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Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention.

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.

SUBMITTER: Gaba F

PROVIDER: S-EPMC7281662 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention.

Gaba Faiza F Blyuss Oleg O Liu Xinting X Goyal Shivam S Lahoti Nishant N Chandrasekaran Dhivya D Kurzer Margarida M Kalsi Jatinderpal J Sanderson Saskia S Lanceley Anne A Ahmed Munaza M Side Lucy L Gentry-Maharaj Aleksandra A Wallis Yvonne Y Wallace Andrew A Waller Jo J Luccarini Craig C Yang Xin X Dennis Joe J Dunning Alison A Lee Andrew A Antoniou Antonis C AC Legood Rosa R Menon Usha U Jacobs Ian I Manchanda Ranjit R

Cancers 20200515 5

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.<h4>Inclusion criteria</h4>women ≥18 years.<h4>Exclusion criteria</h4>prior ovarian/tubal/peritoneal cancer, previous gene ...[more]

PMID: 32429029

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Project description:IntroductionIdentification of individuals at high risk for lung cancer should be of value to individuals, patients, clinicians, and researchers. Existing prediction models have only modest capabilities to classify persons at risk accurately.MethodsProspective data from 70 962 control subjects in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) were used in models for the general population (model 1) and for a subcohort of ever-smokers (N = 38 254) (model 2). Both models included age, socioeconomic status (education), body mass index, family history of lung cancer, chronic obstructive pulmonary disease, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. Model 2 also included smoking quit-time (time in years since ever-smokers permanently quit smoking). External validation was performed with 44 223 PLCO intervention arm participants who completed a supplemental questionnaire and were subsequently followed. Known available risk factors were included in logistic regression models. Bootstrap optimism-corrected estimates of predictive performance were calculated (internal validation). Nonlinear relationships for age, pack-years smoked, smoking duration, and quit-time were modeled using restricted cubic splines. All reported P values are two-sided.ResultsDuring follow-up (median 9.2 years) of the control arm subjects, 1040 lung cancers occurred. During follow-up of the external validation sample (median 3.0 years), 213 lung cancers occurred. For models 1 and 2, bootstrap optimism-corrected receiver operator characteristic area under the curves were 0.857 and 0.805, and calibration slopes (model-predicted probabilities vs observed probabilities) were 0.987 and 0.979, respectively. In the external validation sample, models 1 and 2 had area under the curves of 0.841 and 0.784, respectively. These models had high discrimination in women, men, whites, and nonwhites.ConclusionThe PLCO lung cancer risk models demonstrate high discrimination and calibration.

Dataset Information

Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention.

Publications

Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention.

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