Overview of models for predicting risk of developing ovarian cancer: a systematic review
Abstract
Background: Ovarian cancer is the deadliest gynaecological malignancy, and most cases are diagnosed at an advanced stage. There is no population-wide screening; models for predicting risk of ovarian cancer could be useful in decreasing mortality. We aimed to provide the first comprehensive overview of models for predicting the risk of developing ovarian cancer and their performance, risk of bias, and applicability.
Methods: We performed a systematic search through MEDLINE and Embase to identify articles in which models for predicting risk of developing ovarian cancer were developed and/or validated. Model characteristics and measures of predictive ability were extracted and summarised for each model. The Prediction model Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability.
Results: We identified twenty-eight studies representing twenty-one ovarian cancer risk prediction models. Fifteen risk prediction models were developed or validated in the general population, while six were developed for high-risk populations, defined by family history, and one for patients with endometriosis. Twenty-three studies that reported discrimination and/or calibration had moderate-to-good performance. Risk prediction models with the best predictive ability reported an area under the receiver-operating characteristic curve (AUC) of 0.86 (95% confidence interval (CI) 0.84–0.87) for 2-year and an AUC of 0.77 (95% CI 0.76-0.78) for 10-year ovarian cancer risk in the general population.
Conclusions: Several models for predicting short- and long-term risk of ovarian cancer have been published, of which a few have been validated in other populations. Some have limited predictive performance. Some models may be suitable for use in primary care or specialty clinics; however, assessment of their utility and validation in diverse cohorts is warranted.
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BibTeX
@article{ardasheva2026overview,
title = {{Overview of models for predicting risk of developing ovarian cancer: a systematic review}},
author = {Anastasia Ardasheva and Karyna Atha and Ikraan Mohamed and Hannah Nentwich and Bea Harris Forder and Kezia Gaitskell and Christiana Kartsonaki},
journal = {Communications Medicine},
year = {2026},
month = nov,
doi = {10.1038/s43856-026-01905-y},
url = {https://www.nature.com/articles/s43856-026-01905-y},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Overview of models for predicting risk of developing ovarian cancer: a systematic review
AU - Anastasia Ardasheva
AU - Karyna Atha
AU - Ikraan Mohamed
AU - Hannah Nentwich
AU - Bea Harris Forder
AU - Kezia Gaitskell
AU - Christiana Kartsonaki
PY - 2026
DA - 2026/11/05
JO - Communications Medicine
DO - 10.1038/s43856-026-01905-y
UR - https://www.nature.com/articles/s43856-026-01905-y
AB - Background: Ovarian cancer is the deadliest gynaecological malignancy, and most cases are diagnosed at an advanced stage. There is no population-wide screening; models for predicting risk of ovarian cancer could be useful in decreasing mortality. We aimed to provide the first comprehensive overview of models for predicting the risk of developing ovarian cancer and their performance, risk of bias, and applicability. Methods: We performed a systematic search through MEDLINE and Embase to identify articles in which models for predicting risk of developing ovarian cancer were developed and/or validated. Model characteristics and measures of predictive ability were extracted and summarised for each model. The Prediction model Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability. Results: We identified twenty-eight studies representing twenty-one ovarian cancer risk prediction models. Fifteen risk prediction models were developed or validated in the general population, while six were developed for high-risk populations, defined by family history, and one for patients with endometriosis. Twenty-three studies that reported discrimination and/or calibration had moderate-to-good performance. Risk prediction models with the best predictive ability reported an area under the receiver-operating characteristic curve (AUC) of 0.86 (95% confidence interval (CI) 0.84–0.87) for 2-year and an AUC of 0.77 (95% CI 0.76-0.78) for 10-year ovarian cancer risk in the general population. Conclusions: Several models for predicting short- and long-term risk of ovarian cancer have been published, of which a few have been validated in other populations. Some have limited predictive performance. Some models may be suitable for use in primary care or specialty clinics; however, assessment of their utility and validation in diverse cohorts is warranted.
SN - 2730-664X
ER -