Ye Wanlu, Wang Qing, Lu Yanming
Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, 110003, Liaoning, China.
J Cancer Res Clin Oncol. 2023 Nov;149(15):13607-13618. doi: 10.1007/s00432-023-05172-5. Epub 2023 Jul 29.
Ovarian endometrioid carcinoma (OEC) is the second most commonly occurring ovarian epithelial malignancy, but the associated prognostic factors remain obscure. This study aimed to analyze independent prognostic factors for patients with OEC and to develop and validate a nomogram to predict the overall survival (OS) of these patients.
Clinical information of patients with OEC (2000-2019) was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were used to identify independent prognostic factors, and nomogram models were constructed using independent prognostic factors. Receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA) were used to verify the accuracy and validity of the nomogram. Kaplan-Meier curves were used to compare the differences in OS and cancer-specific survival (CSS) among subgroups.
A total of 4628 patients with OEC were included, being divided into training (n = 3238) and validation (n = 1390) sets (7:3 ratio). On multivariate Cox analysis, AJCC stage, age, tumor size, differentiation, chemotherapy, and lymph node resection were significant predictors of survival outcomes (P < 0.05). Resection of 1-3 lymph nodes in early-stage OEC patients did not significantly prolong OS (P > 0.05), but resection of ≥ 4 lymph nodes in early-stage improved OS and CSS (P < 0.05). The OS of early-stage patients was not related to whether or not they received chemotherapy (P > 0.05). Lymph node resection and chemotherapy significantly improved the prognosis of patients with advanced OEC (P < 0.05). The c-index of nomogram prediction model was 0.782. ROC with good discrimination, calibration plots with high consistency, and DCA with large net benefit rate result in large clinical value.
AJCC stage, differentiation, tumor size, age, chemotherapy, and lymph node dissection were prognostic factors of OEC. The constructed nomogram prediction model can effectively predict the prognosis of OEC patients and improve the accuracy of clinical decision-making.
卵巢子宫内膜样癌（OEC）是第二常见的卵巢上皮性恶性肿瘤，但其相关的预后因素仍不明确。本研究旨在分析OEC患者的独立预后因素，并开发和验证一种列线图以预测这些患者的总生存期（OS）。
从监测、流行病学和最终结果（SEER）数据库中获取2000 - 2019年OEC患者的临床信息。采用单因素和多因素Cox分析来识别独立预后因素，并使用独立预后因素构建列线图模型。采用受试者工作特征（ROC）曲线、校准图和决策曲线分析（DCA）来验证列线图的准确性和有效性。使用Kaplan - Meier曲线比较各亚组之间OS和癌症特异性生存期（CSS）的差异。
共纳入4628例OEC患者，分为训练集（n = 3238）和验证集（n = 1390）（比例为7:3）。多因素Cox分析显示，美国癌症联合委员会（AJCC）分期、年龄、肿瘤大小、分化程度、化疗和淋巴结切除是生存结局的显著预测因素（P < 0.05）。早期OEC患者切除1 - 3个淋巴结并未显著延长OS（P > 0.05），但早期切除≥4个淋巴结可改善OS和CSS（P < 0.05）。早期患者的OS与是否接受化疗无关（P > 0.05）。淋巴结切除和化疗显著改善了晚期OEC患者的预后（P < 0.05）。列线图预测模型的c指数为0.782。具有良好区分度的ROC曲线、高度一致性的校准图以及具有较大净获益率的DCA结果具有较大的临床价值。
AJCC分期、分化程度、肿瘤大小、年龄化疗和淋巴结清扫是OEC的预后因素。构建的列线图预测模型能够有效预测OEC患者的预后，提高临床决策的准确性。
