Ovarian cancer risk prediction: a clinical epidemiology perspective

• Wentzensen, Nicolas ^[3] ; Ring, Kari ^[1] ; Erickson, Britt K ^[2] ; Reid, Brett ^[4] ; O’Donnell, Thomas ^[3] ; Check, David ^[3] ; Tworoger, Shelley S ^[4] ; Choudhury, Parichoy Pal ^[5]
  1. [1] University of Virginia

     University of Virginia

     Estados Unidos

     
  2. [2] University of Minnesota

     University of Minnesota

     City of Minneapolis, Estados Unidos

     
  3. [3] Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health , Rockville, MD ,
  4. [4] Department of Cancer Epidemiology, Moffitt Cancer Center , Tampa, FL ,
  5. [5] American Cancer Society, Department of Population Science , Atlanta, GA ,

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• Localización: American journal of epidemiology, ISSN-e 1476-6256, ISSN 0002-9262, Vol. 194, Nº. 5, 2025, págs. 1182-1191
• Idioma: inglés
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  • Abstract Ovarian cancer is a rare and highly heterogeneous disease usually detected at late stages when outcomes are poor. Population-based screening approaches have not been successful at reducing ovarian cancer mortality, but preventive bilateral salpingo-oophorectomy is highly effective at preventing ovarian cancer in high-risk populations. Ovarian cancer risk prediction models may allow identification of populations at increased risk of ovarian cancer for preventive interventions or targeted early detection. We propose a life-course approach to ovarian cancer risk prediction based on the time at which a risk model should be applied and the risk factors that are available. The discriminative ability of ovarian cancer risk prediction models published so far is limited, with areas under the curve ranging from 0.58 to 0.65 for different combinations of risk factors and genetic susceptibility markers. Currently proposed absolute risk thresholds for preventive surgery are around 4% lifetime risk. The absolute risk predicted by ovarian cancer risk models ranges from 0.6% to 2.5% lifetime risk in the general population, highlighting the need to improve ovarian cancer risk prediction models and evaluating new preventive approaches that can be offered to individuals at lower risk.