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dc.contributor.authorGaba, Faiza
dc.contributor.authorBlyuss, Oleg
dc.contributor.authorLiu, Xinting
dc.contributor.authorGoyal, Shivam
dc.contributor.authorLahoti, Nishant
dc.contributor.authorChandrasekaran, Dhivya
dc.contributor.authorKurzer, Margarida
dc.contributor.authorKalsi, Jatinderpal
dc.contributor.authorSanderson, Saskia
dc.contributor.authorLanceley, Anne
dc.contributor.authorAhmed, Munaza
dc.contributor.authorSide, Lucy
dc.contributor.authorGentry-Maharaj, Aleksandra
dc.contributor.authorWallis, Yvonne
dc.contributor.authorWallace, Andrew
dc.contributor.authorWaller, Jo
dc.contributor.authorLuccarini, Craig
dc.contributor.authorYang, Xin
dc.contributor.authorDennis, Joe
dc.contributor.authorDunning, Alison
dc.contributor.authorLee, Andrew
dc.contributor.authorAntoniou, Antonis C
dc.contributor.authorLegood, Rosa
dc.contributor.authorMenon, Usha
dc.contributor.authorJacobs, Ian
dc.contributor.authorManchanda, Ranjit
dc.date.accessioned2020-06-03T00:12:23Z
dc.date.available2020-06-03T00:12:23Z
dc.date.issued2020-05-15
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 2020 , ' Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention ' , Cancers , vol. 12 , no. 5 , 1241 . https://doi.org/10.3390/cancers12051241
dc.identifier.issn2072-6694
dc.identifier.otherORCID: /0000-0002-0194-6389/work/74884692
dc.identifier.urihttp://hdl.handle.net/2299/22803
dc.description© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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
dc.format.extent21
dc.format.extent605074
dc.language.isoeng
dc.relation.ispartofCancers
dc.subjectBRCA1
dc.subjectBRCA2
dc.subjectBRIP1
dc.subjectOvarian cancer risk
dc.subjectPopulation genetic testing
dc.subjectRAD51C
dc.subjectRAD51D
dc.subjectRisk modelling
dc.subjectRisk stratification
dc.subjectSNP
dc.subjectOncology
dc.subjectCancer Research
dc.titlePopulation Study of Ovarian Cancer Risk Prediction for Targeted Screening and Preventionen
dc.contributor.institutionSchool of Physics, Astronomy and Mathematics
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85085106601&partnerID=8YFLogxK
rioxxterms.versionofrecord10.3390/cancers12051241
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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