Automated breast volume scanning versus conventional ultrasound in breast cancer screening

Assessing Diagnostic Performance Using the Transverse Plane Plus Coronal Plane Compared With the Transverse Plane Alone in Screening Automated Breast Ultrasound

OBJECTIVES

The coronal plane is the unique display mode of automated breast (AB) ultrasound (US), which has valuable features of showing the entire breast anatomy and providing additional diagnostic value for breast lesions. However, whether adding the coronal plane could improve the diagnostic performance in screening breast cancer remains uncertain. This study aimed to evaluate the value of adding the coronal plane in interpretation for AB US screening.

METHODS

In this retrospective study, AB US images from 644 women (396 in the no-finding group, 143 with benign lesions, and 105 with malignant lesions) aged 40-70 years were collected between January 2016 and October 2020. Four novice radiologists (with 1-5 years of experience with breast US) and four experienced radiologists (with >5 years of experience with breast US) were assigned to read all AB US images in the transverse plane plus coronal plane (T + C planes) and transverse plane (T plane) alone in separate reading sessions. Diagnostic performance, lesion conspicuity, and reading time were compared using analysis of variance.

RESULTS

The mean reading time of all radiologists was significantly shorter in the T + C planes reading mode than in the T plane alone (115 ± 32 vs 128 ± 31 s, respectively; P < .05), and cancers had a higher conspicuity (odds ratio, 1.76; 95% confidence interval [CI], 1.00-3.08; P = .04). No significant differences were noted in the two reading modes (T + C planes vs T plane) in the sensitivity (82% [95% CI, 74-89%] vs 81% [95% CI, 74-88%], respectively; P = .68) and specificity (68% [95% CI, 62-75%] vs 70% [95% CI, 64-75%], respectively; P = .39) when Breast Imaging-Reporting and Data System (BI-RADS) 3 was set as the threshold. There were also no significant differences in the two reading modes (T + C planes vs T plane) in the sensitivity (70% [95% CI, 64-76%] vs 69% [95% CI, 63-75%], respectively; P = .39) and specificity (91% [95% CI, 87-96%] vs 91% [95% CI, 88-95%], respectively; P = .90) when BI-RADS 4 was set as the threshold. In addition, the mean areas under the receiver operating characteristic curves of all radiologists in the two reading modes (T + C planes vs T plane) were not significantly different (0.84 [95% CI, 0.79-0.89] vs 0.83 [95% CI, 0.78-0.89], respectively; P = .61).

CONCLUSIONS

Adding a coronal plane in the AB US screening setting saved the reading time and improved the conspicuity of breast cancers but not the diagnostic performance.

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection Software for Automated Breast Ultrasound

RATIONALE AND OBJECTIVES

This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS).

MATERIALS AND METHODS

ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared.

RESULTS

For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing.

CONCLUSION

AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.

A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer

Objective

To compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.

Methods

Published studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan's nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.

Results

A total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73-0.95)], specificity was 0.93 [95% CI (0.82-0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94-0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84-0.91)], specificity was 0.76 [95% CI (0.66-0.84)], AUC was 0.89 [95% CI (0.86-0.92)] and DOR was 24. The Deeks' funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.

Conclusion

Compared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.

Pros and Cons for Automated Breast Ultrasound (ABUS): A Narrative Review

Automated breast ultrasound (ABUS) is an ultrasound technique that tends to be increasingly used as a supplementary technique in the evaluation of patients with dense glandular breasts. Patients with dense breasts have an increased risk of developing breast cancer compared to patients with fatty breasts. Furthermore, for this group of patients, mammography has a low sensitivity in detecting breast cancers, especially if it is not associated with architectural distortion or calcifications. ABUS is a standardized examination with many advantages in both screening and diagnostic settings: it increases the detection rate of breast cancer, improves the workflow, and reduces the examination time. On the other hand, like any imaging technique, ABUS has disadvantages and even some limitations. Many disadvantages can be diminished by additional attention and training. Disadvantages regarding image acquisition are the inability to assess the axilla, the vascularization, and the elasticity of a lesion, while concerning the interpretation, the disadvantages are the artifacts due to poor positioning, lack of contact, motion or lesion related. This article reviews and discusses the indications, the advantages, and disadvantages of the method and also the sources of error in the ABUS examination.

Automated whole breast segmentation for hand-held ultrasound with position information: Application to breast density estimation

BACKGROUND AND OBJECTIVE

Women with higher breast densities have a relatively higher risk to be diagnosed with breast cancer. Hand-held ultrasound (HHUS) can provide precise screening results and detect masses in dense breasts. However, its lack of position information and automatic extraction of breast area hinder the implementation of density estimation. To facilitate reliable breast density evaluation, this study proposed an upgraded version of our whole-breast ultrasound (WBUS) system, which not only can provide precise position information, but also can extract precise breast area automatically based on deep learning method.

METHODS

WBUS images with probe position information were collected from 117 women. For each case, an automatic breast region segmentation by DeepResUnet was conducted, then fibroglandular tissues were extracted from breast region using fuzzy c-mean (FCM) classifier. Finally, the percentage of breast density and breast area of the DeepResUnet predicted region and the breast region of the ground truth were calculated and compared.

RESULTS

The average and standard deviation of each breast case for DeepResUnet predicted breast region of 10-fold in Accuracy (ACC) was 0.963±0.054. Sensitivity (SENS) was 0.928±0.11. Specificity (SPEC) was 0.967±0.054. Dice coefficient (Dice) was 0.916±0.98. Region intersection over union (IoU) was 0.856±0.134. Significant and very high correlations of breast density, fibroglandular tissue area and breast area (R = 0.843, R= 0.822 and R = 0.984, all p values < 0.001) were found between the ground truth and the result of the proposed method for ultrasound images.

CONCLUSIONS

Breast density, fibroglandular tissue, and breast volume evaluated based on the proposed method and WBUS system have significant correlations with ground truth, indicating that the proposed method and WBUS system has the potential to be an alternative modality for breast screening and density estimation in clinical use.

Automated Breast Ultrasound: Interobserver Agreement, Diagnostic Value, and Associated Clinical Factors of Coronal-Plane Image Features

Objective

To evaluate the interobserver agreement, diagnostic value, and associated clinical factors of automated breast ultrasound (ABUS) coronal features in differentiating breast lesions.

Materials and Methods

This study enrolled 457 pathologically confirmed lesions in 387 female (age, 46.4 ± 10.3 years), including 377 masses and 80 non-mass lesions (NMLs). The unique coronal features, including retraction phenomenon, hyper- or hypoechoic rim (continuous or discontinuous), skipping sign, and white wall sign, were defined and recorded. The interobserver agreement on image type and coronal features was evaluated. Furthermore, clinical factors, including the lesion size, distance to the nipple or skin, palpability, and the histological grade were analyzed.

Results

Among the 457 lesions, 296 were malignant and 161 were benign. The overall interobserver agreement for image type and all coronal features was moderate to good. For masses, the retraction phenomenon was significantly associated with malignancies (p < 0.001) and more frequently presented in small and superficial invasive carcinomas with a low histological grade (p = 0.027, 0.002, and < 0.001, respectively). Furthermore, continuous hyper- or hypoechoic rims were predictive of benign masses (p < 0.001), whereas discontinuous rims were predictive of malignancies (p < 0.001). A hyperechoic rim was more commonly detected in masses more distant from the nipple (p = 0.027), and a hypoechoic rim was more frequently found in large superficial masses (p < 0.001 for both). For NMLs, the skipping sign was a predictor of malignancies (p = 0.040).

Conclusion

The coronal plane of ABUS may provide useful diagnostic value for breast lesions.

Preliminary study of the technical limitations of automated breast ultrasound: from procedure to diagnosis

Objective

To evaluate the main technical limitations of automated breast ultrasound and to determine the proportion of examinations excluded.

Materials and Methods

We evaluated 440 automated breast ultrasound examinations performed, over a 12-month period, by technicians using an established protocol.

Results

In five cases (1.1%), the examination was deemed unacceptable for diagnostic purposes, those examinations therefore being excluded.

Conclusion

Automated breast ultrasound is expected to overcome some of the major limitations of conventional ultrasound in breast cancer screening. In Brazil, this new method can be accepted for inclusion in routine clinical practice only after its advantages have been validated in the national context.

Automatic breast ultrasound: state of the art and future perspectives

The three-dimensional automated breast ultrasound system (3D ABUS) is a new device which represents a huge innovation in the breast ultrasound field, with several application scenarios of great interest.

ABUS's aim is to solve some of the main defects of traditional ultrasound, such as lack of standardization, high level of skill non-reproducibility, small field of view and high commitment of physician time. ABUS has proven to be an excellent non-ionising alternative to other supplemental screening options for women with dense breast tissue; also, it has appeared to be very promising in daily clinical practice.

The purpose of this paper is to present a summary of current applications of ABUS, focusing on clinical applications and future perspectives as ABUS is particularly promising for studies involving artificial intelligence, radiomics and evaluation of breast molecular subtypes.

Screening Breast Ultrasound Using Handheld or Automated Technique in Women with Dense Breasts

In women with dense breasts (heterogeneously or extremely dense), adding screening ultrasound to mammography increases detection of node-negative invasive breast cancer. Similar incremental cancer detection rates averaging 2.1-2.7 per 1000 have been observed for physician- and technologist-performed handheld ultrasound (HHUS) and automated ultrasound (AUS). Adding screening ultrasound (US) for women with dense breasts significantly reduces interval cancer rates. Training is critical before interpreting examinations for both modalities, and a learning curve to achieve optimal performance has been observed. On average, about 3% of women will be recommended for biopsy on the prevalence round because of screening US, with a wide range of 2%-30% malignancy rates for suspicious findings seen only on US. Breast Imaging Reporting and Data System 3 lesions identified only on screening HHUS can be safely followed at 1 year rather than 6 months. Computer-aided detection and diagnosis software can augment performance of AUS and HHUS; ongoing research on machine learning and deep learning algorithms will likely improve outcomes and workflow with screening US.

Automated breast ultrasound (ABUS) as a screening tool: initial experience

Background

Breast cancer is a major health problem, being the most common cancer in women. Early detection of breast cancer aims to the reduction of mortality and morbidity rates. Conventional screening methods include mammography and ultrasonography; however, both modalities have their limitations. Automated breast ultrasound (ABUS) is a recent technological advancement in the field of breast imaging having the benefit of standardization of the scans and lack of operator dependence as in conventional handheld ultrasound scans. The aim of this work was to report our initial experience of the added value of ABUS as a breast screening tool. The study included 200 patients who had screening mammograms, ultrasound, and ABUS.

Results

A significant difference was found between the number of lesions detected by ABUS and conventional ultrasound. A significant difference was found between lesions detected by ABUS and mammography which was most evident in patients with dense breasts.

Conclusions

ABUS is a valuable tool in the screening of the breast with improved lesion detection, especially in patients with dense breasts.

Clinical Application Value of Automated Breast Volume Scanner in the Diagnosis of Abdominal External Hernia

This study investigated the clinical application value of the automated breast volume scanner (ABVS) in patients with abdominal external hernias. A total of 121 abdominal external hernias from 103 inpatients were examined with both conventional handheld ultrasound (HHUS) and the ABVS before surgery. The rate of detection of abdominal external hernias and the diagnostic accuracy of classifying inguinal hernias using the two modalities were compared between junior and senior sonographers. Additionally, the sonographic appearance of abdominal external hernias with the ABVS system was evaluated. The results revealed that junior sonographers had a higher detection rate with the ABVS (92.6%) than with HHUS (83.5%). For the classification of inguinal hernias, the accuracy of junior sonographers using the ABVS was 89.2%, which was higher than that for the HHUS system (77.6%), but there was no significant difference between HHUS (92.7%) and the ABVS (93.1%) for senior sonographers. Measurement of the hernia orifice size with the ABVS was accurate. In addition, the ABVS could provide coronal images and visually revealed the contents of the hernia, the shape of the hernia orifice and the pathway of herniation, as well as the relationship with adjacent tissues. Therefore, the clinical application value of the ABVS for abdominal external hernias is excellent.

Automated Breast Ultrasonography (ABUS) in the Screening and Diagnostic Setting: Indications and Practical Use

Automated breast ultrasonography (ABUS) is a new imaging technology for automatic breast scanning through ultrasound. It was first developed to overcome the limitation of operator dependency and lack of standardization and reproducibility of handheld ultrasound. ABUS provides a three-dimensional representation of breast tissue and allows images reformatting in three planes, and the generated coronal plane has been suggested to improve diagnostic accuracy. This technique has been first used in the screening setting to improve breast cancer detection, especially in mammographically dense breasts. In recent years, numerous studies also evaluated its use in the diagnostic setting: they showed its suitability for breast cancer staging, evaluation of tumor response to neoadjuvant chemotherapy, and second-look ultrasound after magnetic resonance imaging. The purpose of this article is to provide a comprehensive review of the current body of literature about the clinical performance of ABUS, summarize available evidence, and identify gaps in knowledge for future research.

Prospective Study Comparing Two Second-Look Ultrasound Techniques: Handheld Ultrasound and an Automated Breast Volume Scanner

OBJECTIVES

The aim of this study was to compare the diagnostic performance of handheld ultrasound (US) and an automated breast volume scanner (ABVS) as second-look US techniques subsequent to preoperative breast magnetic resonance imaging (MRI).

METHODS

We prospectively enrolled patients with breast cancer who underwent handheld US and ABVS examinations as second-look US modalities for additional suspicious lesions found via preoperative breast MRI. We reviewed each second-look US modality independently and evaluated the detection rate of each modality. We then analyzed the correlation between the detection rate and the MRI factors (size, distance, and enhancement type).

RESULTS

From March to September 2014, both types of second-look US examinations were performed on 40 patients with breast cancer who had 76 additional suspicious lesions detected via preoperative breast MRI. The detection rate of the ABVS was higher than that of handheld US for the second-look examination (94.7% versus 86.8%; P< .05). Among the 76 total lesions, 7 were only identified by the ABVS, 1 was only found by handheld US, and 3 were not detected by either the ABVS or handheld US. When we analyzed the correlation between the detection rate and MRI factors, the only meaningful factor was the enhancement type. The ability to detect a nonmass lesion was lower than the ability to detect a mass-type lesion (P < 0.05) for both the ABVS and handheld US.

CONCLUSIONS

For a second-look US examination subsequent to preoperative breast MRI in patients with breast cancer, the ABVS is a more efficient modality than handheld US for preoperative evaluations. However, both techniques have limitations in detecting nonmass lesions.

Reducing Costs of Breast Examination: Ultrasound Performance and Inter-Observer Variability of Expert Radiologists Versus Residents

AIM

To compare efficiency levels between radiologist and radiology resident and any significant or clinically relevant differences in breast ultrasound diagnosis, thus reducing extra costs.

MATERIAL AND METHODS

100 patients attending for breast ultrasound were included. Each patient was examined by a radiologist, and subsequently by a resident of the radiology department. Both operators noted their findings and wrote a concluding report. Reports were compared for histological and biological analysis.

RESULTS

100 female patients with a mean age about 49 years were examined. The proportions of correct diagnoses of lesions individuated by radiologist and resident were 26.90 > 13.71% (p-value = 10.7), i.e. the radiologist was more accurate in comparison to resident in the individuation of breast lesions.

CONCLUSIONS

The radiologist was more accurate in comparison to radiology resident in the evaluation of breast pathology in ultrasonography diagnoses, and this could reduce cost and/or in-depth analysis.

Recommendations for screening and early detection of common cancers in India

Cancers of the breast, uterine cervix, and lip or oral cavity are three of the most common malignancies in India. Together, they account for about 34% of more than 1 million individuals diagnosed with cancer in India each year. At each of these cancer sites, tumours are detectable at early stages when they are most likely to be cured with standard treatment protocols. Recognising the key role that effective early detection and screening programmes could have in reducing the cancer burden, the Indian Institute for Cytology and Preventive Oncology, in collaboration with the US National Cancer Institute Center for Global Health, held a workshop to summarise feasible options and relevant evidence for screening and early detection of common cancers in India. The evidence-based recommendations provided in this Review are intended to act as a guide for policy makers, clinicians, and public health practitioners who are developing and implementing strategies in cancer control for the three most common cancers in India.