Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, P. R. China.
Department of Radiation Oncology, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, 543002, P. R. China.
Cancer Commun (Lond). 2021 Jan;41(1):37-50. doi: 10.1002/cac2.12113. Epub 2020 Dec 3.
Conditional survival (CS) provides dynamic prognostic estimates by considering the patients existing survival time. Since CS for endemic nasopharyngeal carcinoma (NPC) is lacking, we aimed to assess the CS of endemic NPC and establish a web-based calculator to predict individualized, conditional site-specific recurrence risk.
Using an NPC-specific database with a big-data intelligence platform, 10,058 endemic patients with non-metastatic stage I-IVA NPC receiving intensity-modulated radiotherapy with or without chemotherapy between April 2009 and December 2015 were investigated. Crude CS estimates of conditional overall survival (COS), conditional disease-free survival (CDFS), conditional locoregional relapse-free survival (CLRRFS), conditional distant metastasis-free survival (CDMFS), and conditional NPC-specific survival (CNPC-SS) were calculated. Covariate-adjusted CS estimates were generated using inverse probability weighting. A prediction model was established using competing risk models and was externally validated with an independent, non-metastatic stage I-IVA NPC cohort undergoing intensity-modulated radiotherapy with or without chemotherapy (n = 601) at another institution.
The median follow-up of the primary cohort was 67.2 months. The 5-year COS, CDFS, CLRRFS, CDMFS, and CNPC-SS increased from 86.2%, 78.1%, 89.8%, 87.3%, and 87.6% at diagnosis to 87.3%, 87.7%, 94.4%, 96.0%, and 90.1%, respectively, for an existing survival time of 3 years since diagnosis. Differences in CS estimates between prognostic factor subgroups of each endpoint were noticeable at diagnosis but diminished with time, whereas an ever-increasing disparity in CS between different age subgroups was observed over time. Notably, the prognoses of patients that were poor at diagnosis improved greatly as patients survived longer. For individualized CS predictions, we developed a web-based model to estimate the conditional risk of local (C-index, 0.656), regional (0.667), bone (0.742), lung (0.681), and liver (0.711) recurrence, which significantly outperformed the current staging system (P < 0.001). The performance of this web-based model was further validated using an external validation cohort (median follow-up, 61.3 months), with C-indices of 0.672, 0.736, 0.754, 0.663, and 0.721, respectively.
We characterized the CS of endemic NPC in the largest cohort to date. Moreover, we established a web-based calculator to predict the CS of site-specific recurrence, which may help to tailor individualized, risk-based, time-adapted follow-up strategies.
条件生存（CS）通过考虑患者现有的生存时间来提供动态预后估计。由于缺乏地方性鼻咽癌（NPC）的 CS 数据，我们旨在评估地方性 NPC 的 CS，并建立一个基于网络的计算器来预测个体化、有条件的局部特定复发风险。
使用具有大数据智能平台的 NPC 特异性数据库，调查了 2009 年 4 月至 2015 年 12 月期间接受调强放疗联合或不联合化疗的 10058 例非转移性 I-IVA 期地方性 NPC 患者。计算了条件总生存（COS）、条件无病生存（CDFS）、条件局部区域无复发生存（CLRRFS）、条件远处无转移生存（CDMFS）和条件 NPC 特异性生存（CNPC-SS）的粗 CS 估计值。使用逆概率加权法生成协变量调整后的 CS 估计值。使用竞争风险模型建立预测模型，并使用来自另一机构的接受调强放疗联合或不联合化疗的独立非转移性 I-IVA 期 NPC 队列（n=601）进行外部验证。
主要队列的中位随访时间为 67.2 个月。5 年的 COS、CDFS、CLRRFS、CDMFS 和 CNPC-SS 从诊断时的 86.2%、78.1%、89.8%、87.3%和 87.6%增加到诊断后 3 年的 87.3%、87.7%、94.4%、96.0%和 90.1%。在每个终点的预后因素亚组中，CS 估计值的差异在诊断时明显，但随时间的推移而减小，而不同年龄亚组之间 CS 的差异随时间的推移而逐渐增大。值得注意的是，随着患者生存时间的延长，诊断时预后较差的患者的预后有了很大的改善。为了进行个体化 CS 预测，我们开发了一个基于网络的模型来估计局部（C 指数，0.656）、区域（0.667）、骨骼（0.742）、肺（0.681）和肝脏（0.711）复发的条件风险，该模型显著优于当前的分期系统（P<0.001）。使用外部验证队列（中位随访时间为 61.3 个月）进一步验证了该基于网络的模型的性能，其 C 指数分别为 0.672、0.736、0.754、0.663 和 0.721。
我们在迄今为止最大的队列中描述了地方性 NPC 的 CS。此外，我们建立了一个基于网络的计算器来预测局部特定复发的 CS，这可能有助于制定个体化、基于风险、适应时间的随访策略。
