Development and external validation of nomograms for predicting individual survival in patients with ovarian clear cell carcinoma

Author:

Liu Xiaoshi123ORCID,Lu Huaiwu4,Zhou Ying5ORCID,Long Xiaoran12,Li Qing12,Zhuang Guanglei6,Yin Xia12,Di Wen12

Affiliation:

1. Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China

2. Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China

3. Department of Gynecological Oncology, the Affiliated Cancer Hospital, School of Medicine University of Electronic Science and Technology of China, Sichuan Cancer Hospital & Institute Chengdu China

4. Department of Gynecologic Oncology, Sun Yat‐Sen Memorial Hospital Sun Yat‐Sen University Guangzhou China

5. Department of Obstetrics and Gynecology, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC University of Science and Technology of China Hefei China

6. State Key Laboratory of Oncogene and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China

Abstract

AbstractPurposeOvarian 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.MethodsIn 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.ResultsAdvanced 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.ConclusionsWe 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.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Shanghai Hospital Development Center

Publisher

Wiley

Subject

Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology

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