ArticleJournal of ovarian research2015
Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics.
Article in Journal of ovarian research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- BRCA1 Expression by Immunohistochemistry and Prognosis in Ovarian Cancer: A Systematic Review and Meta-Analysis.Targeted oncology · 2020Pooled it
- Hypermethylation of tumor suppressor genes is a risk factor for poor prognosis in ovarian cancer: A meta-analysis.Medicine · 2019Pooled it
- The prognostic value of HER2 in ovarian cancer: A meta-analysis of observational studies.PloS one · 2018Pooled it
- Diagnostic value of a multimodal approach combining inflammatory biomarkers, tumor markers, and ultrasonographic features for borderline ovarian epithelial tumors.Frontiers in oncology · 2026Article
- Review
- Germline MLH1, MSH2 and MSH6 variants in Brazilian patients with colorectal cancer and clinical features suggestive of Lynch Syndrome.Cancer medicine · 2018Article
- EGFR as a prognostic biomarker and therapeutic target in ovarian cancer: evaluation of patient cohort and literature review.Genes & cancer · 2017Article
- HBXIP overexpression is correlated with the clinical features and survival outcome of ovarian cancer.Journal of ovarian research · 2017Article
- Characterization of ovarian clear cell carcinoma using target drug-based molecular biomarkers: implications for personalized cancer therapy.Journal of ovarian research · 2017Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA high-quality risk prediction model is urgently needed for the clinical management of ovarian cancer. However most existing models are solely based on clinical parameters, and molecular classifications in recent reports are still being debated. This study aimed to establish a risk prediction model by using both clinicopathological and molecular factors (the synthetic model) for epithelial ovarian cancer.
methodsA retrospective cohort study was conducted in epithelial ovarian cancer patients (n = 161) treated with primary debulking surgery and adjuvant chemotherapy. The expression level of 15 selected molecular markers were measured using immunohistochemistry. A risk model was developed using COX regression analysis with overall survival as the primary outcome. A simplified scoring system for each prognostic factor was based on its coefficient. Independent validation (n = 40) was conducted to evaluate the performance of the model.
resultsA total of 10 out of 15 molecular markers were significantly associated with clinical characteristics and overall survival. The synthetic model performed better than the clinicopathological risk model or the molecular risk model alone, as assessed by analysis of the receiver-operating characteristics curve area and the Youden index. The synthetic model included parity (>3), peritoneal metastasis, stage, tumor type, residual disease, and expression of human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), breast cancer 1 (BRCA1), murine sarcoma viral oncogene homolog B (BRAF) and Kirsten rat sarcoma viral oncogene homolog (KRAS).
conclusionsOur synthetic risk model may more accurately predict survival of epithelial ovarian cancer patients than current models.
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