Detection of potential microcalcification clusters using multivendor for-presentation digital mammograms for short-term breast cancer risk estimation.

PURPOSE

We explore using the number of potential microcalcification clusters detected in for-presentation mammographic images (the images which are typically accessible to large epidemiological studies) a marker of short-term breast cancer risk.

METHODS

We designed a three-step algorithm for detecting potential microcalcification clusters in for-presentation digital mammograms. We studied association with short-term breast cancer risk using a nested case control design, with a mammography screening cohort as a source population. In total, 373 incident breast cancer cases (diagnosed at least 3 months after a negative screen at study entry) and 1466 matched controls were included in our study. Conditional logistic regression Wald tests were used to test for association with the presence of microcalcifications at study entry. We compared results of these analyses to those obtained using a Computer-aided Diagnosis (CAD) software (VuComp) on corresponding for-processing images (images which are used clinically, but typically not saved).

RESULTS

We found a moderate agreement between our measure of potential microcalcification clusters on for-presentation images and a CAD measure on for-processing images. Similar evidence of association with short-term breast cancer risk was found (P =  and P =  , for our approach on for-presentation images and for the CAD measure on for-processing images, respectively) and interestingly both measures contributed independently to association with a short-term risk (P =  for the CAD measure, adjusted for our proposed method and P =  for our proposed method, adjusted for the CAD measure).

CONCLUSION

Meaningful measurement of potential microcalcifications, in the context of short-term breast cancer risk assessment, is feasible for for-presentation images across a range of vendors. Our algorithm for for-presentation images performs similarly to a CAD algorithm on for-processing images, hence our algorithm can be a useful tool for research on microcalcifications and their role on breast cancer risk, based on large-scale epidemiological studies with access to for-presentation images.