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Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention
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 ri...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281662/ https://www.ncbi.nlm.nih.gov/pubmed/32429029 http://dx.doi.org/10.3390/cancers12051241 |
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author | Gaba, Faiza Blyuss, Oleg Liu, Xinting Goyal, Shivam Lahoti, Nishant Chandrasekaran, Dhivya Kurzer, Margarida Kalsi, Jatinderpal Sanderson, Saskia Lanceley, Anne Ahmed, Munaza Side, Lucy Gentry-Maharaj, Aleksandra Wallis, Yvonne Wallace, Andrew Waller, Jo Luccarini, Craig Yang, Xin Dennis, Joe Dunning, Alison Lee, Andrew Antoniou, Antonis C. Legood, Rosa Menon, Usha Jacobs, Ian Manchanda, Ranjit |
author_facet | Gaba, Faiza Blyuss, Oleg Liu, Xinting Goyal, Shivam Lahoti, Nishant Chandrasekaran, Dhivya Kurzer, Margarida Kalsi, Jatinderpal Sanderson, Saskia Lanceley, Anne Ahmed, Munaza Side, Lucy Gentry-Maharaj, Aleksandra Wallis, Yvonne Wallace, Andrew Waller, Jo Luccarini, Craig Yang, Xin Dennis, Joe Dunning, Alison Lee, Andrew Antoniou, Antonis C. Legood, Rosa Menon, Usha Jacobs, Ian Manchanda, Ranjit |
author_sort | Gaba, Faiza |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7281662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72816622020-06-17 Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention Gaba, Faiza Blyuss, Oleg Liu, Xinting Goyal, Shivam Lahoti, Nishant Chandrasekaran, Dhivya Kurzer, Margarida Kalsi, Jatinderpal Sanderson, Saskia Lanceley, Anne Ahmed, Munaza Side, Lucy Gentry-Maharaj, Aleksandra Wallis, Yvonne Wallace, Andrew Waller, Jo Luccarini, Craig Yang, Xin Dennis, Joe Dunning, Alison Lee, Andrew Antoniou, Antonis C. Legood, Rosa Menon, Usha Jacobs, Ian Manchanda, Ranjit Cancers (Basel) Article 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. MDPI 2020-05-15 /pmc/articles/PMC7281662/ /pubmed/32429029 http://dx.doi.org/10.3390/cancers12051241 Text en © 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/). |
spellingShingle | Article Gaba, Faiza Blyuss, Oleg Liu, Xinting Goyal, Shivam Lahoti, Nishant Chandrasekaran, Dhivya Kurzer, Margarida Kalsi, Jatinderpal Sanderson, Saskia Lanceley, Anne Ahmed, Munaza Side, Lucy Gentry-Maharaj, Aleksandra Wallis, Yvonne Wallace, Andrew Waller, Jo Luccarini, Craig Yang, Xin Dennis, Joe Dunning, Alison Lee, Andrew Antoniou, Antonis C. Legood, Rosa Menon, Usha Jacobs, Ian Manchanda, Ranjit Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention |
title | Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention |
title_full | Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention |
title_fullStr | Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention |
title_full_unstemmed | Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention |
title_short | Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention |
title_sort | population study of ovarian cancer risk prediction for targeted screening and prevention |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281662/ https://www.ncbi.nlm.nih.gov/pubmed/32429029 http://dx.doi.org/10.3390/cancers12051241 |
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