Leveraging Machine Learning Models to Explore Disparities in Prostate Cancer Diagnosis, Treatment, and Survival
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning models identified age, treatment, and smoking as key prostate cancer survival predictors. Younger patients and those receiving surgery had better outcomes, highlighting disparities in underserved areas.
Area Of Science
- Oncology
- Data Science
- Public Health
Background
- Prostate cancer (CaP) is a leading cause of cancer mortality globally, with significant disparities in the US.
- Medically underserved regions like Appalachia face higher CaP mortality due to access barriers.
- Machine learning (ML) can identify complex survival predictors in CaP.
Purpose Of The Study
- To apply ML models to identify key predictors of CaP survival.
- To assess the impact of sociodemographic and clinical factors on CaP outcomes.
- To analyze CaP survival using Kentucky Cancer Registry data.
Main Methods
- Retrospective analysis of 37,893 CaP cases (2010-2022) from the Kentucky Cancer Registry.
- Utilized Kaplan-Meier, Random Survival Forest (RSF), and Elastic Net regression for survival estimation and variable importance.
- Employed multiple imputation for missing data and leave-one-out cross-validation.
Main Results
- Age at diagnosis, treatment modality, and smoking status were top survival predictors.
- Age was ~2.5 times more influential than treatment type in RSF models.
- Younger patients (<60) had better outcomes; insurance status, tumor grade, and lymph node involvement also showed disparities.
Conclusions
- ML models effectively identified age, smoking, and treatment as critical CaP survival predictors.
- Findings underscore the need for early screening and equitable treatment access.
- Interventions should address disparities in high-risk regions like Kentucky and Appalachia.
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