S-GRAS score performs better than a model from SEER for patients with adrenocortical carcinoma.

Purpose: To externally validate the performance of the S-GRAS score and a model from the Surveillance, Epidemiology, and End Results (SEER) database in a Chinese cohort of patients with adrenocortical carcinoma (ACC).

Methods: We first developed a model using data from the SEER database, after which we retrospectively reviewed 51 ACC patients hospitalized between 2013 and 2018, and we finally validated the model and S-GRAS score in this Chinese cohort.

Results: Patient age at diagnosis, tumor size, TNM stage, and radiotherapy were used to construct the model, and the Harrell's C-index of the model in the training set was 0.725 (95% CI: 0.682-0.768). However, the 5-year area under the curve (AUC) of the model in the validation cohort was 0.598 (95% CI: 0.487-0.708). The 5-year AUC of the ENSAT stage was 0.640 (95% CI: 0.543-0.737), but the Kaplan-Meier curves of stages I and II overlapped in the validation cohort. The resection status (P = 0.066), age (P=0.68), Ki67 (P = 0.69), and symptoms (P = 0.66) did not have a significant impact on cancer-specific survival in the validation cohort. In contrast, the S-GRAS score group showed better discrimination (5-year AUC: 0.683, 95% CI: 0.602-0.764) than the SEER model or the ENSAT stage.

Conclusion: The SEER model showed favorable discrimination and calibration ability in the training set, but it failed to distinguish patients with various prognoses in our institution. In contrast, the S-GRAS score could effectively stratify patients with different outcomes.