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dc.contributor.authorGaba, F
dc.contributor.authorBlyuss, O
dc.contributor.authorLiu, X
dc.contributor.authorGoyal, S
dc.contributor.authorLahoti, N
dc.contributor.authorChandrasekaran, D
dc.contributor.authorKurzer, M
dc.contributor.authorKalsi, J
dc.contributor.authorSanderson, S
dc.contributor.authorLanceley, A
dc.contributor.authorAhmed, M
dc.contributor.authorSide, L
dc.contributor.authorGentry-Maharaj, A
dc.contributor.authorWallis, Y
dc.contributor.authorWallace, A
dc.contributor.authorWaller, J
dc.contributor.authorLuccarini, C
dc.contributor.authorYang, X
dc.contributor.authorDennis, J
dc.contributor.authorDunning, A
dc.contributor.authorLee, A
dc.contributor.authorAntoniou, AC
dc.contributor.authorLegood, R
dc.contributor.authorMenon, U
dc.contributor.authorJacobs, I
dc.contributor.authorManchanda, R
dc.identifier.citationGaba, F.; Blyuss, O.; Liu, X.; Goyal, S.; Lahoti, N.; Chandrasekaran, D.; Kurzer, M.; Kalsi, J.; Sanderson, S.; Lanceley, A.; Ahmed, M.; Side, L.; Gentry-Maharaj, A.; Wallis, Y.; Wallace, A.; Waller, J.; Luccarini, C.; Yang, X.; Dennis, J.; Dunning, A.; Lee, A.; Antoniou, A.C.; Legood, R.; Menon, U.; Jacobs, I.; Manchanda, R. Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention. Cancers 2020, 12, 1241.en_US
dc.description.abstractUnselected population-based personalised ovarian cancer (OC) risk assessment combining genetic/epidemiology/hormonal data has not previously been undertaken. We aimed to perform a feasibility study of OC risk stratification of general population women using a personalised OC risk tool followed by risk management. Volunteers were recruited through London primary care networks. Inclusion criteria: women ≥18 years. Exclusion criteria: prior ovarian/tubal/peritoneal cancer, previous genetic testing for OC genes. Participants accessed an online/web-based decision aid along with optional telephone helpline use. Consenting individuals completed risk assessment and underwent genetic testing (BRCA1/BRCA2/RAD51C/RAD51D/BRIP1, OC susceptibility single-nucleotide polymorphisms). A validated OC risk prediction algorithm provided a personalised OC risk estimate using genetic/lifestyle/hormonal OC risk factors. Population genetic testing (PGT)/OC risk stratification uptake/acceptability, satisfaction, decision aid/telephone helpline use, psychological health and quality of life were assessed using validated/customised questionnaires over six months. Linear-mixed models/contrast tests analysed impact on study outcomes. Main outcomes: feasibility/acceptability, uptake, decision aid/telephone helpline use, satisfaction/regret, and impact on psychological health/quality of life. In total, 123 volunteers (mean age = 48.5 (SD = 15.4) years) used the decision aid, 105 (85%) consented. None fulfilled NHS genetic testing clinical criteria. OC risk stratification revealed 1/103 at ≥10% (high), 0/103 at ≥5%–<10% (intermediate), and 100/103 at <5% (low) lifetime OC risk. Decision aid satisfaction was 92.2%. The telephone helpline use rate was 13% and the questionnaire response rate at six months was 75%. Contrast tests indicated that overall depression (p = 0.30), anxiety (p = 0.10), quality-of-life (p = 0.99), and distress (p = 0.25) levels did not jointly change, while OC worry (p = 0.021) and general cancer risk perception (p = 0.015) decreased over six months. In total, 85.5–98.7% were satisfied with their decision. Findings suggest population-based personalised OC risk stratification is feasible and acceptable, has high satisfaction, reduces cancer worry/risk perception, and does not negatively impact psychological health/quality of life.en_US
dc.format.extent1241 - 1241
dc.publisherMDPI AGen_US
dc.rightsCreative Commons Attribution (CC BY) license
dc.titlePopulation Study of Ovarian Cancer Risk Prediction for Targeted Screening and Preventionen_US
dc.rights.holder© 2020 by the authors.
pubs.notesNot knownen_US
pubs.publication-statusPublished onlineen_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US

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