The objective of this research was to study the contrast enhancement patterns of the different molecular subtypes of breast cancer on contrast-enhanced ultrasound (CEUS) using both qualitative and quantitative parameters. This prospective study included females with a single breast mass which was histopathologically proven carcinoma. B mode ultrasound (USG) and CEUS were performed in all patients during baseline assessment. Qualitative CEUS assessment encompassed enhancement pattern, presence of fill-in and washout. Quantitative assessment included measurement of peak enhancement, time to peak; area under the curve and mean transit time. A p-value < 0.05 was considered statistically significant for differentiating the subtypes. The included thirty masses were categorised into two subtypes-triple negative breast cancer (TNBC) (36.7%) and non-TNBC (63.3%) subtypes. With B-mode USG, a statistically significant difference was observed between the two groups with respect to their shape and margins. TNBC lesions showed an oval shape, circumscribed margins and peripheral nodular enhancement on CEUS with the absence of fill-in even in the delayed phase (p-value - 0.04). The two subtypes did not significantly differ in terms of quantitative perfusion parameters. The various subtypes of breast cancer therefore possess distinct contrast enhancement patterns. CEUS potentially allows differentiation amongst these molecular subtypes that may aid in radiology-pathology (rad-path) correlation and follow up of the patients.

Background: Owing to its high sensitivity, as concluded in the Breast UltraSound Trial (BUST), targeted ultrasound (US) now seems a promising accurate stand-alone modality for diagnostic evaluation of breast complaints. This approach implies omission of bilateral digital breast tomosynthesis (DBT) in women with clearly benign US findings. Within BUST, radiologists started with US followed by DBT. This side-study investigates women\'s experiences with DBT, their main motivation to undergo diagnostic imaging, and their view on US as a stand-alone modality. Methods: A subset of BUST participants completed a questionnaire on their DBT experiences, reason for undergoing diagnostic assessment, and view on US-only diagnostics. Responses were analyzed with descriptive statistics and logistic regression analyses. Results: In total, 778 of 838 women (response rate 92.8%) were included (M = 47, SD = 11.16). Of them, 16.8% reported no burden of DBT, 33.5% slight burden, 31.0% moderate, and 12.7% severe burden. Furthermore, 13% reported no pain, 35.3% slight pain, 33.2% moderate, and 11.3% severe pain. Moreover, 88.3% indicated that the most important reason for breast assessment was explanation of their complaint and to rule out breast cancer, whereas 3.2% wanted to \"check\" both breasts. And 82.4% reported satisfaction with US only in case of a nonmalignancy. Conclusions: Our study shows that most women in the diagnostic setting experience at least slight-to-moderate DBT-related burden and pain, and that explanation for their symptoms is their main interest. Also, the majority report satisfaction with US only in case of nonmalignant findings. However, exploration of women\'s perspectives outside this study is needed as our participants all underwent both examinations.

Artificial intelligence breast ultrasound diagnostic system (AIBUS) has been introduced as an alternative approach for handheld ultrasound (HHUS), while their results in BI-RADS categorization has not been compared.
This pilot study was based on a screening program conducted from May 2020 to October 2020 in southeast China. All the participants who received both HHUS and AIBUS were included in the study (N = 344). The ultrasound videos after AIBUS scanning were independently watched by a senior radiologist and a junior radiologist. Agreement rate and weighted Kappa value were used to compare their results in BI-RADS categorization with HHUS.
The detection rate of breast nodules by HHUS was 14.83%, while the detection rates were 34.01% for AIBUS videos watched by a senior radiologist and 35.76% when watched by a junior radiologist. After AIBUS scanning, the weighted Kappa value for BI-RADS categorization between videos watched by senior radiologists and HHUS was 0.497 (p < 0.001) with an agreement rate of 78.8%, indicating its potential use in breast cancer screening. However, the Kappa value of AIBUS videos watched by junior radiologist was 0.39, when comparing to HHUS.
AIBUS breast scan can obtain relatively clear images and detect more breast nodules. The results of AIBUS scanning watched by senior radiologists are moderately consistent with HHUS and might be used in screening practice, especially in primary health care with limited numbers of radiologists.

BACKGROUND. Overlap in ultrasound features of benign and malignant breast masses yields high rates of false-positive interpretations and benign biopsy results. Optoacoustic imaging is an ultrasound-based functional imaging technique that can increase specificity. OBJECTIVE. The purpose of this study was to compare specificity at fixed sensitivity of ultrasound images alone and of fused ultrasound and optoacoustic images evaluated with machine learning-based decision support tool (DST) assistance. METHODS. This retrospective Reader-02 study included 480 patients (mean age, 49.9 years) with 480 breast masses (180 malignant, 300 benign) that had been classified as BI-RADS category 3-5 on the basis of conventional gray-scale ultrasound findings. The patients were selected by stratified random sampling from the earlier prospective 16-site Pioneer-01 study. For that study, masses were further evaluated by ultrasound alone followed by fused ultrasound and optoacoustic imaging between December 2012 and September 2015. For the current study, 15 readers independently reviewed the previously acquired images after training in optoacoustic imaging interpretation. Readers first assigned probability of malignancy (POM) on the basis of clinical history, mammographic findings, and conventional ultrasound findings. Readers then evaluated fused ultrasound and optoacoustic images, assigned scores for ultrasound and optoacoustic imaging features, and viewed a POM prediction score derived by a machine learning-based DST before issuing final POM. Individual and mean specificities at fixed sensitivity of 98% and partial AUC (pAUC) (95-100% sensitivity) were calculated. RESULTS. Averaged across all readers, specificity at fixed sensitivity of 98% was significantly higher for fused ultrasound and optoacoustic imaging with DST assistance than for ultrasound alone (47.2% vs 38.2%; p = .03). Across all readers, pAUC was higher (p < .001) for fused ultrasound and optoacoustic imaging with DST assistance (0.024 [95% CI, 0.023-0.026]) than for ultrasound alone (0.021 [95% CI, 0.019-0.022]). Better performance using fused ultrasound and optoacoustic imaging with DST assistance than using ultrasound alone was observed for 14 of 15 readers for specificity at fixed sensitivity and for 15 of 15 readers for pAUC. CONCLUSION. Fused ultrasound and optoacoustic imaging with DST assistance had significantly higher specificity at fixed sensitivity than did conventional ultrasound alone. CLINICAL IMPACT. Optoacoustic imaging, integrated with reader training and DST assistance, may help reduce the frequency of biopsy of benign breast masses.

Background: The high diagnostic performance of modern breast ultrasound (US) opens the possibility to shift toward targeted US as initial imaging test in women with breast complaints. This comparative cohort study investigates the effects of starting with US followed by digital breast tomosynthesis (DBT), as practiced in the breast ultrasound study (BUST), on women\'s health-related quality of life (QoL). Methods: Fifty BUST participants and 50 \"controls\" who underwent DBT and US in regular order filled out the EQ-5D-3L three times during their visit: BUST participants before US (T1), after US (T2), and after DBT (T3) and non-BUST participants before DBT (T1), after DBT (T2), and after US (T3). Changes in QoL from baseline to T2 and T3 were assessed using generalized least squares, also taking into account the effects of biopsy, age, and complaint type. Results: Participants\' mean age was 50.6 years (BUST: SD = 12.1, controls: SD = 11.5). At T2 the overall QoL was higher [t(102.9) = 2.4, p = 0.017] and anxiety levels were lower [t(98.7) = -2.4, p = 0.020] in BUST participants compared with controls. However, from T2 to T3 these effects equalize, resulting in similar performances in QoL and anxiety at T3, respectively [t(97.6) = -2.3, p = 0.023] and [t(97.2) = 3.1, p = 0.002]. Compared with BUST participants, controls show a clear decrease in pain after US [t(106.5) = -2.8, p = 0.006]. Women undergoing biopsy had lower QoL [t(167.1) = -2.4, p = 0.017] and pain [t(154.1) = -2.1, p = 0.038], and more anxiety [t(187.4) = 4.3, p = 0.000]. Conclusions: The results suggest that changing the radiological order by starting with US has a short-term positive effect on overall QoL, anxiety, and DBT pain experience in symptomatic women. Owing to its negative impact, biopsies should be performed cautiously. In conclusion, the moment of reassurance for women advances by reversing the radiological order according to the BUST, showing the high importance of human interaction in diagnostic care in addition to the clinical performance of imaging modalities.

BACKGROUND: Shear wave elastography (SWE) and strain elastography (SE) have shown promising potential in breast cancer diagnostics by evaluating the stiffness of a lesion. Combining these two techniques could further improve the diagnostic performance. We aimed to exploratorily define the cut-offs at which adding combined SWE and SE to B-mode breast ultrasound could help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3-4 lesions to reduce the number of unnecessary breast biopsies.
METHODS: We report the secondary results of a prospective, multicentre, international trial (NCT02638935). The trial enrolled 1288 women with BI-RADS 3 to 4c breast masses on conventional B-mode breast ultrasound. All patients underwent SWE and SE (index test) and histopathologic evaluation (reference standard). Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after recategorising with SWE and SE were the outcome measures.
RESULTS: On performing histopathologic evaluation, 368 of 1288 breast masses were malignant. Following the routine B-mode breast ultrasound assessment, 53.80% (495 of 920 patients) underwent an unnecessary biopsy. After recategorising BI-RADS 4a lesions (SWE cut-off ≥3.70 m/s, SE cut-off ≥1.0), 34.78% (320 of 920 patients) underwent an unnecessary biopsy corresponding to a 35.35% (320 versus 495) reduction of unnecessary biopsies. Malignancies in the new BI-RADS 3 cohort were missed in 1.96% (12 of 612 patients).
CONCLUSIONS: Adding combined SWE and SE to routine B-mode breast ultrasound to recategorise BI-RADS 4a patients could help reduce the number of unnecessary biopsies in breast diagnostics by about 35% while keeping the rate of undetected malignancies below the 2% ACR BI-RADS 3 definition.

OBJECTIVE: This prospective study explored the value of synchronous tele-ultrasound (US) to aid doctors inexperienced in US with breast US examinations.
METHODS: In total, 99 patients were enrolled. Two trainee doctors who were inexperienced in US (trainee A [TA] and trainee B [TB]) and one doctor who was an expert in US completed the US examinations sequentially. TA completed the US examinations independently, while TB was instructed by the expert using synchronous tele-US. Subsequently, the expert performed on-site US examinations in person. Separately, they selected the most clinically significant nodule as the target nodule. Consistency with the expert and image quality were compared between TA and TB to evaluate tele-US. Furthermore, TB and the patients evaluated tele-US through questionnaires.
RESULTS: TB demonstrated higher consistency with the expert in terms of target nodule selection than TA (93.3% vs. 63.3%, P<0.001). TB achieved good inter-observer agreement (>0.75) with the expert on five US features (5/9, 55.6%), while TA only did so for one (1/9, 11.1%) (P=0.046). TB\'s image quality was higher than TA\'s in gray value, time gain compensation, depth, color Doppler adjustment, and the visibility of key information (P=0.018, P<0.001, P<0.001, P=0.033, and P=0.006, respectively). The comprehensive assessment score was higher for TB than for TA (3.96±0.82 vs. 3.09±0.87, P<0.001). Tele-US was helpful in 69.7% of US examinations and had a training effect in 68.0%. Furthermore, 63.6% of patients accepted tele-US and 60.6% were willing to pay.
CONCLUSIONS: Tele-US can help doctors inexperienced in US to perform breast US examinations.

In this study we aimed to evaluate the operation times of ABUS by technologists during the learning time course and share the learning experience.
The first consequent 400 examinations after the installation of an ABUS unit in the breast clinic between August 2017 and December 2017 were included. Total examination time was measured for each procedure. The initial and final examination times during the learning period were compared. Data were analyzed with the Mann-Whitney Test.
The acquisition times for routine six position examination ranged between eight and 36 minutes with an average of 13.2 ± 3.58 min. The examination time for the eight position examination ranged between 18 and 32 min, with an average of 22.9 ± 3.93 min. The overall average examination time was 13.3 ± 3.98 min. There was a significant difference (p = 0.00) between the average initial and final examination times of the learning period with an average decrease of 10.6 min.
The average time of an ABUS examination for an average breast is less than 15 min. ABUS examination time reduced as technologists became familiar with the sonographic anatomy of the breast and experienced in positioning technique during the learning curve.

OBJECTIVE: Breast ultrasound has been widely used as an essential examination for diagnosing breast cancer. However, standardized diagnostic criteria are as yet lacking. This study aimed to develop a simple diagnostic flowchart for beginners learning breast ultrasonography. The diagnostic flowchart was developed based on the recall criteria widely used in Japan.
METHODS: We conducted a multicenter study to examine recall criteria usefulness in the diagnostic phase of breast disease. Women with ultrasound-visible breast masses who underwent B-mode breast ultrasound examination were recruited from 22 hospitals in Japan between September 2009 and January 2010. B-mode images were evaluated by members of the centralized image interpretation committee. We developed the new diagnostic flowchart based on the results. The usefulness of the diagnostic flowchart was assessed by employing datasets from the current study and another study which we conducted (BC-04 study).
RESULTS: We evaluated 1045 solid masses (malignant: 495, benign: 550). Multivariate analysis showed that shape, margin, echogenic halo, interruption of the mammary gland interface, and depth width ratio were significant findings for distinguishing between benign and malignant masses. We modified the recall criteria and developed our novel diagnostic flowchart using these findings. The sensitivity and specificity of the new flowchart (current study: 0.97, 0.45; BC-04 study dataset: 0.95, 0.45) were similar to those of experts (current study: 0.96, 0.54; BC-04 study dataset: 0.98, 0.38).
CONCLUSIONS: We developed a simple diagnostic flowchart for breast ultrasound. This flowchart is anticipated to be applicable to educating beginners learning breast ultrasound.

The automatic segmentation of breast tumors in ultrasound (BUS) has recently been addressed using convolutional neural networks (CNN). These CNN-based approaches generally modify a previously proposed CNN architecture or they design a new architecture using CNN ensembles. Although these methods have reported satisfactory results, the trained CNN architectures are often unavailable for reproducibility purposes. Moreover, these methods commonly learn from small BUS datasets with particular properties, which limits generalization in new cases. This paper evaluates four public CNN-based semantic segmentation models that were developed by the computer vision community, as follows: (1) Fully Convolutional Network (FCN) with AlexNet network, (2) U-Net network, (3) SegNet using VGG16 and VGG19 networks, and (4) DeepLabV3+ using ResNet18, ResNet50, MobileNet-V2, and Xception networks. By transfer learning, these CNNs are fine-tuned to segment BUS images in normal and tumoral pixels. The goal is to select a potential CNN-based segmentation model to be further used in computer-aided diagnosis (CAD) systems. The main significance of this study is the comparison of eight well-established CNN architectures using a more extensive BUS dataset than those used by approaches that are currently found in the literature. More than 3000 BUS images acquired from seven US machine models are used for training and validation. The F1-score (F1s) and the Intersection over Union (IoU) quantify the segmentation performance. The segmentation models based on SegNet and DeepLabV3+ obtain the best results with F1s>0.90 and IoU>0.81. In the case of U-Net, the segmentation performance is F1s=0.89 and IoU=0.80, whereas FCN-AlexNet attains the lowest results with F1s=0.84 and IoU=0.73. In particular, ResNet18 obtains F1s=0.905 and IoU=0.827 and requires less training time among SegNet and DeepLabV3+ networks. Hence, ResNet18 is a potential candidate for implementing fully automated end-to-end CAD systems. The CNN models generated in this study are available to researchers at https://github.com/wgomezf/CNN-BUS-segment, which attempts to impact the fair comparison with other CNN-based segmentation approaches for BUS images.