How Math Can Potentially Reduce Unnecessary Breast Biopsies

The perceived risk of missing a breast cancer diagnosis with imaging studies often leads to unnecessary breast biopsies, according to a new report published in the American Journal of Roentgenology. The authors showed how statistical methods can be used to downgrade the risk classification of breast masses to reduce the need for unnecessary biopsies. Clinicians from San Antonio, TX-based Seno Medical and medical center collaborators from the University of Texas (MD Anderson and U of Texas Health Sciences Center) co-authored the report. The authors studied a statistical calculation known as the negative likelihood ratio (NLR), which can be calculated from a diagnostic test's sensitivity and specificity, and outlined how the breast imaging and reporting data system (BI-RADS) 4A subcategory has low enough and narrow enough range of pre-test probabilities to allow downgrading to a post-test probability of 2% or less after a negative diagnostic imaging test with an adequately low NLR. Each BI-RADS category is associated with a specific range of risks of breast cancer. The approach includes the following steps: Classify lesions according to BI-RADS category 4 subcategories. Subcategory 4A represents the subcategory where the range of PPVs is both low enough and narrow enough to allow downgrading to BI-RADS category 3. Achieve the positive predictive value (PPV) is within the American College of Radiology (ACR) benchmark PPV range for BI-RADS subcategory 4A, which is greater...
Source: MDDI - Category: Medical Devices Authors: Tags: Imaging Source Type: news