Evaluation of an automated computer-aided diagnosis system for the detection of masses on prior mammograms

We have developed a computer algorithm to detect breast masses on digitized mammograms. In this study, we analyze the performance of the trained algorithm with independent, clinical mammograms to assess its potential as an aid to the radiologist in mammographic interpretation. A digitized mammogram is processed with an adaptive enhancement filter followed by region growing to detect significant breast structures. Morphological and texture features are then extracted from each of the detected structures and used to identify potential breast masses. In the current study, we evaluated the performance of the algorithm with independent sets of 92 prior mammograms (films acquired 1 to 4 years prior to biopsy) and 260 preoperative mammograms from 123 patients. The computer algorithm had a "by-film" mass detection sensitivity of 51% with 2.3 FPs/image when applied to the prior mammograms including the detection of 57% of the malignant masses. When applied to the set of preoperative mammograms, the algorithm identified 73% of the masses with 2.2 FPs/image and had a malignant mass detection sensitivity of 83%. The "by-case" sensitivity was 67% (74% for malignant masses) and 85% (92% for malignant masses) for the prior and preoperative mammograms, respectively. This study indicates that the computer algorithm may be useful as a second reader in the clinical interpretation of mammograms because it has the ability to detect masses in both preoperative and prior mammograms.