ArticleJCO clinical cancer informatics2021
Risk Prediction Using Bayesian Networks: An Immunotherapy Case Study in Patients With Metastatic Renal Cell Carcinoma.
Article in JCO clinical cancer informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Explainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study.NPJ precision oncology · 2026Article
- Artificial intelligence and transforming cancer care.Discover oncology · 2026Review
- Bayesian networks as prognostic models in oncology: a systematic review and recommendations for clinical practice.BMJ oncology · 2026Article
- Immunotherapy and Cancer: The Multi-Omics Perspective.International journal of molecular sciences · 2024Review
- A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer.Frontiers in immunology · 2021Article
Corrections and comments
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Authors and funding
13 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo address the need for more accurate risk stratification models for cancer immuno-oncology, this study aimed to develop a machine-learned Bayesian network model (BNM) for predicting outcomes in patients with metastatic renal cell carcinoma (mRCC) being treated with immunotherapy.
methodsPatient-level data from the randomized, phase III CheckMate 025 clinical trial comparing nivolumab with everolimus for second-line treatment in patients with mRCC were used to develop the BNM. Outcomes of interest were overall survival (OS), all-cause adverse events, and treatment-related adverse events (TRAE) over 36 months after treatment initiation. External validation of the model's predictions for OS was conducted using data from select centers from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
resultsAreas under the receiver operating characteristic curve (AUCs) for BNM-based classification of OS using baseline data were 0.74, 0.71, and 0.68 over months 12, 24, and 36, respectively. AUC for OS at 12 months increased to 0.86 when treatment response and progression status in year 1 were included as predictors; progression and response at 12 months were highly prognostic of all outcomes over the 36-month period. AUCs for adverse events and treatment-related adverse events were approximately 0.6 at 12 months but increased to approximately 0.7 by 36 months. Sensitivity analysis comparing the BNM with machine learning classifiers showed comparable performance. Test AUC on IMDC data for 12-month OS was 0.71 despite several variable imbalances. Notably, the BNM outperformed the IMDC risk score alone.
conclusionThe validated BNM performed well at prediction using baseline data, particularly with the inclusion of response and progression at 12 months. Additionally, the results suggest that 12 months of follow-up data alone may be sufficient to inform long-term survival projections in patients with mRCC.
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