预测卵巢癌患者肝转移风险和预后的列线图。
Nomograms for predicting risk and prognosis of liver metastases in ovarian cancer patients.
作者信息
Jiang Feng, Yao Chunfang
机构信息
Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, 215000, Jiangsu, PR China.
Department of Pathology, Children's Hospital of Soochow University, No. 92 Zhongnan Street, Suzhou, 215000, Jiangsu, PR China.
出版信息
J Gynecol Obstet Hum Reprod. 2025 Apr;54(4):102918. doi: 10.1016/j.jogoh.2025.102918. Epub 2025 Jan 30.
AIMS
Liver metastases (LiM) commonly manifest in ovarian cancer (OC) patients. We intended to establish nomograms for predicting the risk and prognostic factors in OCLiM patients.
METHODS
Data from the SEER database (Nov 2022, Sub 1992-2020) were analyzed, excluding patients with missing data on liver metastases, survival months, race, AJCC T stage, marital status, rural/urban status, and metastases to bone, brain, or lung. Logistic and Cox regression analyses identified risk and prognostic factors for liver metastases. Predictive nomograms were developed from the multivariable regression results. The nomograms were evaluated using Harrell's C-index, ROC curve, calibration curve, DCA, NRI, and IDI. Moreover, the efficacy of the treatment in the new risk stratification subgroups was demonstrated by Kaplan-Meier (KM) curves.
RESULTS
Among 17,056 OC patients, 5.67% (n = 967) had liver metastases. Nomograms were constructed based on identified risk and prognostic factors, with dynamic web-based nomograms developed for clinical use. The nomogram demonstrated C-index values of 81.9% (training) and 82.9% (validation) for predicting liver metastases. For OS and CSS, the C-index values were 73.3% and 73.7% (training), and 73.3% and 72.8% (validation), respectively. The ROC curves for OS at 1-, 3-, 5-year showed AUC values of 84.1%, 79.8%, 75.9% (training) and 82.9%, 78.5%, 82.2% (validation), respectively. For CSS, AUC values at 1-, 3-, and 5-year were 84.5%, 80.2%, 76.1% (training) and 82.6%, 78.0, 82.0% (validation), respectively. The calibration and DCA curves confirmed favorable performance. NRI and IDI analyses showed superiority over the Grade and AJCC stage systems. Surgery improved prognosis in the low-risk group, while chemotherapy was more effective in both low- and medium-risk groups.
CONCLUSIONS
we developed nomograms and risk stratification systems to assist clinicians in the individualized prediction, risk stratification, and prognostic assessment of OCLiM patients.
目的
肝转移（LiM）在卵巢癌（OC）患者中较为常见。我们旨在建立列线图以预测OC-LiM患者的风险和预后因素。
方法
分析了SEER数据库（2022年11月，子集1992 - 2020年）中的数据，排除了肝转移、生存月数、种族、AJCC T分期、婚姻状况、城乡状况以及骨、脑或肺转移数据缺失的患者。逻辑回归和Cox回归分析确定了肝转移的风险和预后因素。根据多变量回归结果绘制预测列线图。使用Harrell's C指数、ROC曲线、校准曲线、决策曲线分析（DCA）、净重新分类指数（NRI）和综合鉴别改善指数（IDI）对列线图进行评估。此外，通过Kaplan-Meier（KM）曲线展示了新风险分层亚组中治疗的疗效。
结果
在17056例OC患者中，5.67%（n = 967）有肝转移。根据确定的风险和预后因素构建了列线图，并开发了基于网络的动态列线图供临床使用。该列线图预测肝转移的C指数值在训练集为81.9%，验证集为82.9%。对于总生存期（OS）和癌症特异性生存期（CSS），C指数值在训练集分别为73.3%和73.7%，验证集分别为73.3%和72.8%。OS在1年、3年、5年的ROC曲线AUC值在训练集分别为84.1%、79.8%、75.9%，验证集分别为82.9%、78.5%、82.2%。对于CSS，1年、3年和5年的AUC值在训练集分别为84.5%、80.2%、76.1%，验证集分别为82.6%、78.0%、82.0%。校准曲线和DCA曲线证实了良好的性能。NRI和IDI分析显示优于分级和AJCC分期系统。手术改善了低风险组的预后，而化疗在低风险和中风险组中更有效。
结论
我们开发了列线图和风险分层系统，以协助临床医生对OC-LiM患者进行个体化预测、风险分层和预后评估。
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