Recalibration of the Gail model for predicting invasive breast cancer risk in Spanish women: a population-based cohort study.
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SUBMITTER: Pastor-Barriuso R
PROVIDER: S-EPMC3586062 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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?<?0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06-1.70, p?=?0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44-0.79, p?<?0.0001). Similar patterns were observed separately for women aged 50-59 and 60-69?years. The model's overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56-0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups. CONCLUSIONS:This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50-69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening.
| S-EPMC6302513 | biostudies-literature
Project description:BACKGROUND:Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35-50. METHODS:In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers. RESULTS:The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer. CONCLUSIONS:AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35-50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.
| S-EPMC6425605 | biostudies-literature
Project description:BackgroundThe Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA).MethodsThree systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive.ResultsGail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively.ConclusionsThe Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses.Trial registrationPROSPERO CRD42016047215 .
| S-EPMC5850919 | biostudies-literature
Project description:IntroductionMammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.MethodsWe recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology's Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.ResultsIn the multivariate model, age (odds ratio (OR)=1.05; 95% confidence interval (CI), 1.03 to 1.08; P<0.001), SNP panel relative risk (OR=2.30; 95% CI, 1.06 to 4.99, P=0.035) and BMI (≥30 kg/m2 versus <25 kg/m(2); OR=2.20; 95% CI, 1.05 to 4.58; P=0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR=2.20; 95% CI, 1.05 to 4.58; P=0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI<25 kg/m2. The SNP panel appeared to have predictive ability among both white and black women.ConclusionsBreast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.
| S-EPMC4311477 | biostudies-literature
Project description:No risk assessment tool is available for identifying high risk population of breast cancer (BCa) in Hong Kong. A case-control study including 918 BCa cases and 923 controls was used to develop the risk assessment model among Hong Kong Chinese women.Each participant received an in-depth interview to obtain their lifestyle and environmental risk factors. Least absolute shrinkage and selection operator (LASSO) selection model was used to select the optimal risk factors (LASSO-model). A risk score system was constructed to evaluate the cumulative effects of selected factors. Bootstrap simulation was used to test the internal validation of the model. Model performance was evaluated by receiver-operator characteristic curves and the area under the curve (AUC).Age, number of parity, number of BCa cases in 1st-degree relatives, exposure to light at night, and sleep quality were the common risk factors for all women. Alcohol drinking was included for premenopausal women; body mass index, age at menarche, age at 1st give birth, breast feeding, using of oral contraceptive, hormone replacement treatment, and history of benign breast diseases were included for postmenopausal women. The AUCs were 0.640 (95% CI, 0.598-0.681) and 0.655 (95% CI, 0.621-0.653) for pre- and postmenopausal women, respectively. Further subgroup evaluation revealed that the model performance was better for women aged 50 to 70 years or ER-positive.This BCa risk assessment tool in Hong Kong Chinese women based on LASSO selection is promising, which shows a slightly higher discriminative accuracy than those developed in other populations.
| S-EPMC4985325 | biostudies-other
Project description:PURPOSE:To determine whether there are changes in sexuality after breast cancer, to better understand the sexual function of women with breast cancer, and to investigate the potential relationship between sexual dysfunction and socio-demographic and clinical variables. METHODS:A cross-sectional study. This study included 514 women with breast cancer between 21- and 66-years-old. The cases were gathered between June 2016 and January 2017. The instruments used were the questionnaire on Women's Sexual Function and a questionnaire to collect socio-demographic and clinical data. RESULTS:The average age (± standard deviation, SD) of participants was 46.34 ± 8.28 years. Their average age at date of diagnosis was 42.26 ± 8.56 years, and the average time suffering from cancer was 4.05 ± 5.23 years. There were significant differences (p = 0.002) in the presence of sexual dysfunction before (32.1%) and after (91.2%) cancer. The primary sexual dysfunctions were due to penetration pain (50.6%), lubrication (50.6%), dysfunctional desire (44.6%), and dysfunctional excitement (44.6%). Two-thirds of participants were satisfied with their sexual relations. The women who presented most sexual dysfunction were those that had a bilateral mastectomy (p = 0.009) and those who received chemotherapy, radiotherapy and hormonal-therapy (p < 0.001). CONCLUSION:Sexual function was changed in women with breast cancer. The main problems included penetration pain, desire, lubrication, and dysfunctional excitement. It is important that Health professionals recognize which circumstances influence the sexual function of women with breast cancer and to make interventions that facilitate sexual adjustment.
| S-EPMC6118366 | biostudies-literature
Project description:OBJECTIVE:To evaluate the long term risks of invasive breast cancer and death from breast cancer after ductal carcinoma in situ (DCIS) diagnosed through breast screening. DESIGN:Population based observational cohort study. SETTING:Data from the NHS Breast Screening Programme and the National Cancer Registration and Analysis Service. PARTICIPANTS:All 35?024 women in England diagnosed as having DCIS by the NHS Breast Screening Programme from its start in 1988 until March 2014. MAIN OUTCOME MEASURES:Incident invasive breast cancer and death from breast cancer. RESULTS:By December 2014, 13?606 women had been followed for up to five years, 10?998 for five to nine years, 6861 for 10-14 years, 2620 for 15-19 years, and 939 for at least 20 years. Among these women, 2076 developed invasive breast cancer, corresponding to an incidence rate of 8.82 (95% confidence interval 8.45 to 9.21) per 1000 women per year and more than double that expected from national cancer incidence rates (ratio of observed rate to expected rate 2.52, 95% confidence interval 2.41 to 2.63). The increase started in the second year after diagnosis of DCIS and continued until the end of follow-up. In the same group of women, 310 died from breast cancer, corresponding to a death rate of 1.26 (1.13 to 1.41) per 1000 women per year and 70% higher than that expected from national breast cancer mortality rates (observed:expected ratio 1.70, 1.52 to 1.90). During the first five years after diagnosis of DCIS, the breast cancer death rate was similar to that expected from national mortality rates (observed:expected ratio 0.87, 0.69 to 1.10), but it then increased, with values of 1.98 (1.65 to 2.37), 2.99 (2.41 to 3.70), and 2.77 (2.01 to 3.80) in years five to nine, 10-14, and 15 or more after DCIS diagnosis. Among 29?044 women with unilateral DCIS undergoing surgery, those who had more intensive treatment (mastectomy, radiotherapy for women who had breast conserving surgery, and endocrine treatment in oestrogen receptor positive disease) and those with larger final surgical margins had lower rates of invasive breast cancer. CONCLUSIONS:To date, women with DCIS detected by screening have, on average, experienced higher long term risks of invasive breast cancer and death from breast cancer than women in the general population during a period of at least two decades after their diagnosis. More intensive treatment and larger final surgical margins were associated with lower risks of invasive breast cancer.
| S-EPMC7251423 | biostudies-literature
Project description:BackgroundCurrent online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions.ObjectiveWe aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China.MethodsThis study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT.ResultsThe training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values.ConclusionsOur prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China.
| S-EPMC7683252 | biostudies-literature
Project description:BackgroundDifferences in the incidence and outcome of breast cancer among Hispanic women compared with white women are well documented and are likely explained by ethnic differences in genetic composition, lifestyle, or environmental exposures. METHODOLGY/PRINCIPAL FINDINGS: A population-based study was conducted in Galicia, Spain. A total of 510 women diagnosed with operable invasive breast cancer between 1997 and 2010 participated in the study. Data on demographics, breast cancer risk factors, and clinico-pathological characteristics were collected. The different breast cancer tumor subtypes were compared on their clinico-pathological characteristics and risk factor profiles, particularly reproductive variables and breastfeeding. Among the 501 breast cancer patients (with known ER and PR receptors), 85% were ER+/PR+ and 15% were ER-&PR-. Among the 405 breast cancer with known ER, PR and HER2 status, 71% were ER+/PR+/HER2- (luminal A), 14% were ER+/PR+/HER2+ (luminal B), 10% were ER-/PR-/HER2- (triple negative breast cancer, TNBC), and 5% were ER-/PR-/HER2+ (non-luminal). A lifetime breastfeeding period equal to or longer than 7 months was less frequent in case patients with TNBC (OR?=?0.25, 95% CI?=?0.08-0.68) compared to luminal A breast cancers. Both a low (2 or fewer pregnancies) and a high (3-4 pregnancies) number of pregnancies combined with a long breastfeeding period were associated with reduced odds of TNBC compared with luminal A breast cancer, although the association seemed to be slightly more pronounced among women with a low number of pregnancies (OR?=?0.09, 95% CI?=?0.005-0.54).Conclusions/significanceIn case-case analyses with the luminal A cases as the reference group, we observed a lower proportion of TNBC among women who breastfed 7 or more months. The combination of longer breastfeeding duration and lower parity seemed to further reduce the odds of having a TNBC compared to a luminal A breast cancer.
| S-EPMC3394701 | biostudies-literature
Project description:PurposePrognostic models can help inform patients on the future course of their cancer and assist the decision making of clinicians and patients in respect to management and treatment of the cancer. In contrast to previous studies considering survival following treatment, this study aimed to develop a prognostic model to quantify breast cancer-specific survival at the time of diagnosis.MethodsA large (n = 3323), population-based prospective cohort of women were diagnosed with invasive breast cancer in Queensland, Australia between 2010 and 2013, and followed up to December 2018. Data were collected through a validated semi-structured telephone interview and a self-administered questionnaire, along with data linkage to the Queensland Cancer Register and additional extraction from medical records. Flexible parametric survival models, with multiple imputation to deal with missing data, were used.ResultsKey factors identified as being predictive of poorer survival included more advanced stage at diagnosis, higher tumour grade, "triple negative" breast cancers, and being symptom-detected rather than screen detected. The Harrell's C-statistic for the final predictive model was 0.84 (95% CI 0.82, 0.87), while the area under the ROC curve for 5-year mortality was 0.87. The final model explained about 36% of the variation in survival, with stage at diagnosis alone explaining 26% of the variation.ConclusionsIn addition to confirming the prognostic importance of stage, grade and clinical subtype, these results highlighted the independent survival benefit of breast cancers diagnosed through screening, although lead and length time bias should be considered. Understanding what additional factors contribute to the substantial unexplained variation in survival outcomes remains an important objective.
| S-EPMC9374611 | biostudies-literature