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- W2971900642 abstract "11516 Background: Accurate prognosis is crucial to decision making in oncology, but remains challenging in older patients due to the heterogeneity of this population and the lack of ability of current models to capture complex interactions between oncological and geriatric predictors. We aimed to develop new predictive algorithms based on machine learning to refine individualized prognosis in older patients with cancer. Methods: Data were collected from 3409 patients ≥70 years referred to geriatric oncology clinics for completion of a geriatric assessment (GA), including 2012 and 1397 patients from the ELCAPA (training set) and ONCODAGE (validation set) French prospective cohorts, respectively. Candidate predictors included baseline oncological and geriatric parameters, G-8 score and routine biological data (CRP/albumin ratio). Prognostic models for 12-months mortality were built using Cox regression model, single decision tree (DT) and random survival forest (RSF). Models performance was compared based on externally validated Harrell’s C-indexes. Results: During the 1-year study period, 875 (43%) and 219 (16%) patients died in the training and validation sets, respectively (mean age: 81±6 / 78±5, women 47% / 70%, metastasis 50% / 34%). Cox model identified 9 independent predictors of mortality: tumor site/metastatic status, anticancer treatment, weight loss > 3kg, drugs > 5, renal failure, increased CRP/Albumin, ECOG-PS≥2, ADL≤5 and altered TGUG. DT identified more complex combinations between features, yielding 16 patient groups with highly differentiated survival, notably depending on the G-8 ( < 10 vs. ≥10 as the root node). RFS had the highest C-index (0.86 [RFS], 0.82 [Cox], 0.81 [DT]), identifying the G-8, CRP/albumin and tumor site/metastasis as the most important features. Conclusions: While Cox modeling confirmed known independent prognostic factors, DT revealed more complex interactions between them and random forest achieved superior prognostic performance by better capturing patient’s complexity. The latter model has been implemented into an interactive web interface for easy and direct use in clinical practice. Clinical trial information: NCT02884375." @default.
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- W2971900642 date "2019-05-20" @default.
- W2971900642 modified "2023-09-29" @default.
- W2971900642 title "Using machine learning to predict mortality in older patients with cancer: Decision tree and random forest analyses from the ELCAPA and ONCODAGE prospective cohorts." @default.
- W2971900642 doi "https://doi.org/10.1200/jco.2019.37.15_suppl.11516" @default.
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