Dataset Information

Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women.

ABSTRACT: BACKGROUND:There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population. METHODS:We used data from 40,158 women aged 50-69?years (via the lifepool cohort) participating in Australia's BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance. RESULTS:Over a median of 4.3?years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O)?=?1.09, 95% confidence interval (CI) 1.00-1.18). There was good agreement across decile groups of Gail scores (?2?=?7.1, p?=?0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O?=?1.65, 95% CI 1.33-2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73-3.02, p?

SUBMITTER: Nickson C

PROVIDER: S-EPMC6302513 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women.

Nickson Carolyn C Procopio Pietro P Velentzis Louiza S LS Carr Sarah S Devereux Lisa L Mann Gregory Bruce GB James Paul P Lee Grant G Wellard Cameron C Campbell Ian I

Breast cancer research : BCR 20181220 1

<h4>Background</h4>There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population.<h4>Methods</h4>We used data from 40,158 women aged 50-69 years (via the lifepool c ...[more]

PMID: 30572910

Similar Datasets

Project description:The Gail model for predicting the absolute risk of invasive breast cancer has been validated extensively in US populations, but its performance in the international setting remains uncertain. We evaluated the predictive accuracy of the Gail model in 54,649 Spanish women aged 45-68 years who were free of breast cancer at the 1996-1998 baseline mammographic examination in the population-based Navarre Breast Cancer Screening Program. Incident cases of invasive breast cancer and competing deaths were ascertained until the end of 2005 (average follow-up of 7.7 years) through linkage with population-based cancer and mortality registries. The Gail model was tested for calibration and discrimination in its original form and after recalibration to the lower breast cancer incidence and risk factor prevalence in the study cohort, and compared through cross-validation with a Navarre model fully developed from this cohort. The original Gail model overpredicted significantly the 835 cases of invasive breast cancer observed in the cohort (ratio of expected to observed cases 1.46, 95 % CI 1.36-1.56). The recalibrated Gail model was well calibrated overall (expected-to-observed ratio 1.00, 95 % CI 0.94-1.07), but it tended to underestimate risk for women in low-risk quintiles and to overestimate risk in high-risk quintiles (P = 0.01). The Navarre model showed good cross-validated calibration overall (expected-to-observed ratio 0.98, 95 % CI 0.92-1.05) and in different cohort subsets. The Navarre and Gail models had modest cross-validated discrimination indexes of 0.542 (95 % CI 0.521-0.564) and 0.544 (95 % CI 0.523-0.565), respectively. Although the original Gail model cannot be applied directly to populations with different underlying rates of invasive breast cancer, it can readily be recalibrated to provide unbiased estimates of absolute risk in such populations. Nevertheless, its limited discrimination ability at the individual level highlights the need to develop extended models with additional strong risk factors.

Project description:OBJECTIVES:To assess the health burden of breast hypertrophy and the comparative effectiveness of breast reduction surgery in improving health-related quality of life. DESIGN:Prospective cohort study. SETTING:A major public tertiary care hospital in Australia. PARTICIPANTS:Women with symptomatic breast hypertrophy who underwent breast reduction surgery were followed for 12 months. A comparison control cohort comprised women with breast hypertrophy who did not undergo surgery. INTERVENTIONS:Bilateral breast reduction surgery for women in the surgical cohort. MAIN OUTCOME MEASURES:The primary outcome measure was health-related quality of life measured preoperatively and at 3, 6 and 12 months postoperatively using the Short Form-36 (SF-36) questionnaire. Secondary outcome measures included post-surgical complications. RESULTS:209 patients in the surgical cohort completed questionnaires before and after surgery. 124 patients in the control hypertrophy cohort completed baseline and 12-month follow-up questionnaires. At baseline, both groups had significantly lower scores compared with population norms across all scales (p<0.001). In the surgical cohort significant improvements were seen across all eight SF-36 scales (p<0.001) following surgery. Within 3 months of surgery scores were equivalent to those of the normal population and this improvement was sustained at 12 months. SF-36 physical and mental component scores both significantly improved following surgery, with a mean change of 10.2 and 9.2 points, respectively (p<0.001). In contrast, SF-36 scores for breast hypertrophy controls remained at baseline across 12 months. The improvement in quality of life was independent of breast resection weight and body mass index. CONCLUSION:Breast reduction significantly improved quality of life in women with breast hypertrophy. This increase was most pronounced within 3?months of surgery and sustained at 12-month follow-up. This improvement in quality of life is comparable to other widely accepted surgical procedures. Furthermore, women benefit from surgery regardless of factors including body mass index and resection weight.

Dataset Information

Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women.

Publications

Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets