ArticleFrontiers in surgery2022
Establishment of a Dynamic Nomogram for Predicting the Risk of Lymph Node Metastasis in T1 Stage Colorectal Cancer.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Tumour deposits in colorectal carcinoma are associated with immune evasion and dose-dependent adverse prognosis beyond nodal status.British journal of cancer · 2026Article
- Calibrated Absolute Risk of Lymph-Node-Metastasis After Non-curative Endoscopic Resection of pT1 Colorectal Cancer.Annals of surgical oncology · 2026Article
- Development and explanation of a machine learning model for identifying non-localized early-onset T1 colorectal cancer.Discover oncology · 2025Article
- Article
- Evaluation of the utility of a nomogram for predicting lymph node metastasis in T1 colorectal cancer in shared decision-making in clinical practice: a survey-based study.Annals of coloproctology · 2025Article
- Predicting the risk of lymph node metastasis in colon cancer: development and validation of an online dynamic nomogram based on multiple preoperative data.BMC gastroenterology · 2025Article
- From endoscopy to surgery: a translational perspective on early esophageal cancer management.Frontiers in medicine · 2025Article
- A nomogram of inflammatory indexes for preoperatively predicting the risk of lymph node metastasis in colorectal cancer.Techniques in coloproctology · 2024Article
- A clinical-radiomics nomogram based on spectral CT multi-parameter images for preoperative prediction of lymph node metastasis in colorectal cancer.Clinical & experimental metastasis · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Accurate prediction of the risk of lymph node metastasis in patients with stage T1 colorectal cancer is crucial for the formulation of treatment plans for additional surgery and lymph node dissection after endoscopic resection. The purpose of this study was to establish a predictive model for evaluating the risk of LNM in patients with stage T1 colorectal cancer. Methods: The clinicopathological and imaging data of 179 patients with T1 stage colorectal cancer who underwent radical resection of colorectal cancer were collected. LASSO regression and a random forest algorithm were used to screen the important risk factors for LNM, and a multivariate logistic regression equation and dynamic nomogram were constructed. The C index, Calibration curve, and area under the ROC curve were used to evaluate the discriminant and prediction ability of the nomogram. The net reclassification index (NRI), comprehensive discriminant improvement index (IDI), and clinical decision curve (DCA) were compared with traditional ESMO criteria to evaluate the accuracy, net benefit, and clinical practicability of the model. Results: The probability of lymph node metastasis in patients with T1 colorectal cancer was 11.17% (20/179). Multivariate analysis showed that the independent risk factors for LNM in T1 colorectal cancer were submucosal invasion depth, histological grade, CEA, lymphovascular invasion, and imaging results. The dynamic nomogram model constructed with independent risk factors has good discrimination and prediction capabilities. The C index was 0.914, the corrected C index was 0.890, the area under the ROC curve was 0.914, and the accuracy, sensitivity, and specificity were 93.3, 80.0, and 91.8%, respectively. The NRI, IDI, and DCA show that this model is superior to the ESMO standard. Conclusion: This study establishes a dynamic nomogram that can effectively predict the risk of lymph node metastasis in patients with stage T1 colorectal cancer, which will provide certain help for the formulation of subsequent treatment plans for patients with stage T1 CRC after endoscopic resection.
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