Computer-aided diagnosis scheme for identifying histological classification of clustered microcalcifications by use of follow-up magnification mammograms.
OBJECTIVE
Our purpose in this study was to investigate the usefulness of follow-up magnification mammograms (i.e., both current and previous magnification mammograms) in a computer-aided diagnosis (CAD) scheme for identifying the histological classification of clustered microcalcifications.


METHODS
Our database consisted of current and previous magnification mammograms obtained from 93 patients before and after 3-month follow-up: 11 invasive carcinomas, 19 noninvasive carcinomas of the comedo type, 25 noninvasive carcinomas of the noncomedo type, 23 mastopathies, and 15 fibroadenomas. In our CAD scheme, we extracted five objective features of clustered microcalcifications from each of the current and previous magnification mammograms by taking into account image features that experienced radiologists commonly use to identify histological classifications. These features were then merged by a modified Bayes discriminant function for distinguishing among five histological classifications. For the input of the modified Bayes discriminant function, we used five objective features obtained from the previous magnification mammogram (previous features), five objective features obtained from the current magnification mammogram (current features), and the set of the five previous features and the five current features.


RESULTS
The classification accuracies with the five current features were higher than those with the five previous features. These classification accuracies were improved substantially by using the set of the five previous features and the five current features. For the set of the five previous features and the five current features, the classification accuracies of our CAD scheme were 81.8% (9 of 11) for invasive carcinoma, 84.2% (16 of 19) for noninvasive carcinoma of the comedo type, 76.0% (19 of 25) for noninvasive carcinoma of the noncomedo type, 73.9% (17 of 23) for mastopathy, and 86.8% (13 of 15) for fibroadenoma.


CONCLUSIONS
Our CAD scheme with use of follow-up magnification mammograms improved classification performance for mammographic clustered microcalcifications.
Nakayama
Ryohei
R
Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu, 514-8507, Japan. nakayama@clin.medic.mie-u.ac.jp
Watanabe
Ryoji
R
Namba
Kiyoshi
K
Takeda
Kan
K
Yamamoto
Koji
K
Katsuragawa
Shigehiko
S
Doi
Kunio
K
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D002114:Calcinosis / Q000000981:diagnostic imaging* / Q000473:pathology*; D016000:Cluster Analysis; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008327:Mammography / Q000379:methods*; D011230:Precancerous Conditions / Q000000981:diagnostic imaging* / Q000473:pathology*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity