Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Cancer Med. 2023 May;12(10):11385-11395. doi: 10.1002/cam4.5853. Epub 2023 Mar 27.
Ovarian clear cell carcinoma (OCCC) is a distinct and highly malignant subtype of ovarian cancer with high individual heterogeneity in survival that requires specific prognostic predictive tools. Thus, this study aimed to construct and validate nomograms for predicting individual survival in OCCC patients.
In total, 91 patients with OCCC who were diagnosed and treated at Renji Hospital between 2010 and 2020 were extracted as the training cohort, then 86 patients from the First Affiliated Hospital of USTC were used as the external validation cohort. Prognostic factors that affect survival were identified using least absolute shrinkage and selection operator regression. Nomograms of progression-free survival (PFS) and overall survival (OS) were then established with the Cox regression model and the performance was subsequently evaluated using the concordance index (C-index), calibration plots, decision curve analysis (DCA), and risk subgroup classification.
Advanced tumor, ascites of >400 mL, lymph node-positive, CA199 of >142.3 IU/mL, and fibrinogen of >5.36 g/L were identified as risk factors for OS while advanced tumor, ascites of >400 mL, lymph node-positive, and fibrinogen of >5.36 g/L were risk factors for PFS. The C-indexes for the OS and PFS nomograms were 0.899 and 0.731 in the training cohort and 0.804 and 0.787 in the validation cohort, respectively. The calibration plots showed that nomograms could provide better consistency in predicting patient survival than the FIGO staging system. DCA also demonstrated that nomograms were more clinically beneficial than the FIGO staging system. Additionally, patients could be classified into two risk groups based on scores using nomograms, with significant survival differences.
We developed nomograms that could more objectively and reliably predict the individual survival of patients with OCCC compared with the FIGO staging system. These tools might assist in clinical decision-making and management of patients with OCCC to improve their survival outcomes.
卵巢透明细胞癌（OCCC）是一种独特且高度恶性的卵巢癌亚型，其生存存在高度个体异质性，需要特定的预后预测工具。因此，本研究旨在构建和验证预测 OCCC 患者个体生存的列线图。
从 2010 年至 2020 年在仁济医院诊断和治疗的 91 名 OCCC 患者中提取作为训练队列，然后从中国科学技术大学第一附属医院提取 86 名患者作为外部验证队列。使用最小绝对收缩和选择算子回归识别影响生存的预后因素。然后使用 Cox 回归模型建立无进展生存（PFS）和总生存（OS）的列线图，并使用一致性指数（C-index）、校准图、决策曲线分析（DCA）和风险亚组分类评估性能。
晚期肿瘤、腹水>400ml、淋巴结阳性、CA199>142.3IU/ml 和纤维蛋白原>5.36g/L 被确定为 OS 的危险因素，而晚期肿瘤、腹水>400ml、淋巴结阳性和纤维蛋白原>5.36g/L 是 PFS 的危险因素。OS 和 PFS 列线图在训练队列中的 C 指数分别为 0.899 和 0.731，在验证队列中的 C 指数分别为 0.804 和 0.787。校准图表明，与 FIGO 分期系统相比，列线图可以更好地预测患者的生存一致性。DCA 还表明，列线图比 FIGO 分期系统更具临床益处。此外，还可以根据列线图的评分将患者分为两个风险组，生存差异显著。
与 FIGO 分期系统相比，我们开发的列线图可以更客观、更可靠地预测 OCCC 患者的个体生存。这些工具可能有助于临床决策和 OCCC 患者的管理，以提高他们的生存结果。
