ArticleThe Journal of urology2019
The Relative Importance of Race Compared to Health Care and Social Factors in Predicting Prostate Cancer Mortality: A Random Forest Approach.
Article in The Journal of urology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of Factors Affecting Prostate Cancer Using Machine Learning Methods: A Systematic Review.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Machine learning approaches to optimize the integration of sociodemographic factors for predicting cancer-specific survival among patients with high-risk prostate cancer.Current urology · 2026Article
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- TCGA Expression Analyses of 10 Carcinoma Types Reveal Clinically Significant Racial Differences.Cancers · 2023Article
- Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.Current urology reports · 2023Review
- Investigating the racial gap in prostate cancer screening with prostate-specific antigen among younger men from 2012 to 2020.JNCI cancer spectrum · 2023Article
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Variation in and Factors Associated With US County-Level Cancer Mortality, 2008-2019.JAMA network open · 2022Article
- Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists.The Urologic clinics of North America · 2022Review
- Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network.Frontiers in public health · 2022Article
- A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects.SSM - population health · 2021Article
- A Data Science Approach to Analyze the Association of Socioeconomic and Environmental Conditions With Disparities in Pediatric Surgery.Frontiers in pediatrics · 2021Article
- The Impact of African Ancestry on Prostate Cancer Disparities in the Era of Precision Medicine.Genes · 2020Review
- Factors associated with appropriate and low-value PSA testing.Cancer epidemiology · 2020Article
- Lack of Racial Survival Differences in Metastatic Prostate Cancer in National Cancer Data Base (NCDB): A Different Finding Compared to Non-metastatic Disease.Frontiers in oncology · 2020Article
- Predicting long-term multicategory cause of death in patients with prostate cancer: random forest versus multinomial model.American journal of cancer research · 2020Article
- Classify multicategory outcome in patients with lung adenocarcinoma using clinical, transcriptomic and clinico-transcriptomic data: machine learning versus multinomial models.American journal of cancer research · 2020Article
- Association of Treatment With 5α-Reductase Inhibitors and Prostate Cancer Mortality Among Older Adults.JAMA network open · 2019Article
- Article
- Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy.Technology in cancer research & treatmentArticle
Corrections and comments
- Commented on by
- Erratum issuedERRATUM.2020
Authors and funding
7 authors.
Funding
Abstract
purposeThere is a differential in prostate cancer mortality between black and white men. Advances in precision medicine have shifted the research focus toward underlying genetic differences. However, nonbiological factors may have a large role in these observed disparities. Therefore, we sought to measure the relative importance of race compared to health care and social factors on prostate cancer specific mortality. MATERIALS AND
methodsUsing the SEER (Surveillance, Epidemiology, and End Results) database we identified 514,878 men diagnosed with prostate cancer at age 40 years or greater between 2004 and 2012. We also selected a subset of black and white men matched by age, stage and birth year. We stratified patients by age 40 to 54, 55 to 69 and 70 years or older and disease stage, resulting in 18 groups. By applying random forest methods with variable importance measures we analyzed 15 variables and interactions across 4 categories of factors (tumor characteristics, race, and health care and social factors) and the relative importance for prostate cancer specific mortality.
resultsTumor characteristics at diagnosis were the most important factors for prostate cancer mortality. Across all groups race was less than 5% as important as tumor characteristics and only more important than health care and social factors in 2 of the 18 groups. Although race had a significant impact, health care and social factors known to be associated with racial disparities had greater or similarly important effects across all ages and stages.
conclusionsEradicating disparities in prostate cancer survival will require a multipronged approach, including advances in precision medicine. Disparities will persist unless health care access and social equality are achieved among all populations.
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