Distribución de lesiones y pronóstico del carcinoma de células renales con metástasis ósea: Un nuevo modelo de evaluación basado en bosques de supervivencia aleatorios

The prognostic value of lesion distribution in renal cell carcinoma bone metastasis (RCC-BM) is unclear. This study aimed to quantify the association between BM distribution and prognosis in RCC-BM patients and to employ a predictive model based on the random survival forests (RSF) algorithm. At first BM diagnosis, 122 patients were stratified by Memorial Sloan-Kettering Cancer Center (MSKCC)/Motzer risk score and classified into locoregional (21.3%), stochastic (56.6%), and extensive (22.1%) groups based on bone lesion distribution. Spinal and pelvic involvement was observed in 39.3% and 35.2% of patients. Univariate, logistic regression, and Kaplan-Meier survival analyses indicated that locoregional spread, spinal involvement (odds ratio [OR] 3.30; 95% confidence interval [CI] 1.20-9.09), and advanced age (OR 1.04; 95% CI 1.00-1.08; p < .05) correlated with higher risk stratifications, while pelvic metastasis was linked to shorter median overall survival (32 vs. 49 months; p < .05). The RSF model was trained in 70% and validated in 30% of the series, incorporating spatial lesion involvement (pelvic, spinal, and upper extremities involvement), MSKCC/Motzer score, and age as principal contributing variables. Time-dependent area under the curve (AUC) values achieved in single-split validation for 1- and 3-year survival were 0.90 and 0.87. Consistent performance was observed across 100 repeated splits, with median AUCs of 0.89, 0.86, and 0.89 for 1-, 3-, and 5-year survival, respectively. A cut-off value of 15.03 effectively separated high- and low-risk groups (p < .05). RSF demonstrated superior accuracy over Cox regression (median AUC 0.89 vs. 0.59 for 1-year survival). Overall, integrating bone lesion patterns into RSF modeling facilitates personalized prognosis and supports more precise care in RCC-BM.