ArticleFrontiers in oncology2021
Development of a Novel Prognostic Model for Predicting Lymph Node Metastasis in Early Colorectal Cancer: Analysis Based on the Surveillance, Epidemiology, and End Results Database.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 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
- Prior endoscopic resection does not affect the outcome of secondary surgery for T1 colorectal cancer, a systematic review and meta-analysis.International journal of colorectal disease · 2022Pooled it
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.Cancers · 2025Review
- The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.Scientific reports · 2025Article
- Machine Learning Algorithm for Predicting Distant Metastasis of T1 and T2 Gallbladder Cancer Based on SEER Database.Bioengineering (Basel, Switzerland) · 2024Article
- Clinical outcomes of colorectal neoplasm with positive resection margin after endoscopic submucosal dissection.Scientific reports · 2024Article
- Article
- Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review).Oncology reports · 2024Review
- Commentary: An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer.Frontiers in oncology · 2024Article
- A discrimination model by machine learning to avoid gastrectomy for early gastric cancer.Annals of gastroenterological surgery · 2023Article
- Prediction model for lymph node metastasis in superficial colorectal cancer: a better choice than computed tomography.Surgical endoscopy · 2023Article
- An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer.Frontiers in oncology · 2023Article
- [Tumor budding in colorectal cancer-Information for clinical use and instructions for practical evaluation].Der Pathologe · 2022Review
- Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer.Cancer cell international · 2022Article
- Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer.Frontiers in oncology · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentification of a simplified prediction model for lymph node metastasis (LNM) for patients with early colorectal cancer (CRC) is urgently needed to determine treatment and follow-up strategies. Therefore, in this study, we aimed to develop an accurate predictive model for LNM in early CRC.
methodsWe analyzed data from the 2004-2016 Surveillance Epidemiology and End Results database to develop and validate prediction models for LNM. Seven models, namely, logistic regression, XGBoost, k-nearest neighbors, classification and regression trees model, support vector machines, neural network, and random forest (RF) models, were used.
resultsA total of 26,733 patients with a diagnosis of early CRC (T1) were analyzed. The models included 8 independent prognostic variables; age at diagnosis, sex, race, primary site, histologic type, tumor grade, and, tumor size. LNM was significantly more frequent in patients with larger tumors, women, younger patients, and patients with more poorly differentiated tumor. The RF model showed the best predictive performance in comparison to the other method, achieving an accuracy of 96.0%, a sensitivity of 99.7%, a specificity of 92.9%, and an area under the curve of 0.991. Tumor size is the most important features in predicting LNM in early CRC.
conclusionWe established a simplified reproducible predictive model for LNM in early CRC that could be used to guide treatment decisions. These findings warrant further confirmation in large prospective clinical trials.
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