ArticleWorld journal of gastroenterology2026
Predicting lymph node metastasis in colorectal cancer using case-level multiple instance learning.
Article in World journal of gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Deep learning and body composition model for predicting postoperative complications in colorectal cancer.Frontiers in medical technology · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe accurate prediction of lymph node metastasis (LNM) is crucial for managing locally advanced (T3/T4) colorectal cancer (CRC). However, both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.
aimTo develop and validate a case-level multiple-instance learning (MIL) framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.
methodsThe whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected. A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosin-stained primary tumour slides for each patient. These pathological features were subsequently integrated with clinical data, and model performance was evaluated using the area under the curve (AUC).
resultsThe case-level framework demonstrated superior LNM prediction over slide-level training, with the CONCH v1.5 model achieving a mean AUC (± SD) of 0.899 ± 0.033
conclusionA case-level MIL framework provides a superior approach for predicting LNM in advanced CRC. This method shows promise for risk stratification and therapy decisions, requiring further validation.
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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.