Cancer medicine · 2026
Mitigating Disparities in Prostate Cancer Survival Prediction Through Fairness-Aware Machine Learning Models.
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Do Hyungrok, Ranganath Rajesh, Murray Katie, Nayan Madhur
Аннотация
PURPOSE: Prediction models can contribute to disparities in care by performing unequally across demographic groups. While fairness-aware methods have been explored for binary outcomes, applications to survival analysis remain limited. This study compares two fairness-aware deep learning survival models to mitigate racial disparities in predicting survival after radical prostatectomy for prostate cancer.
METHODS: We used the National Cancer Database to train deep Cox proportional hazards models for overall survival. Two fairness-aware approaches, Fair Deep Cox Proportional Hazards Model (Fair DCPH) and Group Distributionally Robust Optimization Deep Cox Proportional Hazards Model (GroupDRO DCPH), were compared against a standard Deep Cox model (Baseline). Model fairness was assessed via cross-group and within-group concordance indices (C-index).
RESULTS: Among 418,968 included patients, 78.5% were White, with smaller proportions of Black (13.2%), Hispanic (4.5%), Asian (1.9%), and Other (2.0%) patients. The baseline DCPH model achieved a cross-group C-index of 0.699 for White patients but showed reduced performance for Black (0.678) and Hispanic (0.689) patients. Fairness-aware models improved cross-group C-indices; for Black patients, cross-group C-index increased to 0.692 (Fair DCPH) and 0.696 (GroupDRO DCPH); for Hispanic patients, to 0.693 and 0.697, respectively. Cross-group C-index also improved in the Asian subgroup, where the C-index rose from 0.696 (Baseline DCPH) to 0.702 (Fair DCPH) and 0.707 (GroupDRO DCPH), with minimal performance loss observed for White patients.
CONCLUSION: We benchmark two fairness-aware survival models that address racial disparities in post-prostatectomy survival prediction. These methods can be extended to other time-to-event models to ensure equitable care supported by fair prediction models.
METHODS: We used the National Cancer Database to train deep Cox proportional hazards models for overall survival. Two fairness-aware approaches, Fair Deep Cox Proportional Hazards Model (Fair DCPH) and Group Distributionally Robust Optimization Deep Cox Proportional Hazards Model (GroupDRO DCPH), were compared against a standard Deep Cox model (Baseline). Model fairness was assessed via cross-group and within-group concordance indices (C-index).
RESULTS: Among 418,968 included patients, 78.5% were White, with smaller proportions of Black (13.2%), Hispanic (4.5%), Asian (1.9%), and Other (2.0%) patients. The baseline DCPH model achieved a cross-group C-index of 0.699 for White patients but showed reduced performance for Black (0.678) and Hispanic (0.689) patients. Fairness-aware models improved cross-group C-indices; for Black patients, cross-group C-index increased to 0.692 (Fair DCPH) and 0.696 (GroupDRO DCPH); for Hispanic patients, to 0.693 and 0.697, respectively. Cross-group C-index also improved in the Asian subgroup, where the C-index rose from 0.696 (Baseline DCPH) to 0.702 (Fair DCPH) and 0.707 (GroupDRO DCPH), with minimal performance loss observed for White patients.
CONCLUSION: We benchmark two fairness-aware survival models that address racial disparities in post-prostatectomy survival prediction. These methods can be extended to other time-to-event models to ensure equitable care supported by fair prediction models.