- A multimodal approach integrating NK cell-associated gene signatures and pathomics to predict colon adenocarcinoma prognosis.Scientific reports · 2026Article
- Machine Learning-Based Pathomics Signature in Predicting MSH2 Expression and Prognosis in Gastric Cancer.Clinical and translational gastroenterology · 2026Article
- Article
- Genetic landscape of Chinese colorectal cancer: insights into germline and somatic mutations.BMC cancer · 2026Article
- PMI estimation with cross-species transfer learning and visual information generated by pathomics foundation model.International journal of legal medicine · 2026Article
- Deep learning-based mismatch repair prediction using colorectal cancer macroscopic images: a diagnostic study.Journal of gastroenterology · 2026Article
- Advancing medical AI through benchmarking and competition for specialty triage.NPJ digital medicine · 2026Article
- Integrating histopathology and immune marker analysis for machine learning-based colorectal cancer prognostics.Diagnostic pathology · 2026Article
- Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics.World journal of gastrointestinal oncology · 2026Review
- Pathomic model to predict the expression of UQCRH and overall survival of lung adenocarcinoma patients.Diagnostic pathology · 2026Article
87 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation.
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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.