ArticleInternational journal of colorectal disease2024
A new clinical model for predicting lymph node metastasis in T1 colorectal cancer.
Article in International journal of colorectal disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Risk of lymph-node metastasis in early onset T1 colorectal cancer: a systematic review and meta-analysis.Journal of gastroenterology · 2026Pooled it
- T1 colorectal cancer: determinants of lymph node metastasis, survival patterns, and risk-adapted management.Updates in surgery · 2026Review
- Development and explanation of a machine learning model for identifying non-localized early-onset T1 colorectal cancer.Discover oncology · 2025Article
- The Relationship between Tumor Budding and Tumor Deposits in Patients with Stage III Colorectal Carcinoma.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
purposeLymph node metastasis (LNM) is a crucial factor that determines the prognosis of T1 colorectal cancer (CRC) patients. We aimed to develop a practical prediction model for LNM in T1 CRC.
methodsWe conducted a retrospective analysis of data from 825 patients with T1 CRC who underwent radical resection at a single center in China. All enrolled patients were randomly divided into a training set and a validation set at a ratio of 7:3 using R software. Risk factors for LNM were identified through multivariate logistic regression analyses. Subsequently, a prediction model was developed using the selected variables.
resultsThe lymph node metastasis (LNM) rate was 10.1% in the training cohort and 9.3% in the validation cohort. In the training set, risk factors for LNM in T1 CRC were identified, including depressed endoscopic gross appearance, sex, submucosal invasion combined with tumor grade (DSI-TG), lymphovascular invasion (LVI), and tumor budding. LVI emerged as the most potent predictor for LNM. The prediction model based on these factors exhibited good discrimination ability in the validation sets (AUC: 79.3%). Compared to current guidelines, the model could potentially reduce over-surgery by 48.9%. Interestingly, we observed that sex had a differential impact on LNM between early-onset and late-onset CRC patients.
conclusionsWe developed a clinical prediction model for LNM in T1 CRC using five factors that are easily accessible in clinical practice. The model has better predictive performance and practicality than the current guidelines and can assist clinicians in making treatment decisions for T1 CRC patients.
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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.