ArticleCommunications medicine2023
Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning.
Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Direct tumour-adipocyte contact / Stroma AReactive Invasion Front Area (TAC/SARIFA) predicts adverse outcome in colorectal cancer: a focused evidence synthesis and meta-analysis.Histopathology · 2026Pooled it
- SARIFA and Lipid Metabolic Reprogramming in Prostate Cancer: Fundamental Mechanisms, Tumor Microenvironment, and Novel Biomarker Prospects.Life (Basel, Switzerland) · 2026Review
- Deep learning-based prediction of lymph node metastasis and occult tumor cells in gastric cancer using histopathological images: a retrospective study.British journal of cancer · 2026Article
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug response.The Journal of pathology · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- His-MMDM: Multi-Domain and Multi-Omics Translation of Histopathological Images with Diffusion Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A High Magnifications Histopathology Image Dataset for Oral Squamous Cell Carcinoma Diagnosis and Prognosis.Scientific data · 2026Article
- Predicting lymph node metastasis in colorectal cancer using case-level multiple instance learning.World journal of gastroenterology · 2026Article
- Preoperative prediction of lymphatic metastasis in rectal cancer using a fusion model based on multiparameter magnetic resonance imaging: a retrospective validation study.Frontiers in oncology · 2026Article
- Automated multi-regional IHC scoring enhances prognostication in colorectal cancer.The journal of pathology. Clinical research · 2025Article
- Machine Learning Based Assessment of Inguinal Lymph Node Metastasis in Patients with Squamous Cell Carcinoma of the Vulva.Journal of clinical medicine · 2025Article
- A pathologist-AI collaboration framework for enhancing diagnostic accuracies and efficiencies.Nature biomedical engineering · 2025Article
- A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer.Frontiers in oncology · 2025Article
- Converging deep learning and human-observed tumor-adipocyte interaction as a biomarker in colorectal cancer.Communications medicine · 2024Article
- Lymph Node Metastasis in Gastrointestinal Carcinomas: A View from a Proteomics Perspective.Current oncology (Toronto, Ont.) · 2024Review
- Evolution of Colorectal Cancer Trends and Treatment Outcomes: A Comprehensive Retrospective Analysis (2019-2023) in West Kazakhstan.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- The SARIFA biomarker in the context of basic research of lipid-driven cancers.NPJ precision oncology · 2024Review
- The relationship between Stroma AReactive Invasion Front Areas (SARIFA), Warburg-subtype and survival: results from a large prospective series of colorectal cancer patients.Cancer & metabolism · 2024Article
Corrections and comments
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Authors and funding
20 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPresence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning has shown success in computational pathology, but has struggled to boost performance when combined with known predictors.
methodsMachine-learned features are created by clustering deep learning embeddings of small patches of tumor in colorectal cancer via k-means, and then selecting the top clusters that add predictive value to a logistic regression model when combined with known baseline clinicopathological variables. We then analyze performance of logistic regression models trained with and without these machine-learned features in combination with the baseline variables.
resultsThe machine-learned extracted features provide independent signal for the presence of LNM (AUROC: 0.638, 95% CI: [0.590, 0.683]). Furthermore, the machine-learned features add predictive value to the set of 6 clinicopathologic variables in an external validation set (likelihood ratio test, p < 0.00032; AUROC: 0.740, 95% CI: [0.701, 0.780]). A model incorporating these features can also further risk-stratify patients with and without identified metastasis (p < 0.001 for both stage II and stage III).
conclusionThis work demonstrates an effective approach to combine deep learning with established clinicopathologic factors in order to identify independently informative features associated with LNM. Further work building on these specific results may have important impact in prognostication and therapeutic decision making for LNM. Additionally, this general computational approach may prove useful in other contexts.
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Registered trials
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.