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Abstract
Introduction: Prostate cancer (CaP) is the second most common cancer in men globally and a leading cause of cancer-related mortality, particularly among older men. In the United States, disparities in incidence, stage at diagnosis, and outcomes persist across racial, socioeconomic, and geographic lines. Men in medically underserved regions like Appalachia experience higher mortality due to limited access to timely screening and treatment. Given the complex interplay of clinical, behavioral, and sociodemographic factors, machine learning (ML) offers promise in identifying survival predictors that traditional models may overlook. This study applies ML models to assess the impact of age at diagnosis, treatment modality, and other sociodemographic and clinical factors on CaP survival outcomes using data from the Kentucky Cancer Registry (KCR). Methods: We retrospectively analyzed 37 893 CaP cases diagnosed from 2010 to 2022 using KCR data linked to mortality records. Kaplan-Meier (KM), Random Survival Forest (RSF), and Elastic Net regression were used to estimate survival, assess variable importance, and evaluate predictive performance. Missing data were handled via multiple imputation, and leave-one-out cross-validation minimized overfitting. Models were compared using out-of-bag error, continuous ranked probability scores (CRPS), and interpretability. Results: ML models identified age at diagnosis, treatment modality, and smoking status as the top survival predictors. RSF showed that age was approximately 2.5 times more influential than treatment type. Patients diagnosed before age 60 were more likely to undergo surgery and had lower mortality, while older men more often received non-surgical therapies and experienced worse outcomes. Insurance status, tumor grade, lymph node involvement, and marital status also affected survival, with pronounced disparities among uninsured and government-insured patients. Conclusions: ML models highlighted age, smoking, and treatment type as key predictors of CaP survival. Findings support early screening, equitable treatment access, and behavioral health integration to reduce disparities in high-risk areas such as Kentucky and Appalachia.
Document Type
Article
Publication Date
1-1-2025
Digital Object Identifier (DOI)
10.1177/10732748251384702
Archival?
Archival
Repository Citation
Adatorwovor, Reuben; Oladapo, Rasaq; Ghasemi, Parisa; Kumar, Gaurav; Morton, Daniel J.; Ogunsanya, Motolani E.; and Abraham, Olufunmilola, "Leveraging Machine Learning Models to Explore Disparities in Prostate Cancer Diagnosis, Treatment, and Survival" (2025). Biostatistics Faculty Publications. 128.
https://uknowledge.uky.edu/biostatistics_facpub/128
Notes/Citation Information
Publisher Copyright: © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).