ArticleJCO clinical cancer informatics2022
Development of a Machine Learning Model Using Limited Features to Predict 6-Month Mortality at Treatment Decision Points for Patients With Advanced Solid Tumors.
Article in JCO clinical cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The trial behind it
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Individualized Triplet Chemotherapy Decision-Making in Metastatic Colorectal Cancer: A Machine-Learning-Driven Study.Cancers · 2025Article
- Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.JCO clinical cancer informatics · 2024Article
- Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- External Validation of a Machine Learning Model to Predict 6-Month Mortality for Patients With Advanced Solid Tumors.JAMA network open · 2023Article
- Multi-Omic Integration of Blood-Based Tumor-Associated Genomic and Lipidomic Profiles Using Machine Learning Models in Metastatic Prostate Cancer.JCO clinical cancer informatics · 2023Article
- Simultaneous care in oncology: Assessment of benefit in relation to symptoms, sex, and age in 753 patients.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
purposePatients with advanced solid tumors may receive intensive treatments near the end of life. This study aimed to create a machine learning (ML) model using limited features to predict 6-month mortality at treatment decision points (TDPs).
methodsWe identified a cohort of adults with advanced solid tumors receiving care at a major cancer center from 2014 to 2020. We identified TDPs for new lines of therapy (LoTs) and confirmed mortality at 6 months after a TDP. Using extreme gradient boosting, ML models were developed, which used or derived features from a limited set of electronic health record data considering the literature, clinical relevance, variability, availability, and predictive importance using Shapley additive explanations scores. We predicted and observed 6-month mortality after a TDP and assessed a risk stratification strategy with different risk thresholds to support communication of chance of survival.
resultsFour thousand one hundred ninety-two patients were included. Patients had 7,056 TDPs, for which the 6-month mortality increased from 17.9% to 46.7% after starting first to sixth LoT, respectively. On the basis of internal validation, models using both 111 (Full) or 45 (Limited-45) features accurately predicted 6-month mortality (area under the curve ≥ 0.80). Using a 0.3 risk threshold in the Limited-45 model, the observed 6-month survival was 34% (95% CI, 28 to 40) versus 81% (95% CI, 81 to 82) among those classified with low or higher chance of survival, respectively. The positive predictive value of the Limited-45 model was 0.66 (95% CI, 0.60 to 0.72).
conclusionWe developed and validated a ML model using a limited set of 45 features readily derived from electronic health record data to predict 6-month prognosis in patients with advanced solid tumors. The model output may support shared decision making as patients consider the next LoT.
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