ArticleHeliyon2024
Stage IV ovarian cancer prognosis nomogram and analysis of racial differences: A study based on the SEER database.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Unravelling Multilayered RNA Modification Networks in Female Reproduction and Obstetric/Gynaecologic Disorders.Biomolecules · 2026Review
- Impact of single-site distant metastases on prognosis in advanced ovarian cancer: a Surveillance, Epidemiology, and End Results population-based study.Translational cancer research · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Stage IV ovarian cancer is a tumor with a poor prognosis and lacks prognostic models. This study constructed and validated a model to predict overall survival (OS) in patients with newly diagnosed stage IV ovarian cancer. Methods: The data of this study were extracted from SEER database. Cox regression analysis was used to construct the nomogram model and implemented it in an online web application. Concordance index (C-index), calibration curve, area under receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to verify the performance of the model. Results: A total of 6062 patients were collected in this study. The analysis showed that age, race, histological grade, histological differentiation, T stage, CA125, liver metastasis, primary site surgery, and chemotherapy were independent prognostic parameters, and were used to construct the nomogram model. The C-index of the training group and the verification group was 0.704 and 0.711, respectively. Based on the score of the nomogram responding risk classification system is constructed. The online interface of Alfalfa-IVOC-OS is free to use. In addition, the racial analysis found that Asian or Pacific Islander people had higher survival rates than white and black people. Conclusion: This study established a new survival prediction model and risk classification system designed to predict OS time in patients with stage IV ovarian cancer to help clinicians evaluate the prognosis of patients with stage IV ovarian cancer.
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