Wuxi Medical School, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214000, P.R. China; Department of Obstetrics and Gynecology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, Jiangsu 214000, P.R. China.
Department of Obstetrics and Gynecology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, Jiangsu 214000, P.R. China.
J Gynecol Obstet Hum Reprod. 2022 Sep;51(7):102424. doi: 10.1016/j.jogoh.2022.102424. Epub 2022 Jun 11.
To develop predictive nomograms of overall survival (OS) and cancer-specific survival (CSS) in patients with primary mucinous ovarian cancer (PMOC).
Patients diagnosed with PMOC from 2010 to 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided into a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were conducted to identify the independent risk factors. Nomograms were constructed and then verified by calibration plots, the concordance index (C-index), and the area under the receiver operating characteristic curve (AUC).
A total of 991 patients with PMOC were enrolled and randomly divided into a training cohort (n=695) and a validation cohort (n=296) at a ratio of 7:3. Multivariate Cox regression analyses demonstrated that independent risk factors for OS included age, laterality, and American Joint Committee on Cancer (AJCC) stage. Independent risk factors for CSS included age, laterality, grade, and AJCC stage. Predictive nomograms for OS and CSS were developed with respective independent risk variables. In the training cohort, the C-index of the CSS and OS nomograms were 0.88 [95% confidence interval (CI): 0.84-0.92] and 0.85 (95% CI: 0.81-0.89), respectively. In the validation cohort, the C-index of the predictive CSS and OS nomograms were 0.86 (95% CI: 0.80-0.92) and 0.80 (95% CI: 0.74-0.87), respectively. The AUCs were higher in both cohorts. Furthermore, the calibration curves in both cohorts showed good consistency between the predicted results and the actual results.
The nomograms demonstrated good predictability for the survival of patients with PMOC, and could serve as an applicable tool to help clinicians improve treatment plans.
旨在建立预测原发性卵巢黏液性癌（PMOC）患者总生存（OS）和癌症特异性生存（CSS）的列线图。
从监测、流行病学和最终结果（SEER）数据库中获取 2010 年至 2015 年间诊断为 PMOC 的患者，并将其随机分为训练队列和验证队列。采用单因素和多因素 Cox 回归分析确定独立的危险因素。构建列线图，并通过校准图、一致性指数（C 指数）和接受者操作特征曲线下面积（AUC）进行验证。
共纳入 991 例 PMOC 患者，按 7：3 的比例随机分为训练队列（n=695）和验证队列（n=296）。多因素 Cox 回归分析显示，OS 的独立危险因素包括年龄、侧别和美国癌症联合委员会（AJCC）分期。CSS 的独立危险因素包括年龄、侧别、分级和 AJCC 分期。使用独立的风险变量建立了用于预测 OS 和 CSS 的列线图。在训练队列中，CSS 和 OS 列线图的 C 指数分别为 0.88（95%可信区间：0.84-0.92）和 0.85（95%可信区间：0.81-0.89）。在验证队列中，预测 CSS 和 OS 列线图的 C 指数分别为 0.86（95%可信区间：0.80-0.92）和 0.80（95%可信区间：0.74-0.87）。两个队列的 AUC 均较高。此外，两个队列的校准曲线均显示预测结果与实际结果之间具有良好的一致性。
该列线图对 PMOC 患者的生存具有良好的预测能力，可作为帮助临床医生制定治疗计划的一种实用工具。
