Statistical analysis can help reduce needless breast biopsies

Imaging studies that identify suspicious lesions in women’s breasts often lead to biopsies, even in cases that are only minimally suspicious. These tests to rule out false positives can cause needless anxiety and risks to their health.

But combining the use of statistical methods with image analysis may have the potential to downgrade the risk classification of breast masses, thus reducing the need for unnecessary breast biopsies, according to clinicians from Seno Medical and collaborators from The University of Texas.

Results of the study were recently published in the American Journal of Roentgenology.

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Radiologists who interpret breast images use a classification system to sort them into one of six major categories of the Breast Imaging and Reporting Data System, commonly called BI-RADS. Assessment categories 1 to 3 (negative, benign or probably benign) indicate low risk; categories 5 and 6 (highly suggestive of malignancy and known biopsy-proven) indicate high likelihood of cancer. It’s breast masses put in category 4 (suspicious) that often require biopsies to determine potential malignancy.

Category 4 is divided into three subgroups; one is for masses classified as low suspicion of malignancy (2 percent to 10 percent likelihood). However, because of conservative practice, it’s not uncommon for women in this category to undergo biopsies to rule out malignancy.

However, researchers for the study suggest that the use of a statistical calculation known as the Negative Likelihood Ratio (NLR) can be calculated from a diagnostic test’s sensitivity and specificity can be used to reduce the probability that a breast mass is sufficiently suspicious to require a biopsy.

Depending on the imaging modality, which have varying NLRs, some of the low-risk masses can be downgraded to a post-test probability of 2 percent or less. Dropping the probability of malignancy of masses in the least suspicious subcategory of BI-RADS category 4 can mean biopsies aren’t needed, contends Thomas Stavros, MD, chief medical officer for Seno Medical and co-author of the report.

“The use of the Negative Likelihood Ratio along with the BI-RADS 4 subcategories can help to reduce the number of false positives without experiencing excessive negative results that would lead to cancer going undiagnosed,” he says.

“Reducing the number of unnecessary breast biopsies is an essential advancement toward improving women’s healthcare and protecting breast health,” adds Pam Otto, MD, of the department of radiology at The University of Texas Health Science Center at San Antonio, co-author of the report. She encourages those conducting breast imaging analysis to use images and statistical analysis in deciding whether biopsies are warranted.

Minimizing false positive studies with these tools can result in “minimum adverse effect on sensitivity, optimizing their patients’ breast health,” Otto contends. “The availability of web-based programs for automating the NLR calculations should help to facilitate routine use of this important statistical tool.”