dc.contributor.author | Gaba, F | |
dc.contributor.author | Blyuss, O | |
dc.contributor.author | Liu, X | |
dc.contributor.author | Goyal, S | |
dc.contributor.author | Lahoti, N | |
dc.contributor.author | Chandrasekaran, D | |
dc.contributor.author | Kurzer, M | |
dc.contributor.author | Kalsi, J | |
dc.contributor.author | Sanderson, S | |
dc.contributor.author | Lanceley, A | |
dc.contributor.author | Ahmed, M | |
dc.contributor.author | Side, L | |
dc.contributor.author | Gentry-Maharaj, A | |
dc.contributor.author | Wallis, Y | |
dc.contributor.author | Wallace, A | |
dc.contributor.author | Waller, J | |
dc.contributor.author | Luccarini, C | |
dc.contributor.author | Yang, X | |
dc.contributor.author | Dennis, J | |
dc.contributor.author | Dunning, A | |
dc.contributor.author | Lee, A | |
dc.contributor.author | Antoniou, AC | |
dc.contributor.author | Legood, R | |
dc.contributor.author | Menon, U | |
dc.contributor.author | Jacobs, I | |
dc.contributor.author | Manchanda, R | |
dc.date.accessioned | 2020-06-09T14:26:34Z | |
dc.date.available | 2020-06-09T14:26:34Z | |
dc.date.issued | 2020-05-15 | |
dc.identifier.citation | Gaba, 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.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/64757 | |
dc.description.abstract | Unselected 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.extent | 1241 - 1241 | |
dc.language | en | |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.relation.ispartof | Cancers | |
dc.rights | Creative Commons Attribution (CC BY) license | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.title | Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2020 by the authors. | |
dc.identifier.doi | 10.3390/cancers12051241 | |
pubs.issue | 5 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
pubs.volume | 12 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |