Wang Yuqi, Zheng Yunfeng, Tian Chenfan, Yu Jiaxin, Rao Kunying, Zeng Na, Jiang Peng
Department of Gynecology, Yubei District People's Hospital, Chongqing, 401120, People's Republic of China.
Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China.
J Inflamm Res. 2024 Dec 23;17:11437-11449. doi: 10.2147/JIR.S494716. eCollection 2024.
Surgery is the best approach to treat endometrial cancer (EC); however, there is currently a deficiency in effective scoring systems for predicting EC recurrence post-surgical resection. This study aims to develop a clinicopathological-inflammatory parameters-based nomogram to accurately predict the postoperative recurrence-free survival (RFS) rate of EC patients.
A training set containing 1068 patients and an independent validation set consisting of 537 patients were employed in this retrospective study. The prognostic factors for RFS were identified by univariable and multivariable Cox proportional hazards regression analyses, and integrated into nomogram. The C-index, area under the curves (AUC), and calibration curves were employed to determine the predictive discriminability and accuracy of nomogram. Utilizing the nomogram, patients were stratified into low- and high-risk groups, and the Kaplan-Meier survival curve was further employed to assess the clinical efficacy of the model.
Cox regression analyses revealed that age (HR = 1.769, = 0.002), FIGO staging (HR = 1.790, = 0.018), LVSI (HR = 1.654, = 0.017), Ca125 (HR = 1.532, = 0.023), myometrial invasion (HR = 1.865, = 0.001), cervical stromal invasion (HR = 1.655, = 0.033), histology (HR = 2.637, < 0.001), p53 expression (HR = 1.706, = 0.002), PLR (HR = 1.971, = 0.003), SIRI (HR = 2.187, P = 0.003), and adjuvant treatment (HR = 0.521, = 0.003) were independent prognostic factors for RFS in patients with EC. A combined clinicopathologic-inflammatory parameters model was constructed, which outperformed the single-indicator model and other established models in predicting the 1-, 3-, and 5-year RFS rates in patients with EC.
The nomogram demonstrated sufficient accuracy in predicting the RFS probabilities of EC, enabling personalized clinical decision-making for future clinical endeavors.
手术是治疗子宫内膜癌（EC）的最佳方法；然而，目前在预测EC手术切除后复发的有效评分系统方面存在不足。本研究旨在开发一种基于临床病理炎症参数的列线图，以准确预测EC患者术后无复发生存（RFS）率。
本回顾性研究采用了一个包含1068例患者的训练集和一个由537例患者组成的独立验证集。通过单变量和多变量Cox比例风险回归分析确定RFS的预后因素，并将其纳入列线图。采用C指数、曲线下面积（AUC）和校准曲线来确定列线图的预测辨别力和准确性。利用列线图，将患者分为低风险和高风险组，并进一步采用Kaplan-Meier生存曲线评估模型的临床疗效。
Cox回归分析显示，年龄（HR = 1.769，P = 0.002）、国际妇产科联盟（FIGO）分期（HR = 1.790，P = 0.018）、淋巴血管间隙浸润（LVSI）（HR = 1.654，P = 0.017）、Ca125（HR = 1.532，P = 0.023）、肌层浸润（HR = 1.865，P = 0.001）、宫颈间质浸润（HR = 1.655，P = 0.033）、组织学（HR = 2.637，P < 0.001）、p53表达（HR = 1.706，P = 0.002）、血小板与淋巴细胞比值（PLR）（HR = 1.971，P = 0.003）、全身免疫炎症反应指数（SIRI）（HR = 2.187，P = 0.003）和辅助治疗（HR = 0.521，P = 0.003）是EC患者RFS的独立预后因素。构建了一个联合临床病理炎症参数模型，该模型在预测EC患者1年、3年和5年RFS率方面优于单指标模型和其他已建立的模型。
列线图在预测EC的RFS概率方面显示出足够的准确性，可为未来的临床实践提供个性化的临床决策依据。
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