[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127898-en":3,"doc-seo-127898-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127898,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms","This study develops and validates a machine-learning nomogram to estimate the risk of lymph node metastasis (LNM) in stage II–III gastric cancer, addressing limitations of current clinical assessment methods. A retrospective cohort of 1147 gastrectomy patients was analyzed to compare clinicopathological features between LNM and non-LNM groups. Univariate logistic regression and LASSO with random forest identified vascular invasion, maximum tumor diameter, monocyte proportion, hematocrit, and lymphocyte–monocyte ratio as key predictors, with performance assessed by ROC, calibration, and decision curves. SHAP values interpret each feature’s influence.","TYPE Original Research PUBLISHED 08 October 2024 DOI 10.3389/fonc.2024.1399970  \nOPEN ACCESS  \nEDITED BY  \nKai Li,  \nThe First Affiliated Hospital of China Medical University, China  \nREVIEWED BY  \nChristian Cotsoglou,  \nIRCCS San Gerardo dei Tintori Foundation, Italy  \nPeiqiang Yan,  \nHarvard Medical School, United States  \n*CORRESPONDENCE  \nHuiping Xue  \n [huiping_xue@126.com](huiping_xue@126.com)  \nRECEIVED 12 March 2024  \nACCEPTED 17 September 2024  \nPUBLISHED 08 October 2024  \nCITATION  \nYue C and Xue H (2024) Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms. Front. Oncol. 14:1399970 .  \ndoi: 10.3389/fonc.2024.1399970  \nCOPYRIGHT  \n© 2024 Yue and Xue. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nConstruction and validation of anomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms  \nChongkang Yue and Huiping Xue*  \nDepartment of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China  \nBackground: Gastric cancer, a pervasive malignancy globally, often presents with regional lymph node metastasis (LNM), profoundly impacting prognosis and treatment options. Existing clinical methods for determining the presence of LNM are not precise enough, necessitating the development of an accurate risk prediction model.  \nObjective: Our primary objective was to employ machine learning algorithms to identify risk factors for LNM and establish a precise prediction model for stage IIIII gastric cancer.  \nMethods: A study was conducted at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between May 2010 and December 2022 . This retrospective study analyzed 1147 surgeries for gastric cancer and explored the clinicopathological differences between LNM and non-LNM cohorts. Utilizing univariate logistic regression and two machine learning methodologies—Least absolute shrinkage and selection operator (LASSO) and random forest (RF)—weidentified vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit (HCT), and lymphocyte-monocyte ratio (LMR) as salient factors and consolidated them into a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves were used to evaluate the test efficacy of the nomogram. Shapley Additive Explanation (SHAP) values were utilized to illustrate the predictive impact of each feature on the model’s output.  \nResults: Significant differences in tumor characteristics were discerned between LNM and non-LNM cohorts through appropriate statistical methods. Anomogram, incorporating vascular invasion, maximum tumor diameter, percentage of monocytes, HCT, and LMR, was developed and exhibited satisfactory predictive capabilities with an AUC of 0.787 (95% CI: 0.749-0.824) in the training set and 0 . 753 (95% CI: 0 .694-0. 812) in the validation set. Calibration curve s and decision curves affir med the n omo gram ’ s predictive accuracy.  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nConclusion: In conclusion, leveraging machine learning algorithms, we devised anomogram for precise LNM risk prognostication in stage II-III gastric cancer, offering a valuable tool for tailored risk assessment in clinical decision-making.  \nKEYWORDS  \ngastric cancer, lymph node metastasis, machine learning, nomogram, prediction model  \nIntroduction  \nGastric cancer, a pervas","cbCaibQThAFiG96g","https://ap.wps.com/l/cbCaibQThAFiG96g","pdf",2315530,2,1,13,"English","en",105,"# Introduction\n## Clinical need for accurate LNM risk prediction\n## Machine learning approaches for medical risk modeling\n# Methods\n## Study design and patient cohort\n## Feature selection and model construction\n## Model evaluation and interpretability\n# Results\n## Baseline and clinicopathological differences\n## Nomogram performance and validation\n## Calibration and decision curve assessment\n# Conclusion","[{\"question\":\"What problem does the study address?\",\"answer\":\"It addresses the insufficient accuracy of existing clinical methods for determining lymph node metastasis in stage II–III gastric cancer, which limits prognosis and treatment decisions.\"},{\"question\":\"Which machine learning techniques were used to build the nomogram?\",\"answer\":\"The study used univariate logistic regression plus two machine learning approaches: LASSO (Least absolute shrinkage and selection operator) and random forest (RF) to identify predictive factors and construct the model.\"},{\"question\":\"What variables were included in the final nomogram and how was it evaluated?\",\"answer\":\"The nomogram incorporated vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit, and lymphocyte–monocyte ratio. Performance was assessed using ROC AUC, calibration curves, decision curves, and feature impact visualization with SHAP values.\"}]","Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms | PDF",1785942795,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"construction-and-validation-of-a-nomogram-model-for-lymph-node-metastasis-of-stage-ii-iii-gastric-cancer-based-on-machine-learning-algorithms","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/construction-and-validation-of-a-nomogram-model-for-lymph-node-metastasis-of-stage-ii-iii-gastric-cancer-based-on-machine-learning-algorithms/127898/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address?","Question",{"text":76,"@type":77},"It addresses the insufficient accuracy of existing clinical methods for determining lymph node metastasis in stage II–III gastric cancer, which limits prognosis and treatment decisions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning techniques were used to build the nomogram?",{"text":81,"@type":77},"The study used univariate logistic regression plus two machine learning approaches: LASSO (Least absolute shrinkage and selection operator) and random forest (RF) to identify predictive factors and construct the model.",{"name":83,"@type":74,"acceptedAnswer":84},"What variables were included in the final nomogram and how was it evaluated?",{"text":85,"@type":77},"The nomogram incorporated vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit, and lymphocyte–monocyte ratio. 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