Yao Shen, Fei Xi, Pingge Zhao, Yuhang Zhang, Guanlin Guo, Xueyuan Jia, Jie Wu, Ye Kuang
{"title":"卵巢透明细胞癌预后图的开发和验证：基于SEER数据库和中国队列的研究。","authors":"Yao Shen, Fei Xi, Pingge Zhao, Yuhang Zhang, Guanlin Guo, Xueyuan Jia, Jie Wu, Ye Kuang","doi":"10.1007/s12672-025-02272-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The clinical prognostic factors for ovarian clear cell carcinoma (OCCC) are limited, and we aim to construct a model to predict the survival of OCCC patients.</p><p><strong>Methods: </strong>Data were extracted from the SEER database for patients diagnosed with OCCC. Cox regression analyses were used to identify independent risk factors for OCCC. Two nomograms were developed, and the results were evaluated comprehensively by C-index, ROC curve, calibration curve, and DCA curve. Patients diagnosed with OCCC were used as the validation set to verify the model.</p><p><strong>Results: </strong>A total of 1855 OCCC patients from the SEER database were used as the training set, and 101 patients from our hospital were used as the validation set. Cox regression analysis of the independent risk factors affecting the prognosis of OCCC was used to construct nomograms. The C-index of the training set OS was 0.76, and the validation set OS was 0.75. The AUC of the training set OS is 0.803, 0.794, and 0.802 for 1, 3, and 5 years, and 0.774, 0.800, and 0.923 for the validation set. The calibration curve and DCA curve also showed that OS and CSS have good predictive power.</p><p><strong>Conclusions: </strong>A nomogram based on 8 prognostic factors analyzed by Cox regression can predict the prognosis of OCCC patients effectively.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"482"},"PeriodicalIF":2.8000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a prognostic nomogram for ovarian clear cell carcinoma: a study based on the SEER database and a Chinese cohort.\",\"authors\":\"Yao Shen, Fei Xi, Pingge Zhao, Yuhang Zhang, Guanlin Guo, Xueyuan Jia, Jie Wu, Ye Kuang\",\"doi\":\"10.1007/s12672-025-02272-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The clinical prognostic factors for ovarian clear cell carcinoma (OCCC) are limited, and we aim to construct a model to predict the survival of OCCC patients.</p><p><strong>Methods: </strong>Data were extracted from the SEER database for patients diagnosed with OCCC. Cox regression analyses were used to identify independent risk factors for OCCC. Two nomograms were developed, and the results were evaluated comprehensively by C-index, ROC curve, calibration curve, and DCA curve. Patients diagnosed with OCCC were used as the validation set to verify the model.</p><p><strong>Results: </strong>A total of 1855 OCCC patients from the SEER database were used as the training set, and 101 patients from our hospital were used as the validation set. Cox regression analysis of the independent risk factors affecting the prognosis of OCCC was used to construct nomograms. The C-index of the training set OS was 0.76, and the validation set OS was 0.75. The AUC of the training set OS is 0.803, 0.794, and 0.802 for 1, 3, and 5 years, and 0.774, 0.800, and 0.923 for the validation set. The calibration curve and DCA curve also showed that OS and CSS have good predictive power.</p><p><strong>Conclusions: </strong>A nomogram based on 8 prognostic factors analyzed by Cox regression can predict the prognosis of OCCC patients effectively.</p>\",\"PeriodicalId\":11148,\"journal\":{\"name\":\"Discover. Oncology\",\"volume\":\"16 1\",\"pages\":\"482\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Discover. Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12672-025-02272-1\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discover. Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12672-025-02272-1","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Development and validation of a prognostic nomogram for ovarian clear cell carcinoma: a study based on the SEER database and a Chinese cohort.
Background: The clinical prognostic factors for ovarian clear cell carcinoma (OCCC) are limited, and we aim to construct a model to predict the survival of OCCC patients.
Methods: Data were extracted from the SEER database for patients diagnosed with OCCC. Cox regression analyses were used to identify independent risk factors for OCCC. Two nomograms were developed, and the results were evaluated comprehensively by C-index, ROC curve, calibration curve, and DCA curve. Patients diagnosed with OCCC were used as the validation set to verify the model.
Results: A total of 1855 OCCC patients from the SEER database were used as the training set, and 101 patients from our hospital were used as the validation set. Cox regression analysis of the independent risk factors affecting the prognosis of OCCC was used to construct nomograms. The C-index of the training set OS was 0.76, and the validation set OS was 0.75. The AUC of the training set OS is 0.803, 0.794, and 0.802 for 1, 3, and 5 years, and 0.774, 0.800, and 0.923 for the validation set. The calibration curve and DCA curve also showed that OS and CSS have good predictive power.
Conclusions: A nomogram based on 8 prognostic factors analyzed by Cox regression can predict the prognosis of OCCC patients effectively.
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