我们开发了一种新颖的基于当代人群的模型，用于预测肾上腺皮质癌 (ACC) 患者的癌症特异性生存 (CSS)，并将其与已建立的第 8 版美国癌症分期系统联合委员会 (AJCC) 进行比较。在监测、流行病学和最终结果数据库（2004-2020 年）中，我们确定了 1056 名 ACC 患者。单变量 Cox 回归模型解决了 CSS 问题。Harrell 的一致性指数 (C-index) 在 2000 次引导重新采样进行内部验证后量化了准确性。多变量 Cox 回归模型包含信息最丰富、具有统计显着性的预测变量。校准和决策曲线分析 (DCA) 在头对头比较中测试了多变量模型以及 AJCC。多变量模型中包括诊断时年龄（>60 岁与≤60 岁）、手术、T、N 和 M 分期。3 年 CSS 预测的多变量模型 C 指数为 0.795，而 AJCC 为 0.757。对于大多数可能的 CSS 预测值，多变量模型在 DCA 中的表现优于 AJCC。两种模型都表现出相似的校准特性。最后，多变量模型 CSS 预测概率的范围为 0.02-75.3%，而只有四个单个 AJCC 值，特别是 I 期为 73.2%，II 期为 69.7%，III 期为 46.6%，IV 期为 15.5%。多变量模型生成的 CSS 概率的最大好处适用于 AJCC I 期和 II 期患者。当 CSS 预测代表终点时，多变量模型比 AJCC 分期更准确。此外，多变量模型在 DCA 中的表现优于 AJCC。最后，当歧视针对 AJCC I 期和 II 期患者时，AJCC 似乎落后于多变量模型。

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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.