ArticleClinical cancer research : an official journal of the American Association for Cancer Research2020
Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07417800 (Construction and Clinical Validation of a Predictive Model for Postoperative Adjuvant Therapy in Hepatocellular Carcinoma Based on Whole-Slide Digital Pathological Images and Deep Learning), which is not on this map. Cited by 53 papers, 3 of them syntheses that pooled it.
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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.
Construction and Clinical Validation of a Predictive Model for Postoperative Adjuvant Therapy in Hepatocellular Carcinoma Based on Whole-Slide Digital Pathological Images and Deep Learning
Who cites it
53 citing papers in PubMed, 3 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- Tumour-Associated Neutrophils in Colorectal Cancer: a Systematic Review, Meta-analysis, and Comprehensive Literature Review.Journal of gastrointestinal cancer · 2026Pooled it
- Tumor-associated neutrophils and survival outcomes in colorectal cancer: a systematic review and multilevel meta-analysis.Frontiers in oncology · 2026Pooled it
- Artificial intelligence in colorectal surgery: an AI-powered systematic review.Techniques in coloproctology · 2023Pooled it
- Tumor Immunogenomic Features Determine Outcomes in Patients with Metastatic Colorectal Cancer Treated with Standard-of-Care Combinations of Bevacizumab and Cetuximab.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022Trial
- Preoperative prediction of vessels encapsulating tumor clusters in hepatocellular carcinoma based on CT findings and des-γ-carboxy prothrombin level.International journal of clinical oncology · 2026Article
- An Open Source Automated Tumor Infiltrating Lymphocytes Algorithm for Prognosis in Primary Small Bowel Adenocarcinoma Using Routine Hematoxylin and Eosin Stained Sections.Cancer medicine · 2026Article
- Article
- Significance of tumor infiltrating granulocytes and neutrophil extracellular traps in colorectal cancer.British journal of cancer · 2026Article
- Opposing prognostic roles of tumor-associated and circulating MMP8 in colorectal cancer.Clinical and experimental medicine · 2026Article
- Eosinophils in solid cancers: sentinels, predictors, and therapeutic allies.Journal of experimental & clinical cancer research : CR · 2026Review
- Automated quantification of tumor-infiltrating lymphocytes by machine learning reveals prognostic and immunogenomic features in lung cancer.Scientific reports · 2026Article
- Immune profiling by bulk RNA-seq and multiplex immunofluorescence imaging across multiple cancer types.Frontiers in oncology · 2026Article
- Cytotoxic T-cell exhaustion analyses by in situ single-cell immunofluorescence in relation to colorectal cancer patient age, tissue bacteria and mortality.BMJ oncology · 2026Article
- Significance of Th1 and Th2 Cell Densities and Th1/Th2 Cytokine Profiles in Colorectal Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
- Preliminary qualification of a machine learning-based assessment of the tumor immune infiltrate as a predictor of outcome in patients with hepatocellular carcinoma treated with atezolizumab plus bevacizumab.Journal for immunotherapy of cancer · 2025Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Histopathological evaluation of abdominal aortic aneurysms with deep learning.Diagnostic pathology · 2025Article
- Deep learning-based quantification of eosinophils and lymphocytes shows complementary prognostic effects in colorectal cancer patients.NPJ precision oncology · 2025Article
- Multimarker Assessment of B-Cell and Plasma Cell Subsets and Their Prognostic Role in the Colorectal Cancer Microenvironment.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Recent advances and challenges in colorectal cancer: From molecular research to treatment.World journal of gastroenterology · 2025Review
Corrections and comments
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Authors and funding
27 authors at 7 institutions in 4 countries.
Funding
Abstract
purposeAlthough high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-associated plasma cells, neutrophils, and eosinophils is less well-defined. EXPERIMENTAL
designWe computationally processed digital images of hematoxylin and eosin (H&E)-stained sections to identify lymphocytes, plasma cells, neutrophils, and eosinophils in tumor intraepithelial and stromal areas of 934 colorectal cancers in two prospective cohort studies. Multivariable Cox proportional hazards regression was used to compute mortality HR according to cell density quartiles. The spatial patterns of immune cell infiltration were studied using the G
resultsImmune cell densities measured by the automated classifier demonstrated high correlation with densities both from manual counts and those obtained from an independently trained automated classifier (Spearman's ρ 0.71-0.96). High densities of stromal lymphocytes and eosinophils were associated with better cancer-specific survival [
conclusionsThese findings highlight the potential for machine learning assessment of H&E-stained sections to provide robust, quantitative tumor-immune biomarkers for precision medicine.
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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.