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间变性甲状腺癌患者的风险分层:年龄的作用。

Risk stratification in patients with anaplastic thyroid carcinoma: role of age.

机构信息

Department of Thyroid & Parathyroid Surgery, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan, China.

出版信息

Endocrine. 2022 Aug;77(2):305-318. doi: 10.1007/s12020-022-03085-w. Epub 2022 May 24.

DOI:10.1007/s12020-022-03085-w
PMID:35608773
Abstract

BACKGROUND

Thyroid carcinoma is the only cancer that regards age as an important predictor of thyroid cancer-specific survival (CSS). While the 8th American Joint Committee on Cancer (AJCC) staging system raised the age cutoff from 45 to 55 years for differentiated thyroid carcinoma (DTC) to more accurately predict the prognosis, there is no new information regarding the role of age in the prognosis of anaplastic thyroid carcinoma (ATC). The aim of this study was to determine the optimal age cutoff values for mortality risk stratification in ATC patients. Furthermore, a nomogram to predict ATC CSS was developed in each age group.

METHODS

Patients diagnosed with ATC between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. After applying inclusion and exclusion criteria, a total of 1140 patients were enrolled as cohort 1 to describe the characteristics of ATC, while a total of 556 patients were included as cohort 2 to determine age cutoff values for risk stratification by X-tile program. Training set and testing set were randomly generated to develop and validate a predictive nomogram of CSS in each age group.

RESULTS

The 6-month, 1-year, and 2-year survival was 27.6%, 15.1%, and 6.2%, respectively, in cohort 1. X-tile program results showed that the optimal age cutoff values for mortality risk stratification were 65 and 85 years old (p < 0.001). Distant metastasis was independently associated with CSS in patients younger than 85 years old, and these patients benefited more from complete resection of the tumor and radiotherapy/chemotherapy. However, no predictors of CSS were identified in patients over 85 years old, and interventions (surgery, radiotherapy, and chemotherapy) targeting ATC had little role in disease control in these patients. The nomogram was developed and validated based on the independent CSS predictors in each age group. The C-index values of the training set and testing set were 0.735 [95% CI, 0.696-0.774] and 0.733 [95% CI, 0.663-0.804] for CSS in patients of ≤64 years old, while the values were 0.767 [95% CI, 0.730-0.804] and 0.783 [95% CI, 0.718-0.848] in patients of 65-84 years old. All of the C-index values were larger than 0.7, which showed acceptable prediction performance of the nomograms.

CONCLUSIONS

Age can be used as an auxiliary stratification factor of prognosis in ATC patients. The survival may be improved in patients younger than 85 years old if combination therapy (surgery, radiotherapy, and chemotherapy) was indicated and applicable, while no optimal therapeutic strategy was determined in patients older than 85 years old. The established nomograms can provide good prediction of CSS according to age group.

摘要

背景

甲状腺癌是唯一一种将年龄视为甲状腺癌特异性生存(CSS)的重要预测因素的癌症。虽然第 8 版美国癌症联合委员会(AJCC)分期系统将分化型甲状腺癌(DTC)的年龄截止值从 45 岁提高到 55 岁,以更准确地预测预后,但关于年龄在间变性甲状腺癌(ATC)预后中的作用尚无新信息。本研究旨在确定 ATC 患者死亡风险分层的最佳年龄截止值。此外,还在每个年龄组中开发了预测 ATC CSS 的列线图。

方法

在监测、流行病学和最终结果(SEER)数据库中确定了 2004 年至 2015 年间诊断为 ATC 的患者。在应用纳入和排除标准后,共有 1140 例患者被纳入队列 1 以描述 ATC 的特征,共有 556 例患者被纳入队列 2 以确定 X-tile 程序进行风险分层的年龄截止值。训练集和测试集被随机生成,以开发和验证每个年龄组的 CSS 预测列线图。

结果

队列 1 中的 6 个月、1 年和 2 年生存率分别为 27.6%、15.1%和 6.2%。X-tile 程序结果显示,用于死亡率风险分层的最佳年龄截止值分别为 65 岁和 85 岁(p<0.001)。远处转移是年龄小于 85 岁患者 CSS 的独立预后因素,这些患者从肿瘤完全切除和放疗/化疗中获益更多。然而,年龄大于 85 岁的患者中没有确定 CSS 的预测因素,针对 ATC 的干预措施(手术、放疗和化疗)在这些患者中对疾病控制作用不大。该列线图是基于每个年龄组中独立的 CSS 预测因素开发和验证的。训练集和测试集的 C 指数值分别为 0.735[95%CI,0.696-0.774]和 0.733[95%CI,0.663-0.804],适用于≤64 岁的患者,而 65-84 岁患者的 C 指数值分别为 0.767[95%CI,0.730-0.804]和 0.783[95%CI,0.718-0.848]。所有 C 指数值均大于 0.7,表明列线图具有良好的预测性能。

结论

年龄可作为 ATC 患者预后的辅助分层因素。如果适用并指示联合治疗(手术、放疗和化疗),年龄小于 85 岁的患者的生存可能会得到改善,而年龄大于 85 岁的患者则没有确定最佳治疗策略。根据年龄组建立的列线图可以提供良好的 CSS 预测。

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