Machine Learning Predictive Models and Risk Factors for Lymph Node Metastasis in T1 and T2 Stage Rectal Cancer Patients

Machine Learning Predictive Models and Risk Factors for Lymph Node Metastasis in T1 and T2 Stage Rectal Cancer Patients | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 March 2025 V1 Latest version Share on Machine Learning Predictive Models and Risk Factors for Lymph Node Metastasis in T1 and T2 Stage Rectal Cancer Patients Authors : Yang Lan 0000-0001-8937-3097 , Zi-jian Tang , Qi-wei-Chen , Yang Lu , and Junqiang Chen [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174280175.58553733/v1 163 views 66 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Objective: Lymph node metastasis (LNM) is a critical determinant of poor prognosis in rectal cancer (RC), especially during its early stages. Some patients may have the opportunity to avoid invasive surgery if LNM are accurately assessed. Therefore, precise evaluation of LNM is crucial. This study aims to identify factors associated with LNM and to develop a predictive nomogram for estimating the risk of LNM in patients with RC. Methods: We analyzed a total of 6,114 early-stage RC cases retrieved from the Surveillance, Epidemiology, and End Results (SEER) database spanning 2010 to 2017. The dataset was randomly divided into training and validation cohorts in a 7:3 ratio. Using this data, we constructed seven machine learning (ML) algorithm models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve (AUPRC), and additional performance metrics. The top-performing model was subsequently employed to elucidate the associations between clinicopathological features and the target variable. Results: This study included 6,114 patients, among whom 1,028 (16.73%) had LNM. Logistic regression (LR) analysis was performed to identify independent risk factors associated with LNM in patients with early-stage RC, and a nomogram was developed to predict LNM. By evaluating different models based on various metrics, the gradient boosting machine (GBM) algorithm demonstrated the best predictive performance. Conclusion: This study developed a GBM model to predict the risk of LNM in T1 and T2 stage RC patients, offering valuable support for surgeons in making informed clinical decisions. Supplementary Material File ((manuscript)machine learning predictive models a.docx) Download 1.91 MB Information & Authors Information Version history V1 Version 1 24 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords lnm machine learning rectal cancer seer Authors Affiliations Yang Lan 0000-0001-8937-3097 Guangxi Medical University View all articles by this author Zi-jian Tang Guangxi Medical University View all articles by this author Qi-wei-Chen Maternity and Child Health Care of Guangxi Zhuang Autonomous Region View all articles by this author Yang Lu Southern Medical University Nanfang Hospital View all articles by this author Junqiang Chen [email protected] Guangxi Medical University View all articles by this author Metrics & Citations Metrics Article Usage 163 views 66 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yang Lan, Zi-jian Tang, Qi-wei-Chen, et al. Machine Learning Predictive Models and Risk Factors for Lymph Node Metastasis in T1 and T2 Stage Rectal Cancer Patients. Authorea . 24 March 2025. 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