ArticlePloS one2024
Detecting microsatellite instability in colorectal cancer using Transformer-based colonoscopy image classification and retrieval.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Comprehensive Evaluation of the Effectiveness and Safety of Pembrolizumab for the Treatment of Metastatic Colorectal Cancer: A Systematic Review and Meta-Analysis.Current cancer drug targets · 2026Pooled it
- Review
- Statistical models to characterize colon tumor stiffness heterogeneity through representative atomic force microscopy maps.Scientific reports · 2026Article
- Deep learning-based mismatch repair prediction using colorectal cancer macroscopic images: a diagnostic study.Journal of gastroenterology · 2026Article
- Screening, Prognostic, and Predictive Molecular Tools for Colorectal Cancer: Recent Advances in the Classical Background.International journal of molecular sciences · 2026Review
- ESE-PWDNet: an efficient early-stage pine wilt disease detection network.Frontiers in plant science · 2026Article
- Deficient Mismatch Repair Subtypes in Vietnamese Colorectal Cancer: Clinicopathologic Associations, Predictive Modeling, and IHC-PCR Concordance.Cancer management and research · 2026Article
- Exploring the improvement effect of intestinal network monitoring system on intestinal preparation quality of colonoscopy.World journal of gastrointestinal oncology · 2025Article
- H&E-based MSI/MMR testing with AI in colorectal cancer: a multi-centred blinded evaluation.NPJ digital medicine · 2025Article
- Vendor-Agnostic Vision Transformer-Based Artificial Intelligence for Peroral Cholangioscopy: Diagnostic Performance in Biliary Strictures Compared With Convolutional Neural Networks and Endoscopists.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2025Article
- Fusion Learning from Non-contrast CT Scans for the Detection of Hemorrhagic Transformation in Stroke Patients.Journal of imaging informatics in medicine · 2025Article
- Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.Scientific reports · 2025Article
- A hybrid framework for enhanced segmentation and classification of colorectal cancer histopathology.Frontiers in artificial intelligence · 2025Article
- Ensemble learning for predicting microsatellite instability in colorectal cancer using pretreatment colonoscopy images and clinical data.Frontiers in oncology · 2025Article
- Diagnosis of colorectal cancer using residual transformer with mixed attention and explainable AI.PloS one · 2025Article
Corrections and comments
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3 authors.
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Abstract
Colorectal cancer (CRC) is a major global health concern, with microsatellite instability-high (MSI-H) being a defining characteristic of hereditary nonpolyposis colorectal cancer syndrome and affecting 15% of sporadic CRCs. Tumors with MSI-H have unique features and better prognosis compared to MSI-L and microsatellite stable (MSS) tumors. This study proposed establishing a MSI prediction model using more available and low-cost colonoscopy images instead of histopathology. The experiment utilized a database of 427 MSI-H and 1590 MSS colonoscopy images and vision Transformer (ViT) with different feature training approaches to establish the MSI prediction model. The accuracy of combining pre-trained ViT features was 84% with an area under the receiver operating characteristic curve of 0.86, which was better than that of DenseNet201 (80%, 0.80) in the experiment with support vector machine. The content-based image retrieval (CBIR) approach showed that ViT features can obtain a mean average precision of 0.81 compared to 0.79 of DenseNet201. ViT reduced the issues that occur in convolutional neural networks, including limited receptive field and gradient disappearance, and may be better at interpreting diagnostic information around tumors and surrounding tissues. By using CBIR, the presentation of similar images with the same MSI status would provide more convincing deep learning suggestions for clinical use.
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