Epidemiology and nomogram for predicting the cancer-specific survival of ovarian granulosa cell tumor: A seer database study

被引:0
作者
Xia, Longjie [1 ,2 ]
Qiu, Shenghui [1 ,2 ]
Kong, Fan-Biao [1 ,5 ,6 ]
Lai, Jianqin [1 ,2 ]
Huang, Huixian [3 ]
Hu, Huiqiong [2 ]
Liu, Xiangxia [4 ]
Ye, Zi [7 ]
Cao, Jie [1 ,2 ,8 ]
机构
[1] Jinan Univ, Affiliated Hosp 1, Dept Gen Surg, 613 West Huangpu Ave, Guangzhou 510630, Peoples R China
[2] Guangzhou First Peoples Hosp, Dept Gen Surg, 1 Panfu Rd, Guangzhou 510180, Peoples R China
[3] Sun Yat sen Univ, Affiliated Hosp 1, Dept Plast Surg, 58 Zhongshan Rd 2, Guangzhou 510080, Peoples R China
[4] Univ Tennessee Hlth Sci Ctr, Dept Plast Surg, Memphis, TN 38138 USA
[5] Peoples Hosp Guangxi Zhuang Autonomous Reg, Dept Colorectal & Anal Surg, 6 Taoyuan Rd, Nanning 530021, Peoples R China
[6] Guangxi Acad Med Sci, Inst Minimally Invas Technol & Applicat, 6 Taoyuan Rd, Nanning 530021, Peoples R China
[7] Sun Yat sen Univ, Affiliated Hosp 1, Dept Emergency, 58 Zhongshan Rd 2, Guangzhou 510080, Peoples R China
[8] Jinan Univ, Affiliated Hosp 1, Guangzhou Peoples Hosp 1, Dept Genaral Surg, Guangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Nomogram; epidemiology; cancer-specific survival; ovarian granulosa cell tumor; TERM-FOLLOW-UP; RISK; MANAGEMENT;
D O I
10.1016/j.jogoh.2023.102601
中图分类号
R71 [妇产科学];
学科分类号
100211 ;
摘要
Objective: ovarian granulosa cell tumor (OGCT) is a kind of infrequent ovarian malignant tumor with limited epidemiological data available. we established a predictive nomograph to verify the clinical prognosis.Methods: 1005 diagnosed with ovarian granulosa cell tumor (OGCT) were extracted from Surveillance, Epide-miology, and End Results (SEER) public database from 2000-2018. Kaplan-Meier analysis was applied to dis-tinguish risk factors, univariate and multivariate Cox analyses were used to determine the independent prognostic factors for cancer-specific survival (CSS) of OGCT patients. The obtained prognostic variables were combined to construct a nomogram model for predicting CSS in OGCT patients.Results: Model performance was detected and evaluated with ROC curves and calibration plots. Data col-lected from 1005 patients were divided into two groups: training cohort(n=703,70%) and validation cohort (n=302,30%). The multivariate Cox model identified five covariates including age, marital status, AJCC stages, surgery and chemotherapy as independent interfering factors of CSS. The nomogram has shown a promising and excellent accuracy in evaluating 3-, 5-, 8-year CSS in OGCT patients. In terms of the CSS of the training cohort, the AUC values of the 3-, 5-, 8-year ROC curves were 0.819,0.8,0.819, while in terms of the CSS of the validation cohort, the AUC values of the validation cohort were 0.822,0.84,0.823, respectively. All the calibra-tion curves showed pleasant consistency between predicted and actual survival rates. The nomogram model established in the study can improve the veracity of prognosis prediction, thereby improving the accuracy of individualized survival risk assessment, and providing targeted and constructive recommendations for spe-cific treatment options. Conclusion: Age, advanced clinical stage, widower and without surgery therapy are independent risk factors for poor prognosis and the nomogram we constructed can help clinicians efficiently recognize high-risk OGCT patients to guide targeted therapies and improve their outcomes.(c) 2023 Elsevier Masson SAS. All rights reserved.
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页数:8
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