Developing a novel Comorbidities Index for predicting 10-year mortality in Prostate Cancer patients: A computational data-driven approach

• URL: http://arxiv.org/abs/2605.31213v2
• Date: Wed, 03 Jun 2026 07:26:25 GMT
• Title: Developing a novel Comorbidities Index for predicting 10-year mortality in Prostate Cancer patients: A computational data-driven approach
• Abstract summary: The Charlson Comorbidities Index (CCI) is a weighted additive index widely used to estimate ten-year mortality risk.<n>For candidates eligible for Radical Prostatectomy (RP), accurate estimation of ten-year other-cause mortality is essential.<n>We propose a data-driven framework to derive a comorbidity index tailored to PCa patients considered for RP.
• Score: 31.083156015669932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Charlson Comorbidities Index (CCI) is a weighted additive index widely used to estimate ten-year mortality risk, but its original weights may not reflect contemporary prognoses. This limitation is critical in Prostate Cancer (PCa), where radical treatment is recommended only for patients with a life expectancy of at least ten years. For candidates eligible for Radical Prostatectomy (RP), accurate estimation of ten-year other-cause mortality is essential to balance oncological benefit against competing risks and avoid overtreatment. We propose a data-driven framework to derive a comorbidity index tailored to PCa patients considered for RP. Using a retrospective single-institution cohort, we apply Population-Based Bio-Inspired Algorithms (PBBIAs) to recalibrate comorbidity weights and evolve alternative symbolic formulations optimized for ten-year survival discrimination. We compared six optimization strategies, including symbolic regression approaches based on Genetic Programming (GP), population-based metaheuristics, clinically validated baselines, and survival prediction models. Results show that GA, FST-PSO, and SLIM outperform both the original CCI and the PCCI, particularly when PCa-specific variables are included, improving the Concordance Index by up to 0.1. GPLearn yields compact and interpretable models with competitive performance. Overall, the proposed approach provides an updated and interpretable tool to improve patient selection for RP.

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