We developed a novel contemporary population-based model for predicting cancer-specific survival (CSS) in adrenocortical carcinoma (ACC) patients and compared it with the established 8th edition of the American Joint Committee on Cancer staging system (AJCC). Within the Surveillance, Epidemiology, and End Results database (2004-2020), we identified 1056 ACC patients. Univariable Cox regression model addressed CSS. Harrell's concordance index (C-index) quantified accuracy after 2000 bootstrap resamples for internal validation. The multivariable Cox regression model included the most informative, statistically significant predictors. Calibration and decision curve analyses (DCAs) tested the multivariable model as well as AJCC in head-to-head comparisons. Age at diagnosis (>60 vs ≤60 years), surgery, T, N, and M stages were included in the multivariable model. Multivariable model C-index for 3-year CSS prediction was 0.795 vs 0.757 for AJCC. Multivariable model outperformed AJCC in DCAs for the majority of possible CSS-predicted values. Both models exhibited similar calibration properties. Finally, the range of the multivariable model CSS predicted probabilities raged 0.02-75.3% versus only four single AJCC values, specifically 73.2% for stage I, 69.7% for stage II, 46.6% for stage III, and 15.5% for stage IV. The greatest benefit of the multivariable model-generated CSS probabilities applied to AJCC stage I and II patients. The multivariable model was more accurate than AJCC staging when CSS predictions represented the endpoint. Additionally, the multivariable model outperformed AJCC in DCAs. Finally, the AJCC appeared to lag behind the multivariable model when discrimination addressed AJCC stage I and II patients.
Keywords: adrenocortical carcinoma; cancer-specific mortality; multivariable model; prognostic model.