Wu Guilan, Chen Jiana, Niu Peiguang, Huang Xinhai, Chen Yunda, Zhang Jinhua
Department of Pharmacy, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350001, China.
The Affiliated High School of Fujian Normal University in PingTan, Fuzhou, 350400, China.
Heliyon. 2024 Aug 19;10(16):e36549. doi: 10.1016/j.heliyon.2024.e36549. eCollection 2024 Aug 30.
Stage IV ovarian cancer is a tumor with a poor prognosis and lacks prognostic models. This study constructed and validated a model to predict overall survival (OS) in patients with newly diagnosed stage IV ovarian cancer.
The data of this study were extracted from SEER database. Cox regression analysis was used to construct the nomogram model and implemented it in an online web application. Concordance index (C-index), calibration curve, area under receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to verify the performance of the model.
A total of 6062 patients were collected in this study. The analysis showed that age, race, histological grade, histological differentiation, T stage, CA125, liver metastasis, primary site surgery, and chemotherapy were independent prognostic parameters, and were used to construct the nomogram model. The C-index of the training group and the verification group was 0.704 and 0.711, respectively. Based on the score of the nomogram responding risk classification system is constructed. The online interface of Alfalfa-IVOC-OS is free to use. In addition, the racial analysis found that Asian or Pacific Islander people had higher survival rates than white and black people.
This study established a new survival prediction model and risk classification system designed to predict OS time in patients with stage IV ovarian cancer to help clinicians evaluate the prognosis of patients with stage IV ovarian cancer.
IV期卵巢癌预后较差且缺乏预后模型。本研究构建并验证了一个用于预测新诊断IV期卵巢癌患者总生存期（OS）的模型。
本研究数据取自监测、流行病学与最终结果（SEER）数据库。采用Cox回归分析构建列线图模型，并在在线网络应用程序中实现。使用一致性指数（C指数）、校准曲线、受试者操作特征曲线（ROC）下面积和决策曲线分析（DCA）来验证模型性能。
本研究共纳入6062例患者。分析显示年龄、种族、组织学分级、组织学分化、T分期、CA125、肝转移、原发部位手术和化疗是独立的预后参数，并用于构建列线图模型。训练组和验证组的C指数分别为0.704和0.711。基于列线图得分构建反应风险分类系统。苜蓿-IV期卵巢癌-总生存期（Alfalfa-IVOC-OS）的在线界面可免费使用。此外，种族分析发现亚洲或太平洋岛民的生存率高于白人和黑人。
本研究建立了一种新的生存预测模型和风险分类系统，旨在预测IV期卵巢癌患者的总生存期，以帮助临床医生评估IV期卵巢癌患者的预后。
