ArticleNature communications2023
Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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
75 citing papers in PubMed, 1 synthesis or guideline pooled it, 92 citations in OpenAlex.
- FDG-PET/CT Parameters for distinguishing high and low microsatellite instability in colorectal cancer: A systematic review and Meta-analysis.Annals of nuclear medicine · 2026Pooled it
- The Genetic Landscape of Colorectal Cancer: From Molecular Alterations to Therapeutic Decision Pathways.Cancers · 2026Review
- Mutation enrichment in targeted panels flags immunotherapy-responsive POLE-driven hypermutated microsatellite-stable colorectal cancers.NPJ precision oncology · 2026Article
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- Review
- Deep learning-driven MRI radiomics reveals biological subtypes and predicts recurrence risk in rectal cancer.NPJ precision oncology · 2026Article
- The Heterogeneity of Mucinous Colorectal Adenocarcinoma-Histologic and Molecular Phenotypes Drive Prognostic Outcomes.Cancers · 2026Article
- AI-assisted histomorphological stratification of endometrial cancer: real-world validation of foundation models for molecular subtyping.NPJ precision oncology · 2026Article
- An agentic framework for autonomous scientific discovery in cancer pathology.Nature medicine · 2026Article
- [Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].Innere Medizin (Heidelberg, Germany) · 2026Review
- Article
- Integrated Single-Cell and Spatial Transcriptomic Analysis Identifies Putative Metabolic Crosstalk BetweenCancers · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Regulating complexity in AI-enabled omics and multi-omics technologies for precision medicine.NPJ digital medicine · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Artificial Intelligence for Diagnostic Pathology in Nephrology.Journal of the American Society of Nephrology : JASN · 2026Article
- Molecular profiling-directed individualized adjuvant therapy in colorectal cancer: Bridging consensus guidelines to clinical disparities.World journal of gastroenterology · 2026Review
- Transcriptomic-guided whole-slide image classification for molecular subtype identification.PLoS computational biology · 2026Article
15 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
19 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing and accelerate oncologist decision making in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted. We developed MSIntuit, a clinically approved artificial intelligence (AI) based pre-screening tool for MSI detection from haematoxylin-eosin (H&E) stained slides. After training on samples from The Cancer Genome Atlas (TCGA), a blind validation is performed on an independent dataset of 600 consecutive CRC patients. Inter-scanner reliability is studied by digitising each slide using two different scanners. MSIntuit yields a sensitivity of 0.96-0.98, a specificity of 0.47-0.46, and an excellent inter-scanner agreement (Cohen's κ: 0.82). By reaching high sensitivity comparable to gold standard methods while ruling out almost half of the non-MSI population, we show that MSIntuit can effectively serve as a pre-screening tool to alleviate MSI testing burden in clinical practice.
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.