Detection and diagnosis of automated breast ultrasound in patients with BI-RADS category 4 microcalcifications: a retrospective study.

BACKGROUND: Automated Breast Ultrasound (AB US) has shown good application value and prospects in breast disease screening and diagnosis. The aim of the study was to explore the ability of AB US to detect and diagnose mammographically Breast Imaging Reporting and Data System (BI-RADS) category 4 microcalcifications. METHODS: 575 pathologically confirmed mammographically BI-RADS category 4 microcalcifications from January 2017 to June 2021 were included. All patients also completed AB US examinations. Based on the final pathological results, analyzed and summarized the AB US image features, and compared the evaluation results with mammography, to explore the detection and diagnostic ability of AB US for these suspicious microcalcifications. RESULTS: 250 were finally confirmed as malignant and 325 were benign. Mammographic findings including microcalcifications morphology (61/80 with amorphous, coarse heterogeneous and fine pleomorphic, 13/14 with fine-linear or branching), calcification distribution (189/346 with grouped, 40/67 with linear and segmental), associated features (70/96 with asymmetric shadow), higher BI-RADS category with 4B (88/120) and 4 C (73/38) showed higher incidence in malignant lesions, and were the independent factors associated with malignant microcalcifications. 477 (477/575, 83.0%) microcalcifications were detected by AB US, including 223 malignant and 254 benign, with a significantly higher detection rate for malignant lesions (x2 = 12.20, P < 0.001). Logistic regression analysis showed microcalcifications with architectural distortion (odds ratio [OR] = 0.30, P = 0.014), with amorphous, coarse heterogeneous and fine pleomorphic morphology (OR = 3.15, P = 0.037), grouped (OR = 1.90, P = 0.017), liner and segmental distribution (OR = 8.93, P = 0.004) were the independent factors which could affect the detectability of AB US for microcalcifications. In AB US, malignant calcification was more frequent in a mass (104/154) or intraductal (20/32), and with ductal changes (30/41) or architectural distortion (58/68), especially with the both (12/12). BI-RADS category results also showed that AB US had higher sensitivity to malignant calcification than mammography (64.8% vs. 46.8%). CONCLUSIONS: AB US has good detectability for mammographically BI-RADS category 4 microcalcifications, especially for malignant lesions. Malignant calcification is more common in a mass and intraductal in AB US, and tend to associated with architectural distortion or duct changes. Also, AB US has higher sensitivity than mammography to malignant microcalcification, which is expected to become an effective supplementary examination method for breast microcalcifications, especially in dense breasts.

Efficiency and impact factors of anatomical intelligence for breast and hand-held ultrasound in lesion detection.

PURPOSE: To investigate the efficiency and impact factors of anatomical intelligence for breast (AI-Breast) and hand-held ultrasound (HHUS) in lesion detection. METHODS: A total of 172 outpatient women were randomly selected, underwent AI-Breast ultrasound (Group AI) once and HHUS twice. HHUS was performed by breast imaging radiologists (Group A) and general radiologists (Group B). For the AI-Breast examination, a trained technician performed the whole-breast scan and data acquisition, while other general radiologists performed image interpretation. The examination time and lesion detection rate were recorded. The impact factors for breast lesion detection, including breast cup size, number of lesions, and benign or malignant lesions were analyzed. RESULTS: The detection rates of Group AI, A, and B were 92.8 ± 17.0%, 95.0 ± 13.6%, and 85.0 ± 22.9%, respectively. Comparable lesion detection rates were observed in Group AI and Group A (P > 0.05), but a significantly lower lesion detection rate was observed in Group B compared to the other two (both P < 0.05). Regarding missed diagnosis rates of malignant lesions, comparable performance was observed in Group AI, Group A, and Group B (8% vs. 4% vs. 14%, all P > 0.05). Scan times of Groups AI, A, and B were 262.15 ± 40.4 s, 237.5 ± 110.3 s, 281.2 ± 86.1 s, respectively. The scan time of Group AI was significantly higher than Group A (P < 0.01), but was slightly lower than Group B (P > 0.05). We found a strong linear correlation between scan time and cup size in Group AI (r = 0.745). No impacts of cup size and number of lesions were found on the lesion detection rate in Group AI (P > 0.05). CONCLUSIONS: With the assist of AI-Breast system, the lesion detection rate of AI-Breast ultrasound was comparable to that of a breast imaging radiologist and superior to that of the general radiologist. AI-Breast ultrasound may be used as a potential approach for breast lesions surveillance.

The hidden breast lesions: A case report of bilateral breast cancer.

Bilateral breast cancer (BBC) is rare and is associated with an unfavorable prognosis. Consequently it is crucial to improve diagnostic performance of breast cancer in the clinical setting. We report a case of BBC in a 66-year-old woman and describe the imaging findings, including mammography, hand-held ultrasound, automated breast ultrasound, anatomical intelligence for breast ultrasound (AI-breast), and magnetic resonance imaging. Only AI-breast ultrasound successfully located the two tumors, while other imaging examinations failed to detect the tumor in the right breast.

Diagnostic Performance of Automated Breast Ultrasound in Differentiating Benign and Malignant Breast Masses in Asymptomatic Women: A Comparison Study With Handheld Ultrasound.

OBJECTIVES: Our aim was to investigate the diagnostic potential of an automated breast ultrasound (ABUS) system in differentiating benign and malignant breast masses compared with handheld ultrasound (HHUS). METHODS: Women were randomly and proportionally selected from outpatients and underwent both HHUS and ABUS examinations. Masses with final American College of Radiology Breast Imaging Reporting and Data System categories 2 and 3 were considered benign. Masses with final Breast Imaging Reporting and Data System categories 4 and 5 were considered malignant. The diagnosis was confirmed by pathologic results or at least a 1-year follow-up. Automated breast US and HHUS were compared on the basis of their sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Diagnostic consistency and areas under the receiver operating characteristic curves were analyzed. The maximum diameters of masses were compared among HHUS, ABUS, and pathologic results. RESULTS: A total of 599 masses in 398 women were confirmed by pathologic results or at least a 1-year follow-up; 103 of 599 masses were malignant, and 496 were benign. There were no significant differences between ABUS and HHUS in terms of diagnostic accuracy (80.1% versus 80.6%), specificity (77.62% versus 80.24%), positive predictive value (46.12% versus 46.46%), and negative predictive value (97.96% versus 95.67%). There were significant differences in sensitivity (92.23% versus 82.52%; P < .01) and areas under the curve (0.85 versus 0.81; P < .05) between ABUS and HHUS. The correlation of the maximum diameter was slightly higher between ABUS and pathologic results (r = 0.885) than between HHUS and pathologic results (r = 0.855), but the difference was not significant (P > .05). CONCLUSIONS: Automated breast US is better than HHUS in differentiating benign and malignant breast masses, especially with respect to specificity.