Contrast CT radiomic features add value to prediction of prognosis in adrenal cortical carcinoma

Objective Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy with poor prognosis due to high postoperative recurrence rates. The aim of this study is to develop a contrast CT radiomic feature-based prognosis prediction model for ACC and evaluate its performance by comparison with ENSAT staging system and S-GRAS score. Methods Included in this study were 39 ACC patients, from which we extracted 1411 radiomic features. Using cross-validated least absolute shrinkage and selection operator regression (cv-LASSO regression), we generated a radiomic index. Additionally, we further validated the radiomic index using both univariate and multivariate Cox regression analyses. We constructed a radiomic nomogram that incorporated the radiomic signature and compared it with ENSAT stage and S-GRAS score in terms of calibration, discrimination and clinical usefulnes. Results In this study, the average progression free survival (PFS) of 39 patients was 20.4 (IQR 9.1–60.1) months and the average overall survival (OS) was 57.8 (IQR 32.4–NA). The generated radiomic features were significantly associated with PFS, OS, independent of clinical-pathologic risk factors (HR 0.16, 95%CI 0.02–0.99, p = 0.05; HR 0.20, 95%CI 0.04–1.07, p = 0.06, respectively). The radiomic index, ENSAT stage, resection status, and Ki67% index incorporated nomogram exhibited better performance for both PFS and OS prediction as compared with the S-GRAS and ENSAT nomogram (C-index: 0.75 vs. C-index: 0.68, p = 0.030 and 0.67, p = 0.025; C-index: 0.78 vs. C-index: 0.72, p = 0.003 and 0.73, p = 0.006). Calibration curve analysis showed that the radiomics-based model performs best in predicting the two-year PFS and the three-year OS. Decision curve analysis demonstrated that the radiomic index nomogram outperformed the S-GRAS and ENSAT nomogram in predicting the two-year PFS and the three-year OS. Conclusion The contrast CT radiomic-based nomogram performed better than S-GRAS or ENSAT in predicting PFS and OS in ACC patients.