基于上皮性卵巢癌患者临床特征手术结局预测模型构建
Development of a Predictive Model for Surgical Outcomes in Epithelial Ovarian Cancer Based on Clinical Characteristics
DOI: 10.12677/acm.2025.1551468, PDF,   
作者: 李柯静, 朱前勇*：新乡医学院河南省人民医院，河南 新乡；河南省人民医院妇科，河南 郑州；刘 宁, 吕晋谊：河南省人民医院妇科，河南 郑州
关键词: 上皮性卵巢癌；肿瘤细胞减灭术；列线图；炎症标志物；预后预测；Epithelial Ovarian Cancer； Cytoreductive Surgery； Nomogram； Inflammatory Biomarkers； Prognostic Prediction
摘要: 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 < 0.001)、NLR (p = 0.013)、腹水量(p = 0.007)、FIGO分期(p < 0.001)及新辅助化疗(p = 0.005)为R0切除的独立危险因素。基于上述因素构建的nomogram模型AUC为0.891 (95% CI: 0.839~0.963)，显著优于Fagotti评分(AUC = 0.805, p = 0.045)，校准曲线及决策曲线证实其高准确性与临床实用性。结论：联合HE4、NLR、腹水量、FIGO分期及新辅助化疗的nomogram模型可有效预测R0切除可能性，为术前评估提供可靠工具。
Abstract: 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.
文章引用：李柯静, 刘宁, 吕晋谊, 朱前勇. 基于上皮性卵巢癌患者临床特征手术结局预测模型构建[J]. 临床医学进展, 2025, 15(5): 1070-1080. https://doi.org/10.12677/acm.2025.1551468

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