ArticleCell reports. Medicine2023
Predicting colorectal cancer microsatellite instability with a self-attention-enabled convolutional neural network.
Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 32 citations in OpenAlex.
- Artificial Intelligence-Based Prediction of Molecular Alterations in Colorectal Cancer Using Routine H&E Whole-Slide Images.International journal of molecular sciences · 2026Review
- Review
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Deep learning-based mismatch repair prediction using colorectal cancer macroscopic images: a diagnostic study.Journal of gastroenterology · 2026Article
- Transcriptomic-guided whole-slide image classification for molecular subtype identification.PLoS computational biology · 2026Article
- A full-automated tumor budding annotation approach in hematoxylin and eosin-stained whole slide images of colorectal cancer.NPJ precision oncology · 2025Article
- H&E-based MSI/MMR testing with AI in colorectal cancer: a multi-centred blinded evaluation.NPJ digital medicine · 2025Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- A Deep Learning-Based Approach for Explainable Microsatellite Instability Detection in Gastrointestinal Malignancies.Journal of imaging · 2025Article
- Deepath-MSI: a clinic-ready deep learning model for microsatellite instability detection in colorectal cancer using whole-slide imaging.NPJ precision oncology · 2025Article
- Synergistic H&E and IHC image analysis by AI predicts cancer biomarkers and survival outcomes in colorectal and breast cancer.Communications medicine · 2025Article
- Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images.NPJ digital medicine · 2025Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Article
- Deep learning for fine-grained molecular-based colorectal cancer classification.Translational cancer research · 2025Article
- Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.Cell reports. Medicine · 2025Article
- Lactate and lactylation in cancer.Signal transduction and targeted therapy · 2025Review
- The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.Scientific reports · 2025Article
- Hybrid model for predicting microsatellite instability in colorectal cancer using hematoxylin & eosin-stained images and clinical features.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study develops a method combining a convolutional neural network model, INSIGHT, with a self-attention model, WiseMSI, to predict microsatellite instability (MSI) based on the tiles in colorectal cancer patients from a multicenter Chinese cohort. After INSIGHT differentiates tumor tiles from normal tissue tiles in a whole slide image, features of tumor tiles are extracted with a ResNet model pre-trained on ImageNet. Attention-based pooling is adopted to aggregate tile-level features into slide-level representation. INSIGHT has an area under the curve (AUC) of 0.985 for tumor patch classification. The Spearman correlation coefficient of tumor cell fraction given by expert pathologist and INSIGHT is 0.7909. WiseMSI achieves a specificity of 94.7% (95% confidence interval [CI] 93.7%-95.7%), a sensitivity of 84.7% (95% CI 82.6%-86.9%), and an AUC of 0.954 (95% CI 0.948-0.960). Comparative analysis shows that this method has better performance than the other five classic deep learning methods.
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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.