Computer-based margin analysis of breast sonography for differentiating malignant and benign masses

被引:50
作者
Sehgal, CM [1 ]
Cary, TW [1 ]
Kangas, SA [1 ]
Weinstein, SP [1 ]
Schultz, SM [1 ]
Arger, PH [1 ]
Conant, EF [1 ]
机构
[1] Univ Penn, Med Ctr, Dept Radiol, Philadelphia, PA 19104 USA
关键词
breast imaging; breast neoplasm; breast sonography; computer-aided diagnosis; sonography;
D O I
10.7863/jum.2004.23.9.1201
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
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.
引用
收藏
页码:1201 / 1209
页数:9
相关论文
共 21 条
  • [1] Interreader variability and predictive value of US descriptions of solid breast masses: Pilot study
    Arger, PH
    Sehgal, CM
    Conant, EF
    Zuckerman, J
    Rowling, SE
    Patton, JA
    [J]. ACADEMIC RADIOLOGY, 2001, 8 (04) : 335 - 342
  • [2] Sonography of solid breast lesions: Observer variability of lesion description and assessment
    Baker, JA
    Kornguth, PJ
    Soo, MS
    Walsh, R
    Mengoni, P
    [J]. AMERICAN JOURNAL OF ROENTGENOLOGY, 1999, 172 (06) : 1621 - 1625
  • [3] ANALYSIS OF CANCERS MISSED AT SCREENING MAMMOGRAPHY
    BIRD, RE
    WALLACE, TW
    YANKASKAS, BC
    [J]. RADIOLOGY, 1992, 184 (03) : 613 - 617
  • [4] Breast lesions on sonograms: Computer-aided diagnosis with nearly setting-independent features and artificial neural networks
    Chen, CM
    Chou, YH
    Han, KC
    Hung, GS
    Tiu, CM
    Chiou, HJ
    Chiou, SY
    [J]. RADIOLOGY, 2003, 226 (02) : 504 - 514
  • [5] Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks
    Chen, DR
    Chang, RF
    Kuo, WJ
    Chen, MC
    Huang, YL
    [J]. ULTRASOUND IN MEDICINE AND BIOLOGY, 2002, 28 (10) : 1301 - 1310
  • [6] Computerized lesion detection on breast ultrasound
    Drukker, K
    Giger, ML
    Horsch, K
    Kupinski, MA
    Vyborny, CJ
    Mendelson, EB
    [J]. MEDICAL PHYSICS, 2002, 29 (07) : 1438 - 1446
  • [7] Computer aided classification of masses in ultrasonic mammography
    Dumane, VA
    Shankar, PM
    Piccoli, CW
    Reid, JM
    Forsberg, F
    Goldberg, BB
    [J]. MEDICAL PHYSICS, 2002, 29 (09) : 1968 - 1973
  • [8] IMPROVING THE DISTINCTION BETWEEN BENIGN AND MALIGNANT BREAST-LESIONS - THE VALUE OF SONOGRAPHIC TEXTURE ANALYSIS
    GARRA, BS
    KRASNER, BH
    HORII, SC
    ASCHER, S
    MUN, SK
    ZEMAN, RK
    [J]. ULTRASONIC IMAGING, 1993, 15 (04) : 267 - 285
  • [9] IMPROVEMENT IN SPECIFICITY OF ULTRASONOGRAPHY FOR DIAGNOSIS OF BREAST-TUMORS BY MEANS OF ARTIFICIAL-INTELLIGENCE
    GOLDBERG, V
    MANDUCA, A
    EWERT, DL
    GISVOLD, JJ
    GREENLEAF, JF
    [J]. MEDICAL PHYSICS, 1992, 19 (06) : 1475 - 1481
  • [10] HAKINSON S, 2002, TXB CANC EPIDEMIOLOG, P301