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Abstract
Background
Adrenocortical carcinoma (ACC) is extremely rare in elderly patients. Thus, this study aimed to identify the incidence rate and develop nomogram models for predicting survival in elderly ACC patients.
Methods
Data of ACC patients aged >60 years from 1975 to 2016 were obtained from the Surveillance, Epidemiology, and End Results dataset. The national incidence rate was estimated, and survival was subjected to Kaplan–Meier analysis. A multivariate Cox regression model was used to identify predictors of survival. Nomograms were generated to predict survival, calibrated and internally validated.
Results
We identified 583 cases. Univariate analysis showed that patients with younger age (≤67 years), female sex, lower tumor grade, surgical treatment performed, and earlier European Network for the Study of Adrenal Tumors (ENSAT) stage had a better survival (P < 0.05). In the Cox regression analysis, no surgery performed (hazard ratio [HR]: 3.544, 95% CI: 1.142–10.995, P = 0.029 for overall survival [OS]; HR: 3.230, 95% CI: 1.040–10.034, P = 0.043 for disease-specific survival [DSS]) and advanced ENSAT stage (HR: 3.328, 95% CI: 1.628–6.801, P = 0.001 for OS; HR: 3.701, 95% CI: 1.682–8.141, P = 0.001 for DSS) were associated with worse outcomes. Age, sex, histologic grade, surgical resection, radiotherapy, and ENSAT stage were included in the nomograms, with a C-index of 0.692 for OS and 0.694 for DSS, demonstrating a good accuracy in predicting survival.
Conclusions
This study is the largest review of ACC in elderly patients. We present nomograms to predict survival in elderly ACC patients using clinicopathologic data, which could aid in accurate clinical decision-making.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Ethics approval
None. This study was exempted from institutional review board approval because it was based on a publicly available database.
Funding
This work was funded by the Youth Foundation of Zhongshan Hospital Affiliated to Fudan University (2019ZSQN48) and National Natural Science Foundation of China (82002828).