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External validation of the S-GRAS score for predicting recurrence in patients with adrenocortical carcinoma:
Paula Jimenez-Fonseca1, Cristina Álvarez-Escola2, Inmaculada Ballester Navarro3
1Medical Oncology Department, Hospital Universitario Central de Asturias, ISPA, Oviedo, 33011, Spain.
Background:
Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with variable outcomes post-adrenalectomy. The S-GRAS score integrates 5 clinical and pathological factors to predict prognosis but requires external validation in diverse settings.
Methods:
We validated the S-GRAS score in 138 ACC patients from the Spanish ICARO-GETTHI/SEEN registry (1998-2023). Model performance was assessed using discrimination, calibration, and accuracy. Exploratory refinements included non-linear modeling of age and Ki-67% and an expanded model incorporating venous invasion and tumor size. Cox models examined the interaction between S-GRAS and adjuvant mitotane.
Results:
A total of 76 recurrence events were recorded. The S-GRAS score demonstrated good discrimination for overall survival (C-index 0.706, 95% CI, 0.628-0.785) and recurrence-free survival (C-index 0.673, 95% CI, 0.601-0.745) with well-calibrated predictions. Five-year survival rates differed significantly across score groups: 100% for scores 0-1, 81.6% for 2-3, 55% for 4-5, and 33.8% for 6-7. Non-linear modeling of Ki-67% improved performance (C-index 0.738 for RFS, 0.761 for OS), but adding clinical variables offered minimal benefit, leaving 75% of recurrence variability unexplained. Higher S-GRAS scores correlated with increased mitotane benefit (HR 0.57, 95% CI, 0.34-0.97 for score 4; HR 0.46, 95% CI, 0.23-0.94 for score 5), indicating a potential incremental benefit pattern.
Conclusions:
Our findings validate the S-GRAS score in a multicenter cohort and support its use in identifying candidates for adjuvant mitotane. Non-linear modeling of Ki-67% enhances predictive precision without increasing complexity, but the performance plateau of clinical variables suggests that integrating molecular biomarkers may be necessary to improve prognostic accuracy.
