ArticlePeerJ2026
Construction of nomograms for predicting overall survival and progression-free survival in patients with high-grade serous ovarian carcinoma: a retrospective study.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Background: The aim of this study was to identify independent prognostic factors and develop predictive nomograms for overall survival (OS) and progression-free survival (PFS) in patients with high-grade serous ovarian carcinoma (HGSOC). Methods: Information on patients primarily diagnosed with HGSOC at the Affiliated Hospital of Qingdao University from June 2008 to June 2018 was extracted. Kaplan-Meier (K-M) analyses were used to generate survival curves. We employed univariate and multivariate Cox regression analyses to determine independent prognostic factors, and prognostic nomograms for OS and PFS were developed. Results: A total of 573 patients were included in the final study. The age at diagnosis, first-visit interval, peripheral blood neutrophil-to-lymphocyte ratio, the immunohistochemical expressions of estrogen receptor and progesterone receptor, and Federation of Gynecology and Obstetrics (FIGO) stage were independent prognostic factors associated with OS and PFS. Additionally, the immunohistochemical expression of Wilms' tumor-1 (WT-1) and neoadjuvant chemotherapy were also related to the OS, whereas the serum carbohydrate antigen 125 (CA125) level, the immunohistochemical expression of CK7, omentum metastasis, and postoperative adjuvant chemotherapy were independent prognostic factors linked to PFS. The area under the time-dependent receiver operating characteristic curve values of the nomograms were higher than those of the FIGO staging system. The calibration curves and decision curve analysis demonstrated the clinical applicability of the nomograms. Conclusion: We developed two new risk stratifications based on the total points of the nomograms. This study could provide a foundation for the development of more accurate predictive models that can assist clinicians in creating individualized treatment plans and improving the prognosis of HGSOC.
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