ReviewCancers2021
Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis 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
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.
- Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- The Genetic Landscape of Colorectal Cancer: From Molecular Alterations to Therapeutic Decision Pathways.Cancers · 2026Review
- The Heterogeneity of Mucinous Colorectal Adenocarcinoma-Histologic and Molecular Phenotypes Drive Prognostic Outcomes.Cancers · 2026Article
- Review
- Integrating histopathology and immune marker analysis for machine learning-based colorectal cancer prognostics.Diagnostic pathology · 2026Article
- Understanding Liver and Digestive Diseases: A Paved Road to Improve Diagnosis, Management, and Treatment.Exploration of digestive diseases · 2026Article
- Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer.Frontiers in oncology · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Impact of Magnification, Image Type, and Number on Convolutional Neural Network Performance in Differentiating Canine Large Cell Lymphoma From Non-Lymphoma via Lymph Node Cytology.Veterinary clinical pathology · 2025Article
- Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part I Model development.Veterinary clinical pathology · 2025Review
- Synergistic H&E and IHC image analysis by AI predicts cancer biomarkers and survival outcomes in colorectal and breast cancer.Communications medicine · 2025Article
- Recent advances and challenges in colorectal cancer: From molecular research to treatment.World journal of gastroenterology · 2025Review
- Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy.World journal of radiology · 2025Article
- A Survey of Perspectives and Educational Needs of Canadian Oncology Residents on Artificial Intelligence.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Article
- Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.Molecular imaging and biology · 2025Article
- Artificial intelligence can be trained to predictVeterinary pathology · 2025Article
- The Gut Microbiota and Colorectal Cancer: Understanding the Link and Exploring Therapeutic Interventions.Biology · 2025Review
- Computational pathology applied to clinical colorectal cancer cohorts identifies immune and endothelial cell spatial patterns predictive of outcome.The Journal of pathology · 2025Article
- Integrating AI into Cancer Immunotherapy-A Narrative Review of Current Applications and Future Directions.Diseases (Basel, Switzerland) · 2025Review
- ARTIFICIAL INTELLIGENCE IN THE HISTOLOGICAL DIAGNOSIS OF COLON CANCER: A RAY OF HOPE.Arquivos de gastroenterologia · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microsatellite instability (MSI) is a molecular marker of deficient DNA mismatch repair (dMMR) that is found in approximately 15% of colorectal cancer (CRC) patients. Testing all CRC patients for MSI/dMMR is recommended as screening for Lynch Syndrome and, more recently, to determine eligibility for immune checkpoint inhibitors in advanced disease. However, universal testing for MSI/dMMR has not been uniformly implemented because of cost and resource limitations. Artificial intelligence has been used to predict MSI/dMMR directly from hematoxylin and eosin (H&E) stained tissue slides. We review the emerging data regarding the utility of machine learning for MSI classification, focusing on CRC. We also provide the clinician with an introduction to image analysis with machine learning and convolutional neural networks. Machine learning can predict MSI/dMMR with high accuracy in high quality, curated datasets. Accuracy can be significantly decreased when applied to cohorts with different ethnic and/or clinical characteristics, or different tissue preparation protocols. Research is ongoing to determine the optimal machine learning methods for predicting MSI, which will need to be compared to current clinical practices, including next-generation sequencing. Predicting response to immunotherapy remains an unmet need.
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.