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Automated Breast Volume Scanning: Identifying 3-D Coronal Plane Imaging Features May Help Categorize Complex Cysts
Hong-Yan Wang1, Yu-Xin Jiang1, Qing-Li Zhu1
1Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Ultrasound in Medicine & Biology
|January 9, 2016
Summary
Automated breast volume scanning (ABVS) identifies specific ultrasound features to differentiate benign from malignant breast lesions. Key indicators of malignancy include retraction and hyper-echoic halos, aiding in diagnosis.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Distinguishing benign from malignant breast lesions is crucial for effective patient management.
- Automated Breast Volume Scanning (ABVS) offers a 3D ultrasound approach for breast lesion evaluation.
- Identifying specific ABVS sonographic features can improve diagnostic accuracy.
Purpose of the Study:
- To identify specific ultrasound (US) automated breast volume scanning (ABVS) features that differentiate benign from malignant breast lesions.
- To evaluate the diagnostic performance of ABVS in characterizing cystic breast masses.
- To correlate ABVS coronal plane features with histopathology results.
Main Methods:
- Retrospective review of medical records for 750 patients with 792 breast lesions.
- Inclusion of 101 patients with 122 cystic lesions for ABVS analysis.
- Classification of lesions into six types based on ABVS sonographic features and comparison with biopsy pathology results.
Main Results:
- Of 122 cystic lesions, 90 were benign and 32 malignant. ABVS sensitivity, specificity, and accuracy were 78.1%, 74.4%, and 75.4%, respectively.
- Type I-IV cysts (11 cases) were all benign. Type V cysts (22 cases) showed 16 benign and 6 malignant. Type VI cysts (89 cases) showed 63 benign and 26 malignant.
- Malignancy indicators on ABVS included retraction (PPV=100%), hyper-echoic halos (PPV=85.7%), microcalcification (PPV=66.7%), thick walls/septa (PPV=62.5%), irregular shape (PPV=51.2%), indistinct margin (PPV=48.6%), and predominantly solid masses with eccentric cystic foci (PPV=46.8%).
Conclusions:
- Automated Breast Volume Scanning (ABVS) can effectively identify sonographic features in the coronal plane to aid in distinguishing benign from malignant breast lesions.
- Specific ABVS features like retraction and hyper-echoic halos are strong indicators of malignancy in cystic breast masses.
- ABVS demonstrates significant clinical value in the detection of predominantly cystic masses, improving diagnostic capabilities.
