ArticleBreast cancer research : BCR2015
The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms.
Article in Breast cancer research : BCR, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 114 citations in OpenAlex.
- Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.Breast cancer research : BCR · 2018Pooled it
- Analysis of Lifestyle and Genetic Risk Factors in Urban Women in China Who Had Malignant or Suspected Malignant Breast Nodules Identified via Breast Cancer Screening.Breast care (Basel, Switzerland) · 2025Article
- Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older.Journal of the National Cancer Institute · 2024Article
- A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.Radiology. Artificial intelligence · 2023Article
- Breast cancer prediction model based on clinical and biochemical characteristics: clinical data from patients with benign and malignant breast tumors from a single center in South China.Journal of cancer research and clinical oncology · 2023Article
- Cancer situation in China: what does the China cancer map indicate from the first national death survey to the latest cancer registration?Cancer communications (London, England) · 2023Article
- Prediction of Breast Cancer using Machine Learning Approaches.Journal of biomedical physics & engineering · 2022Article
- Prediction for Breast Cancer in BI-RADS Category 4 Lesion Categorized by Age and Breast Composition of Women in Songklanagarind Hospital.Asian Pacific journal of cancer prevention : APJCP · 2021Article
- Discriminatory Accuracy of the Gail Model for Breast Cancer Risk Assessment among Iranian Women.Iranian journal of public health · 2020Article
- Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review.Heliyon · 2020Review
- Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women.Journal of the National Cancer Institute · 2017Article
- Can the breast screening appointment be used to provide risk assessment and prevention advice?Breast cancer research : BCR · 2015Article
- Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography.BMC cancer · 2015Article
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9 authors at 1 institution in 1 country.
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Abstract
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.
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