title: Automated Breast Volume Scanning: Identifying 3-D Coronal Plane Imaging Features May Help Categorize Complex Cysts.
abstract: The study described here sought to identify specific ultrasound (US) automated breast volume scanning (ABVS) features that distinguish benign from malignant lesions. Medical records of 750 patients with 792 breast lesions were retrospectively reviewed. Of the 750 patients, 101 with 122 cystic lesions were included in this study, and the results ABVS results were compared with biopsy pathology results. These lesions were classified into six categories based on ABVS sonographic features: type I = simple cyst; type II = clustered cyst; type III = cystic masses with thin septa; type IV = complex cyst; type V = predominantly cystic masses; and type VI = predominantly solid masses. Comparisons were conducted between the ABVS coronal plane features of the lesions and histopathology results, and the positive predictive value (PPV) was calculated for each feature. Of the 122 lesions, 90 (73.8%) were classified as benign, and 32 (26.2%) were classified as malignant. The sensitivity, specificity and accuracy associated with ABVS features for cystic lesions were 78.1%, 74.4% and 75.4%, respectively. The 11 cases (8.9%) of type I-IV cysts were all benign. Of the 22 (18.0%) type V cysts, 16 (13.1%) were benign and 6 (4.9%) were malignant. Of the 89 (72.9%) type VI cysts, 63 (51.7%) were benign and 26 (21.3%) were malignant. The typical symptoms of malignancy on ABVS include retraction (PPV = 100%, p < 0.05), hyper-echoic halos (PPV = 85.7%, p < 0.05), microcalcification (PPV = 66.7%, p < 0.05), thick walls or thick septa (PPV = 62.5%, p < 0.05), irregular shape (PPV: 51.2%, p < 0.05), indistinct margin (PPV: 48.6%, p < 0.05) and predominantly solid masses with eccentric cystic foci (PPV = 46.8%, p < 0.05). ABVS can reveal sonographic features of the lesions along the coronal plane, which may be of benefit in the detection of malignant, predominantly cystic masses and provide high clinical values.
authors.affiliation: Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Hong-Yan
authors.initials: HY
authors.lastname: Wang
authors.affiliation: Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China. Electronic address: yuxinjiangxh@163.com.
authors.forename: Yu-Xin
authors.initials: YX
authors.lastname: Jiang
authors.affiliation: Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Qing-Li
authors.initials: QL
authors.lastname: Zhu
authors.affiliation: Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Meng-Su
authors.initials: MS
authors.lastname: Xiao
authors.affiliation: Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: He
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Dai
authors.affiliation: Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Jian-Chu
authors.initials: JC
authors.lastname: Li
authors.affiliation: Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Sun
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D047688:Breast Cyst / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008875:Middle Aged; D056888:Patient Positioning / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*; D055815:Young Adult
keywords: 3-D; Automated breast volume scan; Benign or malignant; Breast neoplasm; Cystic breast lesion; Sonography