RATIONALE AND OBJECTIVES: To investigate the potential usefulness of computer-aided diagnosis as a tool for radiologists in the characterization and classification of mass lesions on ultrasound. MATERIALS AND METHODS: Previously, a computerized method for the automatic classification of breast lesions on ultrasound was developed. The computerized method includes automatic segmentation of the lesion from the ultrasound image background and automatic extraction of four features related to lesion shape, margin, texture, and posterior acoustic behavior. In this study, the effectiveness of the computer output as an aid to radiologists in their ability to distinguish between malignant and benign lesions, and in their patient management decisions in terms of biopsy recommendation are evaluated. Six expert mammographers and six radiologists in private practice at an institution accredited by the American Ultrasound Institute of Medicine participated in the study. Each observer first interpreted 25 training cases with feedback of biopsy results, and then interpreted 110 additional ultrasound cases without feedback. Simulating an actual clinical setting, the 110 cases were unknown to both the observers and the computer. During interpretation, observers gave their confidence that the lesion was malignant and also their patient management recommendation (biopsy or follow-up). The computer output was then displayed, and observers again gave their confidence that the lesion was malignant and theirpatient management recommendation. Statistical analyses included receiver operator characteristic analysis and Student t-test. RESULTS: For the expert mammographers and for the community radiologists, the Az (area under the receiver operator characteristic curve) increased from 0.83 to 0.87 (P = .02) and from 0.80 to 0.84 (P = .04), respectively, when the computer aid was used in the interpretation of the ultrasound images. Also, the Az values for the community radiologists with aid and for the expert mammographers without aid are similar to the Az value for the computer alone (Az = 0.83). CONCLUSION: Computer analysis of ultrasound images of breast lesions has been shown to improve the diagnostic accuracy of radiologists in the task of distinguishing between malignant and benign breast lesions and in recommending cases for biopsy.
RATIONALE AND OBJECTIVES: To investigate the potential usefulness of computer-aided diagnosis as a tool for radiologists in the characterization and classification of mass lesions on ultrasound. MATERIALS AND METHODS: Previously, a computerized method for the automatic classification of breast lesions on ultrasound was developed. The computerized method includes automatic segmentation of the lesion from the ultrasound image background and automatic extraction of four features related to lesion shape, margin, texture, and posterior acoustic behavior. In this study, the effectiveness of the computer output as an aid to radiologists in their ability to distinguish between malignant and benign lesions, and in their patient management decisions in terms of biopsy recommendation are evaluated. Six expert mammographers and six radiologists in private practice at an institution accredited by the American Ultrasound Institute of Medicine participated in the study. Each observer first interpreted 25 training cases with feedback of biopsy results, and then interpreted 110 additional ultrasound cases without feedback. Simulating an actual clinical setting, the 110 cases were unknown to both the observers and the computer. During interpretation, observers gave their confidence that the lesion was malignant and also their patient management recommendation (biopsy or follow-up). The computer output was then displayed, and observers again gave their confidence that the lesion was malignant and theirpatient management recommendation. Statistical analyses included receiver operator characteristic analysis and Student t-test. RESULTS: For the expert mammographers and for the community radiologists, the Az (area under the receiver operator characteristic curve) increased from 0.83 to 0.87 (P = .02) and from 0.80 to 0.84 (P = .04), respectively, when the computer aid was used in the interpretation of the ultrasound images. Also, the Az values for the community radiologists with aid and for the expert mammographers without aid are similar to the Az value for the computer alone (Az = 0.83). CONCLUSION: Computer analysis of ultrasound images of breast lesions has been shown to improve the diagnostic accuracy of radiologists in the task of distinguishing between malignant and benign breast lesions and in recommending cases for biopsy.
Authors: Berkman Sahiner; Heang-Ping Chan; Marilyn A Roubidoux; Lubomir M Hadjiiski; Mark A Helvie; Chintana Paramagul; Janet Bailey; Alexis V Nees; Caroline Blane Journal: Radiology Date: 2007-01-23 Impact factor: 11.105
Authors: Nicholas P Gruszauskas; Karen Drukker; Maryellen L Giger; Ruey-Feng Chang; Charlene A Sennett; Woo Kyung Moon; Lorenzo L Pesce Journal: Radiology Date: 2009-10-28 Impact factor: 11.105
Authors: Anton S Becker; Michael Mueller; Elina Stoffel; Magda Marcon; Soleen Ghafoor; Andreas Boss Journal: Br J Radiol Date: 2018-01-10 Impact factor: 3.039