Zheng Yunfeng, Yang Fan, Liu Gaohua, Wu Xixi, Xie Langting, Hu Ran, Luo Xiaoxiao, Yuan Rui
Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Hangzhou, Zhejiang, China.
Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
Int J Med Sci. 2025 Aug 16;22(14):3789-3801. doi: 10.7150/ijms.107134. eCollection 2025.
: This study aims to develop a robust prediction model using the ProMisE molecular classification and the prognostic immune-inflammatory-nutritional score to predict recurrence in stage I-III endometrial cancer, thereby enabling risk stratification of high-risk patients. : The clinical data of 582 patients (365 in the training cohort and 217 in the validation cohort) were collected from multiple large cancer centers from patients with stage I-III endometrial cancer who underwent surgical resection between August 2019 and February 2022. Cox proportional hazards regression analysis was used to identify the risk factors for recurrence-free survival (RFS). The concordance index (C-index), area under the receiver operating characteristic (ROC) curves, calibration plots, and decision curve analyses (DCA) were used to assess discrimination and clinical utility of the model. : Patients with a hemoglobin, albumin, lymphocyte, and platelet (HALP) score ≤ 31.70 tended to have lower BMI ( = 0.017), advanced FIGO stage ( = 0.016), deep myometrial invasion ( < 0.001), and higher serum Ca125 levels ( < 0.001). Multivariate Cox regression analysis showed that age, FIGO stage, grade, LVSI, Ca125, ProMisE molecular subgroup, HALP score, and adjuvant therapy were independent prognostic factors for RFS in patients with endometrial cancer. A nomogram for predicting RFS was established, and patients were stratified into high- and low-risk groups based on the RFS model. : The preoperative HALP score serves as a reliable predictor of RFS in endometrial cancer. A nomogram combining the HALP score, ProMisE molecular subtyping, and clinical parameters can assist clinicians in identifying high-risk patients for recurrence. These patients may benefit from early triage and more intensive monitoring.
本研究旨在利用ProMisE分子分类和预后免疫炎症营养评分开发一种强大的预测模型，以预测Ⅰ-Ⅲ期子宫内膜癌的复发情况，从而实现高危患者的风险分层。从多个大型癌症中心收集了582例患者（训练队列365例，验证队列217例）的临床数据，这些患者为2019年8月至2022年2月期间接受手术切除的Ⅰ-Ⅲ期子宫内膜癌患者。采用Cox比例风险回归分析确定无复发生存期（RFS）的危险因素。使用一致性指数（C指数）、受试者操作特征（ROC）曲线下面积、校准图和决策曲线分析（DCA）来评估模型的辨别力和临床实用性。血红蛋白、白蛋白、淋巴细胞和血小板（HALP）评分≤31.70的患者往往BMI较低（P = 0.017）、国际妇产科联盟（FIGO）分期较晚（P = 0.016）、肌层浸润较深（P < 0.001）且血清Ca125水平较高（P < 0.001）。多因素Cox回归分析显示，年龄、FIGO分期、分级、淋巴血管间隙浸润（LVSI）、Ca125、ProMisE分子亚组、HALP评分和辅助治疗是子宫内膜癌患者RFS的独立预后因素。建立了预测RFS的列线图，并根据RFS模型将患者分为高危和低危组。术前HALP评分可作为子宫内膜癌RFS的可靠预测指标。结合HALP评分、ProMisE分子分型和临床参数的列线图可帮助临床医生识别复发高危患者。这些患者可能受益于早期分诊和更密切的监测。
