BI-RADS 3 on Screening Breast Ultrasound: What Is It and What Is the Appropriate Management?

Baseline Performance of Ultrasound-Based Strategies in Breast Cancer Screening Among Chinese Women

RATIONALE AND OBJECTIVE

There is a notable absence of robust evidence on the efficacy of ultrasound-based breast cancer screening strategies, particularly in populations with a high prevalence of dense breasts. Our study addresses this gap by evaluating the effectiveness of such strategies in Chinese women, thereby enriching the evidence base for identifying the most efficacious screening approaches for women with dense breast tissue.

METHODS

Conducted from October 2018 to August 2022 in Central China, this prospective cohort study enrolled 8996 women aged 35-64 years, divided into two age groups (35-44 and 45-64 years). Participants were screened for breast cancer using hand-held ultrasound (HHUS) and automated breast ultrasound system (ABUS), with the older age group also receiving full-field digital mammography (FFDM). The Breast Imaging Reporting and Data System (BI-RADS) was employed for image interpretation, with abnormal results indicated by BI-RADS 4/5, necessitating a biopsy; BI-RADS 3 required follow-up within 6-12 months by primary screening strategies; and BI-RADS 1/2 were classified as negative.

RESULTS

Among the screened women, 29 cases of breast cancer were identified, with 4 (1.3‰) in the 35-44 years age group and 25 (4.2‰) in the 45-64 years age group. In the younger age group, HHUS and ABUS performed equally well, with no significant difference in their AUC values (0.8678 vs. 0.8679, P > 0.05). For the older age group, ABUS as a standalone strategy (AUC 0.9935) and both supplemental screening methods (HHUS with FFDM, AUC 0.9920; ABUS with FFDM, AUC 0.9928) outperformed FFDM alone (AUC 0.8983, P < 0.05). However, there was no significant difference between HHUS alone and FFDM alone (AUC 0.9529 vs. 0.8983, P > 0.05).

CONCLUSION

The findings indicate that both HHUS and ABUS exhibit strong performance as independent breast cancer screening strategies, with ABUS demonstrating superior potential. However, the integration of FFDM with these ultrasound techniques did not confer a substantial improvement in the overall effectiveness of the screening process.

Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation

Purpose

Breast ultrasound suffers from low positive predictive value and specificity. Artificial intelligence (AI) proposes to improve accuracy, reduce false negatives, reduce inter- and intra-observer variability and decrease the rate of benign biopsies. Perpetuating racial/ethnic disparities in healthcare and patient outcome is a potential risk when incorporating AI-based models into clinical practice; therefore, it is necessary to validate its non-bias before clinical use.

Approach

Our retrospective review assesses whether our AI decision support (DS) system demonstrates racial/ethnic bias by evaluating its performance on 1810 biopsy proven cases from nine breast imaging facilities within our health system from January 1, 2018 to October 28, 2021. Patient age, gender, race/ethnicity, AI DS output, and pathology results were obtained.

Results

Significant differences in breast pathology incidence were seen across different racial and ethnic groups. Stratified analysis showed that the difference in output by our AI DS system was due to underlying differences in pathology incidence for our specific cohort and did not demonstrate statistically significant bias in output among race/ethnic groups, suggesting similar effectiveness of our AI DS system among different races (p > 0.05 for all).

Conclusions

Our study shows promise that an AI DS system may serve as a valuable second opinion in the detection of breast cancer on diagnostic ultrasound without significant racial or ethnic bias. AI tools are not meant to replace the radiologist, but rather to aid in screening and diagnosis without perpetuating racial/ethnic disparities.

Pearls and Pitfalls of Interpretation of Automated Breast US

Dense breast tissue is an independent risk factor for breast cancer and reduces the sensitivity of mammography. Patients with dense breast tissue are more likely to present with interval cancers and higher-stage disease. Successful breast cancer screening outcomes rely on detection of early-stage breast cancers; therefore, several supplemental screening modalities have been developed to improve cancer detection in dense breast tissue. US is the most widely used supplemental screening modality worldwide and has been proven to demonstrate additional mammographically occult cancers that are predominantly invasive and node negative. According to the American College of Radiology, intermediate-risk women with dense breast tissue may benefit from adjunctive screening US due to the limitations of mammography. Several studies have demonstrated handheld US (HHUS) and automated breast US (AUS) to be comparable in the screening setting. The advantages of AUS over HHUS include lack of operator dependence and a formal training requirement, image reproducibility, and ability for temporal comparison. However, AUS exhibits unique features that can result in high false-positive rates and long interpretation times for new users. Familiarity with the common appearance of benign mammographic findings and artifacts, technical challenges, and unique AUS features is essential for fast, efficient, and accurate interpretation. The goals of this article are to (a) examine the role of AUS as a supplemental screening modality and (b) review the pearls and pitfalls of AUS interpretation. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Attitudes of Practicing Radiologists Toward the Management of Palpable Circumscribed Breast Masses

OBJECTIVE

To investigate the attitudes of radiologists toward palpable breast masses with benign features on US and to determine the factors influencing their decision.

METHODS

A 20-question online questionnaire was sent to radiologists with membership of the Iranian Society of Radiology and included questions regarding demographics, practice experience, and management approach to palpable circumscribed breast masses based on patient age and risk factors. Radiologists' management choice for masses in themselves or close relatives/friends was also queried.

RESULTS

In total, 151 radiologists participated (response rate 16%). For palpable breast masses with benign imaging features in women at high risk, the majority of radiologists selected MRI (95/151, 62.9%) and core-needle biopsy (110/151, 72.8%). In average-risk patients, radiologists with >5 years of practice experience selected biopsy more frequently (33/79, 41.8%) than less experienced radiologists (17/79, 23.6%) for patients ≥40 years old (P < 0.001) and patients <40 years old (20/79, 25.3%; 11/72, 15.3%, respectively) (P = 0.014). Similarly, selecting biopsy was more common in radiologists who completed a breast imaging fellowship for patients ≥40 years old (23/45, 51.1% vs 27/106, 25.5%) (P = 0.04), as well as for patients <40 years old (18/45, 40% vs 13/106, 12.3%) (P = 0.02). Radiologists who were <40 years old selected biopsy more frequently if evaluating a mass in themselves (22/86, 25.6%) compared to patients (15/86, 17.4%) (P < 0.001).

CONCLUSION

Radiologist experience and educational background, as well as patient baseline breast cancer risk, can predispose radiologists to choose biopsy for palpable breast masses despite a benign appearance on imaging.

Screening Outcomes of Supplemental Automated Breast US in Asian Women with Dense and Nondense Breasts

Background Automated breast (AB) US effectively depicts mammographically occult breast cancers in Western women. However, few studies have focused on the outcome of supplemental AB US in Asian women who have denser breasts than Western women. Purpose To evaluate the performance of supplemental AB US on mammography-based breast cancer screening in Asian women with dense breasts and those with nondense breasts. Materials and Methods A retrospective database search identified asymptomatic Korean women who underwent digital mammography (DM) and supplemental AB US screening for breast cancer between January 2018 and December 2019. We excluded women without sufficient follow-up, established final diagnosis, or histopathologic results. Performance measures of DM alone and AB US combined with DM (hereafter AB US plus DM) were compared. The primary outcome was cancer detection rate (CDR), and the secondary outcomes were sensitivity and specificity. Subgroup analyses were performed based on mammography density. Results From 2785 screening examinations in 2301 women (mean age, 52 years ± 9 [SD]), 28 cancers were diagnosed (26 screening-detected cancers, two interval cancers). When compared with DM alone, AB US plus DM resulted in a higher CDR of 9.3 per 1000 examinations (95% CI: 7.7, 10.3) versus 6.5 per 1000 examinations (95% CI: 5.2, 7.2; P < .001) and a higher sensitivity of 90.9% (95% CI: 77.3, 100.0) versus 63.6% (95% CI: 40.9, 81.8; P < .001) but a lower specificity of 86.8% (95% CI: 85.2, 88.2) versus 94.6% (95% CI: 93.6, 95.5; P < .001) in women with dense breasts. In women with nondense breasts, AB US plus DM resulted in a higher CDR of 9.5 per 1000 examinations (95% CI: 7.1, 10.6) versus 6.3 per 1000 examinations (95% CI: 3.5, 7.1; P < .001), whereas specificity was lower at 95.2% (95% CI: 93.4, 96.8) versus 97.1% (95% CI: 95.8, 98.4; P < .001). Conclusion In Asian women, the addition of automated breast US to digital mammography showed higher cancer detection rates but lower specificities in both dense and nondense breasts. © RSNA, 2023 Supplemental material is available for this article.

Radiologic-Pathologic Correlation of Fibroepithelial Lesions: A Pictorial Review

Fibroepithelial lesions (FEL) of the breast encompass a spectrum of masses ranging from benign to malignant. Although these lesions are on the same biologic spectrum, differences in their clinical behaviors necessitate different management approaches. While imaging features are nonspecific, small size (less than 3 cm), oval shape, circumscribed margins, growth in diameter less than 20% in six months, and homogeneous echotexture on US favor fibroadenoma (FA). Conversely, larger size (3 cm or larger), rapid growth, irregular shape, noncircumscribed margins, and heterogeneous echotexture suggest possible phyllodes tumor (PT). Histopathologically, increased stromal cellularity, stromal atypia, and mitotic activity characterize PT, while FA typically lack these features. In this review, we summarize the imaging and pathology characteristics of nonmalignant FEL, including simple, juvenile, and complex FA, and benign and borderline PT and highlight the collaborative role of radiologists and pathologists in informing diagnosis and clinical management.

Establishment of a deep-learning system to diagnose BI-RADS4a or higher using breast ultrasound for clinical application

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI‐RADS) has become widespread worldwide, the problem of inter‐observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a system in which artificial intelligence (AI) can distinguish whether a static image obtained using a breast ultrasound represents BI‐RADS3 or lower or BI‐RADS4a or higher to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4028 images containing 5014 lesions and a test dataset consisting of 3166 images containing 3656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 91.2%, and 90.7%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Although deep‐learning methods to categorize benign and malignant tumors using breast ultrasound have been extensively reported, our work represents the first attempt to establish an AI system to classify BI‐RADS3 or lower and BI‐RADS4a or higher successfully, providing important implications for clinical actions. These results suggest that the AI diagnostic system is sufficient to proceed to the next stage of clinical application.