Computer-aided US Diagnosis of Breast Lesions by Using Cell-based Contour Grouping

• Published in: Radiology Vol. 255; no. 3; pp. 746 - 754
• Main Authors: Cheng, Jie-Zhi, Chou, Yi-Hong, Huang, Chiun-Sheng, Chang, Yeun-Chung, Tiu, Chui-Mei, Chen, Kuei-Wu, Chen, Chung-Ming
• Format: Journal Article
• Language: English
• Published: Oak Brook, IL Radiological Society of North America 01.06.2010
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Biological and medical sciences; Breast Diseases - diagnostic imaging; Breast Diseases - pathology; Computerized, statistical medical data processing and models in biomedicine; Diagnosis, Computer-Assisted - methods; Diagnosis, Differential; Female; Humans; Logistic Models; Medical management aid. Diagnosis aid; Medical sciences; Middle Aged; Retrospective Studies; ROC Curve; Ultrasonography, Mammary; Breast; Grouping; Mammary gland; Lesion; Diagnostic aid
• ISSN: 0033-8419; 1527-1315; 1527-1315
• DOI: 10.1148/radiol.09090001

To develop a computer-aided diagnostic algorithm with automatic boundary delineation for differential diagnosis of benign and malignant breast lesions at ultrasonography (US) and investigate the effect of boundary quality on the performance of a computer-aided diagnostic algorithm. This was an institutional review board-approved retrospective study with waiver of informed consent. A cell-based contour grouping (CBCG) segmentation algorithm was used to delineate the lesion boundaries automatically. Seven morphologic features were extracted. The classifier was a logistic regression function. Five hundred twenty breast US scans were obtained from 520 subjects (age range, 15-89 years), including 275 benign (mean size, 15 mm; range, 5-35 mm) and 245 malignant (mean size, 18 mm; range, 8-29 mm) lesions. The newly developed computer-aided diagnostic algorithm was evaluated on the basis of boundary quality and differentiation performance. The segmentation algorithms and features in two conventional computer-aided diagnostic algorithms were used for comparative study. The CBCG-generated boundaries were shown to be comparable with the manually delineated boundaries. The area under the receiver operating characteristic curve (AUC) and differentiation accuracy were 0.968 +/- 0.010 and 93.1% +/- 0.7, respectively, for all 520 breast lesions. At the 5% significance level, the newly developed algorithm was shown to be superior to the use of the boundaries and features of the two conventional computer-aided diagnostic algorithms in terms of AUC (0.974 +/- 0.007 versus 0.890 +/- 0.008 and 0.788 +/- 0.024, respectively). The newly developed computer-aided diagnostic algorithm that used a CBCG segmentation method to measure boundaries achieved a high differentiation performance.