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.

翻译：查尔森共病指数（CCI）是一种广泛用于估算十年死亡风险的加权加性指数，但其原始权重可能无法反映当代预后情况。这一限制在前列腺癌（PCa）中尤为关键，因为根治性治疗仅推荐给预期寿命至少十年的患者。对于符合根治性前列腺切除术（RP）条件的候选人，准确估算十年其他原因死亡率对于平衡肿瘤学获益与竞争风险、避免过度治疗至关重要。我们提出了一种数据驱动框架，旨在为考虑接受RP的PCa患者定制共病指数。利用回顾性单中心队列数据，我们应用基于种群的生物启发算法（PBBIAs）重新校准共病权重，并演化出针对十年生存判别优化的替代符号公式。我们比较了六种优化策略，包括基于遗传编程（GP）的符号回归方法、基于种群的元启发式算法、临床验证基线和生存预测模型。结果表明，遗传算法（GA）、FST-PSO和SLIM均优于原始CCI和PCCI，尤其是在纳入PCa特异性变量时，一致性指数提升了高达0.1。GPLearn生成了性能具有竞争力的紧凑且可解释的模型。总体而言，所提出的方法为改进RP患者筛选提供了更新且可解释的工具。