Xiao Huifu, Pan Ningping, Ruan Guohai, Hao Qiufen, Chen Jiaojiao
Department of Obstetrics and Gynaecology, Central Hospital of Haining, No. 758, Chang'an Road, Chang'an Town, Haining City, Jiaxing City, 314408, Zhejiang Province, China.
Department of Gynaecology, Women's Hospital School of Medicine Zhejiang University, Hangzhou, 310006, Zhejiang, China.
World J Surg Oncol. 2024 Dec 5;22(1):327. doi: 10.1186/s12957-024-03608-x.
To develop and validate a nomogram for predicting the overall survival (OS) of ovarian cancer patients with liver metastases (OCLM).
This study identified 821 patients in the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly divided in a ratio of 7:3 into a training cohort (n = 574) and a validation cohort (n = 247). Clinical factors associated with OS were assessed using univariate and multivariate Cox regression analyses, and backward stepwise regression was applied using the Akaike information criterion (AIC) to select the optimal predictor variables. The nomogram for predicting the OS of the OCLM patients was constructed based on the identified prognostic factors. Their prediction ability was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curves analysis (DCA) in both the training and validation cohorts.
We identified factors that predict OS for OCLM patients and constructed a nomogram based on the data. The ROC, C-index, and calibration analyses indicated that the nomogram performed well over the 1, 2, and 3-year OS in both the training and validation cohorts. Additionally, in contrast to the External model from multiple perspectives, our model shows higher stability and accuracy in predictive power. DCA curves, NRI, and IDI index demonstrated that the nomogram was clinically valuable and superior to the External model.
We established and validated a nomogram to predict 1,2- and 3-year OS of OCLM patients, and our results may also be helpful in clinical decision-making.
开发并验证一种用于预测卵巢癌肝转移（OCLM）患者总生存期（OS）的列线图。
本研究在监测、流行病学和最终结果（SEER）数据库中识别出821例患者。所有患者按7:3的比例随机分为训练队列（n = 574）和验证队列（n = 247）。使用单因素和多因素Cox回归分析评估与总生存期相关的临床因素，并采用赤池信息准则（AIC）进行向后逐步回归以选择最佳预测变量。基于识别出的预后因素构建预测OCLM患者总生存期的列线图。在训练队列和验证队列中，使用一致性指数（C指数）、受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估其预测能力。
我们识别出了预测OCLM患者总生存期的因素，并基于这些数据构建了列线图。ROC、C指数和校准分析表明，该列线图在训练队列和验证队列的1年、2年和3年总生存期预测方面表现良好。此外，与外部模型相比，我们的模型在预测能力上从多个角度显示出更高的稳定性和准确性。DCA曲线、净重新分类指数（NRI）和综合判别改善指数（IDI）表明，该列线图具有临床价值且优于外部模型。
我们建立并验证了一种用于预测OCLM患者1年、2年和3年总生存期的列线图，我们的结果可能也有助于临床决策。
