ArticleWomen's health reports (New Rochelle, N.Y.)2025
Predicting Survival Outcomes for Patients with Ovarian Cancer Using National Cancer Registry Data from Taiwan: A Retrospective Cohort Study.
Article in Women's health reports (New Rochelle, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Ovarian cancer is one of the top seven causes of cancer deaths. Incidence of ovarian cancer varies by ethnicity, where Asian women demonstrate lower incidence rates than non-Hispanic Blacks and Whites. Survival prediction models for ovarian cancer have been developed for Caucasians and Black populations using national databases; however, whether these models work for Asians is unclear. Therefore, a retrospective cohort study was conducted to develop survival prediction models for patients with epithelial ovarian cancer from a Taiwan Cancer Registry (TCR) who underwent de-bulking and chemotherapy, with the aim to identify variables that can predict prognosis accurately. Patients diagnosed with OC from TCR were included. Method: Two prognostic models (M1 and M2) were developed: M1 utilized clinical variables only, M2 additionally included cancer-specific variables with the aim to improve the accuracy. All methods were repeated independently for patients with only serous ovarian cancer. All findings for model M1 were validated among Black, White, and Asian populations from Surveillance, Epidemiology, and End Results (SEER) database and 10-fold internal cross-validations. Due to absence of cancer-specific site variables in SEER, model M2 was only internally validated. Cox-proportional hazards regression analysis was performed and a stepwise strategy with Akaike-information criterion was used to select appropriate variables as predictors to develop both M1 and M2. Results: The c-index values of both models were >0.7 in both TCR and SEER populations for epithelial ovarian cancer. Calibration analysis demonstrated good prediction performance with the proportional difference between predicted and observed survival to be <5%. The performance was similar for the subset of patients with serous epithelial ovarian cancer. Notably, no significant racial differences were observed. Conclusion: The prognostic models proposed in this study can potentially be used for identifying patients, especially from Taiwan, at higher risk of ovarian cancer mortality early on, leading to improved prognosis, through shared decision-making between physicians and patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.