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CAD Does Not Improve Mammogram Accuracy

July 28, 2011

The use of computer-aided detection (CAD) software to help analyze and interpret mammograms does not improve accuracy, according to a study published online July 27 in the Journal of the National Cancer Institute.

CAD software is currently used for analyzing three out of four mammograms in the United States.  The technology is used to help identify patterns associated with breast cancers and to mark potential abnormalities for the radiologist to consider.

To determine whether CAD leads to more accurate reading of mammograms, Dr. Joshua Fenton at the University of California, Davis, and colleagues analyzed data from more than 1.6 million film screening mammograms conducted at facilities in seven states from 1998 to 2006.

Of 90 total facilities, 25 utilized CAD for an average of 27.5 months during the study period. 

The researchers gathered data on women who had mammograms with and without CAD, including whether they were diagnosed with breast cancer within a year of the screening.

They determined that CAD was associated with more false positives “”identifications of tumors that turned out to be false “” and did not improve detection of invasive cancers.

Furthermore, the cancers detected using CAD were no more likely to be smaller or at a lower stage or to have less lymph node involvement than those detected without CAD.

The results were the same after adjusting for patient age, breast density, use of hormone replacement therapy, and other factors that might influence mammography findings.

“As currently implemented in U.S. practice, CAD appears to increase a woman’s risk of being recalled for further testing after screening mammography while yielding equivocal health benefit,” they wrote.

The use of CAD costs Medicare more than $30 million a year, the researchers noted.

In an accompanying editorial, Donald Berry, Ph.D., of the M.D. Anderson Cancer Center in Houston argues that any benefit to CAD is likely to be so small that it would be difficult to detect even in a very large randomized study.

Moreover, improving the sensitivity of CAD might find less aggressive tumors or those that would otherwise show up between mammograms. Early detection of such tumors, is not likely to have much of an impact on breast cancer mortality, he wrote.

Dr. Berry concludes that researchers should work to make CAD software more useful, but that “this should happen in an experimental setting and not while exposing millions of women to a technology that may be more harmful than it is beneficial.”

Meanwhile, a separate but related study found that women with breasts that appear dense on mammograms are at a higher risk of breast cancer, and that the tumors are more likely to have certain aggressive characteristics than women with less dense breasts.

Mammographic breast density–a reflection of the proportions of fat, connective tissue, and epithelial tissue in the breast–is a well-established risk factor for breast cancer.

Women with higher amounts of epithelial and stromal tissue have higher density and higher risk.

However, it has not been clear whether breast density was associated with specific tumor characteristics and tumor type.

To explore this issue, researchers at Harvard Medical School and Brigham and Women’s Hospital in Boston compared breast density in 1,042 postmenopausal women with breast cancer and 1,794 matched control subjects.

As expected, they found that the risk of breast cancer increased progressively with increasing breast density.   These associations were stronger for larger tumors than for smaller tumors; for high-grade than for low-grade tumors; and for estrogen receptor-negative than for estrogen receptor-positive tumors.

The link between density and breast cancer also appeared to be stronger for ductal carcinoma in situ (DCIS) than for invasive tumors.

There was no association, however, between density and other markers of tumor aggressiveness, such as nodal involvement and HER2 status.

The researchers concluded that higher mammographic density is associated with more aggressive tumor characteristics and also with DCIS.

“Our results suggest that breast density influences the risk of breast cancer subtypes by potentially different mechanisms,” they researchers wrote in their report.

“Further studies are warranted to explain underlying biological processes and elucidate the possible pathways from high breast density to the specific subtypes of breast carcinoma.”

An editorial accompanying the study agrees that understanding the biological links between breast density and specific tumor subtypes could help experts understand more about breast cancer risk and the molecular causes of breast cancer.

Dr. Karla Kerlikowske, of the University of California, San Francisco and Dr. Amanda Phipps of Fred Hutchinson Cancer Research Center in Seattle said the current large study was the first to find a stronger association between breast density and ER-negative tumors rather than ER-positive tumors.

However, they caution that this association might be due, in part, to a ‘masking effect”, which can occur because cancerous tissue and mammographically dense breast tissue have similar x-ray attenuation, allowing tumors to go undetected on screening mammography and progress to a more advanced and aggressive stage before detection,” they wrote.

In the current study, it is not known whether the tumors were detected by screening mammography. 

The editorialists also discuss other possible reasons for the strong link between density and aggressive tumors, including the interaction of increased numbers of stromal and epithelial cells in dense breasts and exposure to postmenopausal hormones.

They conclude that breast density is an important risk factor for diverse subtypes of breast cancer.

“Given that the magnitude of the association with breast density is strong across all breast cancer subtypes and particularly for ER-negative disease, breast density should be included in risk prediction models across tumor subtypes,” they said.

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