Malignant and benign clustered microcalcifications: automated feature analysis and classification.
OBJECTIVE
To develop a method for differentiating malignant from benign clustered microcalcifications in which image features are both extracted and analyzed by a computer.


METHODS
One hundred mammograms from 53 patients who had undergone biopsy for suspicious clustered microcalcifications were analyzed by a computer. Eight computer-extracted features of clustered microcalcifications were merged by an artificial neural network. Human input was limited to initial identification of the microcalcifications.


RESULTS
Computer analysis allowed identification of 100% of the patients with breast cancer and 82% of the patients with benign conditions. The accuracy of computer analysis was statistically significantly better than that of five radiologists (P = .03).


CONCLUSIONS
Quantitative features can be extracted and analyzed by a computer to distinguish malignant from benign clustered microcalcifications. This technique may help radiologists reduce the number of false-positive biopsy findings.
Jiang
Y
Y
Department of Radiology, University of Chicago, Illinois 60637, USA.
Nishikawa
R M
RM
Wolverton
D E
DE
Metz
C E
CE
Giger
M L
ML
Schmidt
R A
RA
Vyborny
C J
CJ
Doi
K
K
D001941:Breast Diseases / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008327:Mammography; D016571:Neural Networks, Computer; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity