Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Cancer Healthcare Research Branch, Center for Uterine Cancer, and Center for Clinical Trials, Research Institute and Hospital and Cancer Control and Policy, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea.
Cancer Res Treat. 2020 Jan;52(1):320-333. doi: 10.4143/crt.2019.124. Epub 2019 Aug 5.
We aimed to develop and validate individual prognostic models in a large cohort of cervical cancer patients that were primarily treated with radical hysterectomy.
We analyzed 1,441 patients with early-stage cervical cancer treated between 2000 and 2008 from the Korean Gynecologic Oncology Group multi-institutional cohort: a train cohort (n=788) and a test cohort (n=653). Models predicting the risk for overall survival (OS), disease- free survival (DFS), lymphatic recurrence and hematogenous recurrence were developed using Cox analysis and stepwise backward selection and best-model options. The prognostic performance of each model was assessed in an independent patient cohort. Model-classified risk groups were compared to groups based on traditional risk factors.
Independent risk factors for OS, DFS, lymphatic recurrence, and hematogenous recurrence were identified for prediction model development. Different combinations of risk factors were shown for each outcome with best predictive value. In train cohort, area under the curve (AUC) at 2 and 5 years were 0.842/0.836 for recurrence, and 0.939/0.882 for OS. When applied to a test cohort, the model also showed accurate prediction result (AUC at 2 and 5 years were 0.799/0.723 for recurrence, and 0.844/0.806 for OS, respectively). The Kaplan-Meier plot by proposed model-classified risk groups showed more distinctive survival differences between each risk group.
We developed prognostic models for OS, DFS, lymphatic and hematogenous recurrence in patients with early-stage cervical cancer. Combining weighted clinicopathologic factors, the proposed model can give more individualized predictions in clinical practice.
我们旨在为主要接受根治性子宫切除术治疗的宫颈癌患者建立并验证大型队列中的个体化预后模型。
我们分析了来自韩国妇科肿瘤学组多机构队列的 1441 例早期宫颈癌患者（2000 年至 2008 年治疗）：训练队列（n=788）和测试队列（n=653）。使用 Cox 分析和逐步后退选择以及最佳模型选项，对预测总生存（OS）、无病生存（DFS）、淋巴复发和血行复发风险的模型进行了开发。在独立患者队列中评估了每个模型的预后性能。将模型分类的风险组与基于传统危险因素的组进行了比较。
确定了用于预测模型开发的 OS、DFS、淋巴复发和血行复发的独立危险因素。对于每种结果，显示了具有最佳预测值的不同风险因素组合。在训练队列中，2 年和 5 年的曲线下面积（AUC）分别为 0.842/0.836 用于复发，0.939/0.882 用于 OS。当应用于测试队列时，该模型也显示了准确的预测结果（2 年和 5 年的 AUC 分别为 0.799/0.723 用于复发，0.844/0.806 用于 OS）。根据所提出的模型分类风险组的 Kaplan-Meier 图显示了每个风险组之间更明显的生存差异。
我们为早期宫颈癌患者建立了 OS、DFS、淋巴和血行复发的预后模型。结合加权临床病理因素，该模型可以在临床实践中提供更个性化的预测。
