Models for predicting risk of ovarian cancer
Anastasia Ardasheva, Karyna Atha, Ikraan Mohamed, Bea Forder, Hannah Nentwich, Kezia Gaitskell, Christiana Kartsonaki
Background: Ovarian cancer is the deadliest gynecological 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.
Methods: We conducted the first comprehensive overview of ovarian cancer risk prediction models and assessed their performance, quality, and applicability. A systematic search was done in MEDLINE and Embase. Articles on ovarian cancer risk prediction models and validation studies were included. Model characteristics and measures of predictive ability were extracted. The Prediction model Risk of Bias Assessment Tool (PROBAST) was used to assess model quality.
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 models may be suitable for use in primary care or specialty clinics; however, assessment of their utility and validation in diverse cohorts is warranted.