ArticlePloS one2026
Decision tree model to predict one-year survival in ambulatory patients with advanced cancer.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAn accurate prognostication is crucial for end-of-life decision-making in advanced cancer care. While existing prognostic tools focus on short-term survival (weeks/months), there is a paucity of studies that have examined the long-term prediction at one year. A one-year timeframe is regarded as a general indicator of palliative care referral; however, there are many uncertain issues. This study aimed to develop a one-year survival prediction model using objective parameters for patients with advanced cancer.
methodsThis was a secondary analysis of data from a Korean prospective cohort study. Participants, with clinician-predicted survival of ≤1 year, were assessed using clinical data, performance status, laboratory data and chemotherapy response. Recursive partitioning analyses (RPA) were used to identify the prognostic factors and build a prediction model.
resultsOf the 200 advanced cancer patients (mean age 64.4, 36% female; 33.5% lung cancer), the median survival was 228 days. Using three variables (chemotherapy response, C reactive protein -Albumin Ratio, and lactate dehydrogenase level), we developed a 4-node survival tree. The model demonstrated an optimism-corrected area under the curve of 0.749 (95% confidence interval: 0.696-0.800) at one year, after 200 bootstrap resampling. The Brier score was 0.161, and the calibration slope was 0.99, indicating high predictive accuracy.
conclusionsWe developed an RPA model to facilitate one-year survival prediction in patients with advanced cancer. The 4-leaf model incorporated only three readily available variables. Following external validation, this model may prove valuable in assisting clinicians with one-year survival prognostication.
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