[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119475-en":3,"doc-seo-119475-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119475,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer - Research Summary","Machine learning enables personalized risk stratification for relapse in early-stage non–small-cell lung cancer (NSCLC) by estimating each patient’s relapse probability. The study trains tabular and graph machine learning models on 1,387 patients from the Spanish Lung Cancer Group and generates automatic, patient-level explanations. For tabular models, SHAP local explanations quantify how individual features drive outcomes; for graph models, an example-based approach highlights influential past patients. Results report up to 76% accuracy and 68% on a held-out set.","Original Reports | Artificial Intelligence  \nMachine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer  \nAdrianna Janik, MS1 ; Maria Torrente, MD, PhD2 ; Luca Costabello, PhD1 ; Virginia Calvo, MD, PhD2; Brian Walsh, PhD3,4;  \nCarlos Camps, MD, PhD5 ; Sameh K. Mohamed, PhD3,4 ; Ana L. Ortega, MD6 ; Vt Novˇcek, PhD3,4,7,8; Bartomeu Massut, MD, PhD9; Pasquale Minervini, PhD10 ; M. Rosario Garcia Campelo, MD11 ; Edel del Barco, MD12; Joaquim Bosch-Barrera, MD13 ;  \nErnestina Menasalvas, PhD14 ; Mohan Timilsina, PhD3,4 ; and Mariano Provencio, MD, PhD2   \nDOI [https://doi.org/10.1200/CCI.22.00062](https://doi.org/10.1200/CCI.22.00062)  \n\n| ABSTRACT |  |\n| --- | --- |\n| PURPOSE | Stratifying patients with cancer according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to use machine learning to estimate probability of relapse inpatients with early-stage non–small-cell lung cancer (NSCLC)? |\n| MATERIALS\u003Cbr>AND METHODS | For predicting relapse in 1,387 patients with early-stage (I-II) NSCLC from the Spanish Lung Cancer Group data (average age 65.7 years, female 24.8%, male 75.2%), we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHapley Additive exPlanations local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients. |\n| RESULTS | Machine learning models trained on tabular data exhibit a 76% accuracy for the random forest model at predicting relapse evaluated with a 10-fold crossvalidation (the model was trained 10 times with different independent sets of patients in test, train, and validation sets, and the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a held-out test set of 200 patients, calibrated on a held-out set of 100 patients. |\n| CONCLUSION | Our results show that machine learning models trained on tabular and graph data can enable objective, personalized, and reproducible prediction of relapse and, therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer. |\n\nACCOMPANYING CONTENT  \n Data Supplement  \nAccepted April 14, 2023  \nPublished July 10, 2023  \nJCO Clin Cancer Inform 7:e2200062  \n© 2023 by American Society of Clinical Oncology  \nCreative Commons Attribution Non-Commercial No Derivatives 4.0 License  \nINTRODUCTION  \nLung cancer is the world’s leading cause of cancer-related death with an estimated 1.8 million deaths equating to 18% of all cancer deaths in 2020 .1 It is the leading cause of cancerrelated deaths in men, and it is the second leading cause in women after breast cancer.1 Mortality and incidence occur roughly twice as much in men than in women.1 Five-year relative survival after being diagnosed with lung and bronchus cancers in US population was 22%, as reported in Cancer Statistics 2022 by ACS for years 2011-2017 .2  \nThe resection of early-stage non–small-cell lung cancer (NSCLC) offers patients the best hope of cure; however,  \nrelapse rates postresection remain high and even for patients with disease at the same stage incidences of relapse after curative surgery vary significantly. There were 30% to 55% of patients with NSCLC who develop relapse and eventually die of their disease despite curative resection. Therefore, accurately predicting the individual cases in which the disease is likely to recur after surgery can facilitate early personalized detection and treatment.3  \nThis work targets machine-aided predi","cbCainEvK4feQEDO","https://ap.wps.com/l/cbCainEvK4feQEDO","pdf",749522,1,11,"English","en",105,"# Abstract\n## Purpose\n## Materials and Methods\n## Results\n## Conclusion\n# Introduction\n## Clinical problem and rationale\n# Context\n## Key objective\n## Knowledge generated\n## Relevance","[{\"question\":\"What clinical question does the study address?\",\"answer\":\"The study asks how machine learning can estimate the probability of relapse in patients with early-stage non–small-cell lung cancer (NSCLC) to personalize care.\"},{\"question\":\"How are recurrence predictions modeled and explained?\",\"answer\":\"It trains tabular and graph machine learning models on clinical data. Tabular models use SHAP local explanations to show feature contributions, while graph model predictions are explained using an example-based method highlighting influential past patients.\"},{\"question\":\"What performance results are reported for the models?\",\"answer\":\"For tabular data, the random forest model achieves 76% accuracy under 10-fold cross-validation. The graph machine learning approach reaches 68% accuracy on a held-out test set of 200 patients, calibrated using a held-out set of 100 patients.\"}]","Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer - Research Summary | PDF",1785724511,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learningassisted-recurrence-prediction-for-patients-with-early-stage-nonsmall-cell-lung-cancer-research-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learningassisted-recurrence-prediction-for-patients-with-early-stage-nonsmall-cell-lung-cancer-research-summary/119475/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical question does the study address?","Question",{"text":75,"@type":76},"The study asks how machine learning can estimate the probability of relapse in patients with early-stage non–small-cell lung cancer (NSCLC) to personalize care.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are recurrence predictions modeled and explained?",{"text":80,"@type":76},"It trains tabular and graph machine learning models on clinical data. Tabular models use SHAP local explanations to show feature contributions, while graph model predictions are explained using an example-based method highlighting influential past patients.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance results are reported for the models?",{"text":84,"@type":76},"For tabular data, the random forest model achieves 76% accuracy under 10-fold cross-validation. The graph machine learning approach reaches 68% accuracy on a held-out test set of 200 patients, calibrated using a held-out set of 100 patients.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]