Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms.
OBJECTIVE
To evaluate whether a computer-aided diagnosis (CADx) technique can accurately classify breast calcifications in full-field digital mammograms (FFDMs) as malignant or benign. The computer technique was developed previously on screen-film mammograms (SFMs) in which individual calcifications were identified manually. The present study evaluated the computer technique independently on a new database of FFDM images with automatic detection of the individual calcifications.


METHODS
We analyzed 49 consecutive FFDM cases (19 cancers) that showed suspicious calcifications. Four mammography radiologists read soft-copy mammograms retrospectively and electronically indicated the region of calcifications in each image. The computer then automatically detected the individual calcifications within the indicated region and analyzed eight features of calcification morphology and distribution to arrive at an estimated likelihood of malignancy. The radiologists entered Breast Imaging Report and Data System assessments before and after seeing the computer results. Performance was analyzed using receiver operating characteristic analysis.


RESULTS
Despite variability in radiologist-indicated regions of calcifications, the computer achieved consistently high performance taking input from the four radiologists (receiver operating characteristic curve area, A(z): 0.80, 0.80, 0.78, and 0.77; differences not statistically significant). Previous results showed that the computer technique achieved an A(z) value of 0.80 on SFMs, which improved radiologists' performance significantly.


CONCLUSIONS
The computer technique appears to maintain consistently high performance in classifying calcifications in FFDMs as malignant or benign without requiring substantial modification from its initial development on SFMs. The computer performance appears to be robust with respect to variations in radiologists' input.
Rana
Rich S
RS
Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA.
Jiang
Yulei
Y
Schmidt
Robert A
RA
Nishikawa
Robert M
RM
Liu
Bei
B
D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000145:classification / Q000000981:diagnostic imaging*; D044584:Carcinoma, Ductal / Q000000981:diagnostic imaging; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008327:Mammography / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D012189:Retrospective Studies