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Computer‐Based Margin Analysis of Breast Sonography for Differentiating Malignant and Benign Masses
Author(s) -
Sehgal Chandra M.,
Cary Theodore W.,
Kangas Sarah A.,
Weinstein Susan P.,
Schultz Susan M.,
Arger Peter H.,
Conant Emily F.
Publication year - 2004
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.7863/jum.2004.23.9.1201
Subject(s) - medicine , margin (machine learning) , radiology , breast tissue , pathology , breast cancer , cancer , machine learning , computer science
Objective. To evaluate the role of quantitative margin features in the computer‐aided diagnosis of malignant and benign solid breast masses using sonographic imaging. Methods. Sonographic images from 56 patients with 58 biopsy‐proven masses were analyzed quantitatively for the following features: margin sharpness, margin echogenicity, and angular variation in margin. Of the 58 masses, 38 were benign and 20 were malignant. Each feature was evaluated individually and in combination with the others to determine its association with malignancy. The combination of features yielding the highest association with malignancy was analyzed by logistic regression to determine the probability of malignancy. The performance of the probability measurements was evaluated by receiver operating characteristic analysis using a round‐robin technique. Results. Margin sharpness, margin echogenicity, and angular variation in margin were significantly different for the malignant and benign masses ( P < .03, 2‐tailed Student t test). According to quantitative measures, tumor‐tissue margins of the malignant masses were less distinct than for the benign masses. Although the mean size of the lesions for the two groups was the same, the mean age of the patients was statistically different ( P = .000625). After logistic regression analysis, the individual features age, margin sharpness, margin echogenicity, and angular variation in margin were found to be associated with the probability of malignancy ( P < .03). The area under the receiver operating characteristic curve ± SD for the 3‐feature logistic regression model combining age, margin echogenicity, and angular variation of margin was 0.87 ± 0.05. Conclusions. The proposed quantitative margin features are robust and can reliably measure margin distinctiveness. These features combined with logistic regression analysis can be useful for computer‐aided diagnosis of solid breast lesions.