Development of a Predictive Model for Surgical Outcomes in Epithelial Ovarian Cancer Based on Clinical Characteristics
Year: 2025
Type: article
Study design: Observational Study
Abstract: 目的：探讨上皮性卵巢癌(EOC)患者临床病理特征及炎症标志物对肿瘤细胞减灭术(CRS)结局的预测价值，构建手术结局预测模型以指导个体化治疗。方法：回顾性纳入2018年1月1日至2023年12月31日河南省人民医院152例初次接受CRS的EOC患者；采用LASSO回归结合单因素/多因素Logistic回归筛选手术结局(R0切除)的独立危险因素，并构建列线图(nomogram)预测模型。通过ROC曲线、校正曲线及决策曲线评估模型性能。结果：1、152例患者中，R0切除率61.8% (94/58)。2、单因素、多因素分析确定HE4 (p Objective: To investigate the predictive value of clinicopathological characteristics and inflammatory biomarkers in surgical outcomes of cytoreductive surgery (CRS) for epithelial ovarian cancer (EOC) and develop a nomogram model to guide individualized treatment. Methods: A total of 152 EOC patients who underwent primary CRS at Henan Provincial People’s Hospital (January 2018~December 2023) were retrospectively enrolled. Independent risk factors for R0 resection were identified using LASSO regression combined with univariate and multivariate logistic regression. A nomogram prediction model was constructed and evaluated via ROC curves, calibration curves, and decision curve analysis (DCA). Results: 1. The R0 resection rate was 61.8% (94/58). 2. Univariate and multivariate analyses identified HE4 (p < 0.001), neutrophil-to-lymphocyte ratio (NLR, p = 0.013), ascites volume (p = 0.007), FIGO stage (p < 0.001), and neoadjuvant chemotherapy (NACT, p = 0.005) as independent risk factors for R0 resection. The nomogram model based on these factors achieved an AUC of 0.891 (95% CI: 0.839~0.963), significantly outperforming the Fagotti score (AUC = 0.805, p = 0.045). Calibration and decision curves confirmed its high accuracy and clinical utility. Conclusion: The nomogram model integrating HE4, NLR, ascites volume, FIGO stage, and NACT effectively predicts the likelihood of R0 resection, providing a reliable tool for preoperative assessment.
Source: Advances in Clinical Medicine
Author: 柯静 李
Language: English
Subfield: Reproductive Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4410523456