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Machine Learning to Predict Mortality in Older Patients With Cancer: Development and External Validation of the Geriatric Cancer Scoring System Using Two Large French Cohorts

dc.rights.licenseopenen_US
dc.contributor.authorAUDUREAU, Etienne
dc.contributor.authorSOUBEYRAN, Pierre
dc.contributor.authorMARTINEZ-TAPIA, Claudia
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorBELLERA, Carine
dc.contributor.authorBASTUJI-GARIN, Sylvie
dc.contributor.authorBOUDOU-ROUQUETTE, Pascaline
dc.contributor.authorCHAHWAKILIAN, Anne
dc.contributor.authorGRELLETY, Thomas
dc.contributor.authorHANON, Olivier
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMATHOULIN PELISSIER, Simone
IDREF: 071358587
dc.contributor.authorPAILLAUD, Elena
dc.contributor.authorCANOUI-POITRINE, Florence
dc.date.accessioned2025-02-21T12:18:30Z
dc.date.available2025-02-21T12:18:30Z
dc.date.issued2025-01-24
dc.identifier.issn1527-7755en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/205102
dc.description.abstractEnPURPOSE: Establishing an accurate prognosis remains challenging in older patients with cancer because of the population's heterogeneity and the current predictive models' reduced ability to capture the complex interactions between oncologic and geriatric predictors. We aim to develop and externally validate a new predictive score (the Geriatric Cancer Scoring System [GCSS]) to refine individualized prognosis for older patients with cancer during the first year after a geriatric assessment (GA). MATERIALS AND METHODS: Data were collected from two French prospective multicenter cohorts of patients with cancer 70 years and older, referred for GA: ELCAPA (training set January 2007-March 2016) and ONCODAGE (validation set August 2008-March 2010). Candidate predictors included baseline oncologic and geriatric factors and routine biomarkers. We built predictive models using Cox regression, single decision tree (DT), and random survival forest (RSF) methods, comparing their predictive performance for 3-, 6-, and 12-month mortalities by computing time-dependent area under the receiver operator curve (tAUC). RESULTS: A total of 2,012 and 1,397 patients were included in the training and validation set, respectively (mean age: 81 ± 6 years/78 ± 5 years; women: 47%/70%; metastatic cancer: 50%/34%; 12-month mortality: 43%/16%). Tumor site/metastatic status, cancer treatment, weight loss, ≥five prescription drugs, impaired functional status and mobility, abnormal G-8 score, low creatinine clearance, and elevated C-reactive protein (CRP)/albumin were identified as relevant predictors in the Cox model. DT and RSF identified more complex combinations of features, with G-8 score, tumor site/metastatic status, and CRP/albumin ratio contributing most to the predictions. The RSF approach gave the highest tAUC (12 months: 0.87 [RSF], 0.82 [Cox], 0.82 [DT]) and was retained as the final model. CONCLUSION: The GCSS on the basis of a machine learning approach applied to two large French cohorts gave an accurate externally validated mortality prediction. The GCSS might improve decision making and counseling in older patients with cancer referred for pretherapeutic GA. GCSS's generalizability must now be confirmed in an international setting.
dc.language.isoENen_US
dc.title.enMachine Learning to Predict Mortality in Older Patients With Cancer: Development and External Validation of the Geriatric Cancer Scoring System Using Two Large French Cohorts
dc.title.alternativeJ Clin Oncolen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1200/jco.24.00117en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed39854651en_US
bordeaux.journalJournal of Clinical Oncologyen_US
bordeaux.pageJco2400117en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamEPICENE_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-04960648
hal.version1
hal.date.transferred2025-02-21T12:18:33Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
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