ArticlePrecision clinical medicine2021
Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images.
Article in Precision clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Rethinking mucinous colorectal adenocarcinoma classification: evidence from histopathology and RAS mutations.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Associations of mucinous differentiation and mucin expression with immune cell infiltration and prognosis in colorectal adenocarcinoma.British journal of cancer · 2025Article
- Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review.Clinical Medicine Insights. Oncology · 2024Review
- Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning.NPJ precision oncology · 2023Article
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
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Authors and funding
17 authors.
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Abstract
Background: In colorectal cancer (CRC), mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype, morphology, and prognosis. However, mucinous components are present in a large number of adenocarcinomas, and the prognostic value of mucus proportion has not been investigated. Artificial intelligence provides a way to quantify mucus proportion on whole-slide images (WSIs) accurately. We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts. Methods: Deep learning was used to segment WSIs stained with hematoxylin and eosin. Mucus-tumor ratio (MTR) was defined as the proportion of mucinous component in the tumor area. A training cohort (N = 419) and a validation cohort (N = 315) were used to evaluate the prognostic value of MTR. Survival analysis was performed using the Cox proportional hazard model. Result: Patients were stratified to mucus-low and mucus-high groups, with 24.1% as the threshold. In the training cohort, patients with mucus-high had unfavorable outcomes (hazard ratio for high vs. low 1.88, 95% confidence interval 1.18-2.99, Conclusion: The deep learning quantified MTR was an independent prognostic factor in CRC. With the advantages of advanced efficiency and high consistency, our method is suitable for clinical application and promotes precision medicine development.
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