Dataset Information

Breast cancer risk prediction and mammography biopsy decisions: a model-based study.

ABSTRACT:

Background

Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening.

Purpose

To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy.

Methods

The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single-nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011.

Results

Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BI-RADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold.

Conclusions

The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors.

SUBMITTER: Armstrong K

PROVIDER: S-EPMC3527848 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Publications

Breast cancer risk prediction and mammography biopsy decisions: a model-based study.

Armstrong Katrina K Handorf Elizabeth A EA Chen Jinbo J Bristol Demeter Mirar N MN

American journal of preventive medicine 20130101 1

<h4>Background</h4>Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening.<h4>Purpose</h4>To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and D ...[more]

PMID: 23253645

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Project description:Importance:Multiparametric magnetic resonance imaging (MRI) in conjunction with MRI-transrectal ultrasound (TRUS) fusion-guided biopsies have improved the detection of prostate cancer. It is unclear whether MRI itself adds additional value to multivariable prediction models based on clinical parameters. Objective:To determine whether an MRI-based prediction model can reduce unnecessary biopsies in patients with suspected prostate cancer. Design, Setting, and Participants:Patients underwent MRI, MRI-TRUS fusion-guided biopsy, and 12-core systematic biopsy in 1 session. The development cohort used to derive the prediction model consisted of 400 patients from 1 institution enrolled between May 14, 2015, and August 31, 2016, and the validation cohort included 251 patients from 2 independent institutions who underwent biopsies between April 1, 2013, and June 30, 2016, at 1 institution and between July 1, 2015, and October 31, 2016, at the other institution. The MRI model included MRI-derived parameters in addition to clinical variables. Area under the curve of receiver operating characteristic curves and decision curve analysis were performed. Main Outcomes and Measures:Risk of clinically significant prostate cancer on biopsy, defined as a Gleason score of 3?+?4 or higher in at least 1 biopsy core. Results:Overall, 193 (48.3%) of the 400 patients in the development cohort (mean [SD] age at biopsy, 64.3 [7.1] years) and 96 (38.2%) of the 251 patients in the validation cohort (mean [SD] age at biopsy, 64.9 [7.2] years) had clinically significant prostate cancer, defined as a Gleason score greater than or equal to 3?+?4. By applying the model to the external validation cohort, the area under the curve increased from 64% to 84% compared with the baseline model (P?<?.001). At a risk threshold of 20%, the MRI model had a lower false-positive rate than the baseline model (46% [95% CI, 32%-66%] vs 92% [95% CI, 70%-100%]), with only a small reduction in the true-positive rate (89% [95% CI, 85%-96%] vs 99% [95% CI, 89%-100%]). Eighteen of 100 fewer biopsies could have been performed, with no increase in the number of patients with missed clinically significant prostate cancers. Conclusions and Relevance:The inclusion of MRI-derived parameters in a risk model could reduce the number of unnecessary biopsies while maintaining a high rate of diagnosis of clinically significant prostate cancers.

Dataset Information

Breast cancer risk prediction and mammography biopsy decisions: a model-based study.

Background

Purpose

Methods

Results

Conclusions

Publications

Breast cancer risk prediction and mammography biopsy decisions: a model-based study.

Similar Datasets

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