Training and standards for performance, interpretation, and structured reporting for supplemental breast cancer screening

False-positive incidental lesions detected on contrast-enhanced breast MRI: clinical and imaging features

PURPOSE

To identify demographic and imaging features of MRI-detected enhancing lesions without clinical, ultrasound, and mammographic correlation associated with false-positive outcomes, impacting patient care.

MATERIALS AND METHODS

A retrospective multi-institutional study of imaging studies and patient's chart review of consecutive women with MRI-detected enhancing lesions without clinical, mammogram, or ultrasound correlation between January and December 2018, who underwent MRI-guided biopsy. According to the BI-RADS lexicon, lesions' frequency and imaging features were recorded. The demographic and imaging characteristics variables were correlated with histopathology as the gold standard and an uneventful follow-up of at least one year. Univariate logistic regression analysis was used to explore the correlation between the baseline variables such as age, genetic mutation, family history of breast cancer, personal history of breast cancer, MRI indication, background parenchymal enhancement, and MRI characteristic of the lesion with the false-positive results in main data and subgroup analysis.

RESULTS

Two hundred nineteen women (median age 49 years; range 26-85 years) with 219 MRI-detected enhancing lesions that underwent MRI-guided vacuum-assisted biopsy during the study period fulfilled the study criteria and formed the study cohort. Out of 219, 180 lesions (82.2%) yielded benign pathology results, including 137 benign outcomes (76%) and 43 high-risk lesions (24%). Most demographic and imaging characteristics variables did not help to differentiate malignant from benign lesions. The variables that showed statistically significant association with true-positive results in univariate analyses were age (OR 1.05; 95% CI 1.02-1.08; p = 0.0015), irregular mass-lesion shape when compared with oval/round mass lesion (OR 11.2; 95% CI 1.6-78.4; p = 0.015), and clumped and clustered ring of enhancement when compared with homogeneous (OR 3.22, 95% CI 1.40-7.40; p = 0.0058). For participants with mass breast lesion, the hyperintense signal on the T2-weighted sequence (compared to the normal fibroglandular signal) was significantly related to the false-positive result (OR 0.13; 95% CI 0.02-0.76; p = 0.024).

CONCLUSION

Young patients, oval/round mass-lesion shape, and homogeneous pattern of non-mass enhancement showed the strongest association with false-positive results of enhancing lesions depicted by MRI. For participants with mass breast lesion, T2-bright mass lesion showed significant association with false-positive result. It may impact the patient's management with a suggestion of follow-up rather than interventional procedure when these demographic and imaging parameters are present, consequently decreasing the patient's anxiety and health care costs.

Deep Learning for Differentiation of Breast Masses Detected by Screening Ultrasound Elastography

Recently, deep learning using convolutional neural networks (CNNs) has yielded consistent results in image-pattern recognition. This study was aimed at investigating the effectiveness of deep learning using CNNs to differentiate benign and malignant breast masses identified by elastography on ultrasound screening. A data set of the elastography images of 245 breast masses (146 benign, 99 malignant) in 239 consecutive patients was retrospectively obtained. The data set was randomly split into training (55%), validation (25%) and test (20%) cohorts. A deep learning model predicting the probability of malignancy was constructed using GoogLeNet architectures (pre-trained by ImageNet) with 50 epochs. The model was then applied to the test data, and the results were compared with those obtained by evaluating the fat-to-lesion ratio (FLR) and by a 5-point visual color assessment (elasticity score). The receiver operating characteristic (ROC) curve was calculated to evaluate the performance of the model. The DeLong test was used to compare the areas under the ROC curve (AUCs). The CNN, FLR and elasticity score had a sensitivity of 0.800, 0.800 and 0.350; specificity of 0.966, 0.586 and 0.931; accuracy of 0.898, 0.673 and 0.694; positive predictive value of 0.941, 0.571 and 0.778; negative predictive value of 0.875, 0.810 and 0.675; and AUC of 0.895, 0.693 and 0.641, respectively. The AUC of the CNN was significantly higher than that of the FLR or elasticity score (p < 0.001). A CNN-based deep learning model for predicting benign or malignant breast masses revealed better diagnostic performance than did FLR or elasticity score-based estimations on ultrasound elastography. The CNN-based model also increased the positive predictive value from 57%-78% to 94%. Therefore, this model may reduce unnecessary biopsy recommendations for masses detected on breast ultrasound screening.

Opportunities in cancer imaging: risk-adapted breast imaging in screening

In the UK, women between 50-70 years are invited for 3-yearly mammography screening irrespective of their likelihood of developing breast cancer. The only risk adaption is for women with >30% lifetime risk who are offered annual magnetic resonance imaging (MRI) and mammography, and annual mammography for some moderate-risk women. Using questionnaires, breast density, and polygenic risk scores, it is possible to stratify the population into the lowest 20% risk, who will develop <4% of cancers and the top 4%, who will develop 18% of cancers. Mammography is a good screening test but has low sensitivity of 60% in the 9% of women with the highest category of breast density (BIRADS D) who have a 2.5- to fourfold breast cancer risk. There is evidence that adding ultrasound to the screening mammogram can increase the cancer detection rate and reduce advanced stage interval and next round cancers. Similarly, alternative tests such as contrast-enhanced mammography (CESM) or abbreviated MRI (ABB-MRI) are much more effective in detecting cancer in women with dense breasts. Scintimammography has been shown to be a viable alternative for dense breasts or for follow-up in those with a personal history of breast cancer and scarring as result of treatment. For supplemental screening to be worthwhile in these women, new technologies need to reduce the number of stage II cancers and be cost effective when tested in large scale trials. This article reviews the evidence for supplemental imaging and examines whether a risk-stratified approach is feasible.

Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

Purpose of Review

Automated breast ultrasound (ABUS) is a three-dimensional imaging technique, used as a supplemental screening tool in women with dense breasts. This review considers the technical aspects, pitfalls, and the use of ABUS in screening and clinical practice, together with new developments and future perspectives.

Recent Findings

ABUS has been approved in the USA and Europe as a screening tool for asymptomatic women with dense breasts in addition to mammography. Supplemental US screening has high sensitivity for cancer detection, especially early-stage invasive cancers, and reduces the frequency of interval cancers. ABUS has similar diagnostic performance to handheld ultrasound (HHUS) and is designed to overcome the drawbacks of operator dependence and poor reproducibility. Concerns with ABUS, like HHUS, include relatively high recall rates and lengthy reading time when compared to mammography. ABUS is a new technique with unique features; therefore, adequate training is required to improve detection and reduce false positives. Computer-aided detection may reduce reading times and improve cancer detection. Other potential applications of ABUS include local staging, treatment response evaluation, breast density assessment, and integration of radiomics.

Summary

ABUS provides an efficient, reproducible, and comprehensive supplemental imaging technique in breast screening. Developments with computer-aided detection may improve the sensitivity and specificity as well as radiologist confidence and reduce reading times, making this modality acceptable in large volume screening centers.

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.

Better use of available radiology resources for women's health in Latin America and the Caribbean

Despite the United Nations Millennium Development Goals and the 2030 Sustainable Development Goals, women in numerous countries still face many challenges in obtaining good-quality health care. For example, various nations in Latin America and the Caribbean (LAC) do not have access to complex radiology technologies. However, conventional radiography, ultrasound, mammography, and computed tomography are available and can be used to address such women's health concerns as breast and cervical cancers, postpartum bleeding, and tuberculosis. LAC countries face additional difficulties in radiology services with respect to quality human resources, quality assurance programs, standardization, and functioning of diagnostic imaging units. These deficiencies affect the quality of the services rendered. Appropriate measures must be implemented to produce quality services and quality images and to reduce adverse events. These steps will ensure better outcomes and consequently reduce mortality and morbidity.

Image quality and artifacts in automated breast ultrasonography

Three-dimensional automated breast ultrasonography (ABUS) has been approved for screening Epub ahead of print studies as an adjunct to mammography. ABUS provides proper orientation and documentation, resulting in better reproducibility. Optimal image quality is essential for a proper diagnosis, and high-quality images should be ensured when ABUS is used in clinical settings. Image quality in ABUS is highly dependent on the acquisition procedure. Artifacts can interfere with the visibility of abnormalities, reduce the overall image quality, and introduce clinical and technical problems. Nipple shadow and reverberation artifacts are some of the artifacts frequently encountered in ABUS. Radiologists should be familiar with proper image acquisition techniques and possible artifacts in order to acquire high-quality images.

Not all false positive diagnoses are equal: On the prognostic implications of false-positive diagnoses made in breast MRI versus in mammography / digital tomosynthesis screening

Background

Breast magnetic resonance imaging (MRI) has been reported to frequently result in false-positive diagnoses, limiting its positive predictive value (PPV). However, for PPV calculation, all nonmalignant tissue changes are equally considered false-positive, although the respective prognostic importance, and thus patient management implications, of different pathologies may well differ. We investigated the pathology of false-positive diagnoses made by MRI compared with radiographic (digital mammography/tomosynthesis [DM/DBT]) screening.

Methods

We conducted an institutional review board-approved prospective analysis of 710 consecutive asymptomatic women at average risk for breast cancer who underwent vacuum biopsy with or without surgical biopsy for screen-detected DM/DBT (n = 344) or MRI (n = 366) findings. We compared the frequency of false-positive biopsies (given by PPV3), as well as the types of nonmalignant tissue changes that caused the respective false-positive biopsies. In an order of increasing relative risk of subsequent breast cancer, pathologies of false-positive biopsies were categorized as nonproliferative, simple proliferative, complex proliferative, or atypical proliferative (including lobular carcinoma in situ/lobular intraepithelial neoplasia). The Mann-Whitney U test was used to compare distributions.

Results

Histology yielded nonmalignant tissue in 202 of 366 biopsies done for positive MRI studies and 195 of 344 biopsies for positive DM/DBT studies, respectively, yielding a similar PPV3 percentages of 44.8% (164 of 202) and 43.3% (149 of 202) for both methods. However, the distribution of tissue types that caused false-positive diagnoses differed significantly (p < 0.0001). On the basis of MRI, high-risk atypical proliferative changes (40.1%; 81 of 202) were most common, followed by complex proliferative changes (23.8%; 48 of 202). In DM/DBT, low-risk, nonproliferative changes were the dominant reason for false-positive diagnoses (49.7%; 97 of 195), followed by simple proliferative changes (25.2%; 51 of 195). Low-risk nonproliferative changes resulted in false-positive diagnoses based on MRI as infrequently as did high-risk atypical proliferative changes based on DM/DBT (18.8% [38 of 202] vs. 18.0% [35 of 195]). The likelihood of a false-positive diagnosis including atypias was twice as high in women undergoing biopsy for MRI findings (81 of 202; 40%) as for those with DM/DBT findings (35 of 195; 18%).

Conclusions

The prognostic importance, and thus the clinical implications, of false-positive diagnoses made on the basis of breast MRI vs. radiographic screening differed significantly, with a reversed prevalence of high- and low-risk lesions. This should be taken into account when discussing the rate of false-positive diagnoses (i.e., PPV levels of MRI vs. radiographic screening). Current benchmarks that rate the utility of breast cancer screening programs (i.e., cancer detection rates and PPVs) do not reflect these substantial biological differences and the different prognostic implications.

Enhancing Your Acoustics: Ultrasound Image Optimization of Breast Lesions

A general understanding of ultrasound physics relative to image optimization is essential when performing breast ultrasound examinations and in accurately interpreting images of the breast. Appropriate technique is crucial to achieve accurate lesion characterization and is emphasized in the second edition of the American College of Radiology Breast Imaging Reporting and Data System for ultrasound (2013). This pictorial essay reviews clinically relevant ultrasound physics to best characterize breast lesions for guiding the clinician to the proper diagnosis. Differences in chosen parameters on ultrasound imaging can create variability in the appearance of breast lesions.

Evaluation of Screening US-detected Breast Masses by Combined Use of Elastography and Color Doppler US with B-Mode US in Women with Dense Breasts: A Multicenter Prospective Study

Purpose To investigate the value of the combined use of elastography and color Doppler ultrasonography (US) with B-mode US for evaluation of screening US-detected breast masses in women with dense breasts. Materials and Methods This prospective, multicenter study included asymptomatic women with dense breasts who were referred for screening US between November 2013 and December 2014. Eligible women had a newly detected breast mass at conventional B-mode US screening, for which elastography and color Doppler US were performed. The following outcome measures were compared between B-mode US and the combination of B-mode US, elastography, and color Doppler US: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and the number of false-positive findings at screening US. Results Among 1021 breast masses (mean size, 1.0 cm; range, 0.3-3.0 cm) in 1021 women (median age, 45 years), 68 were malignant (56 invasive). Addition of elastography and color Doppler US to B-mode US increased the AUC from 0.87 (95% confidence interval [CI]: 0.82, 0.91) to 0.96 (95% CI: 0.95, 0.98; P < .001); specificity from 27.0% (95% CI: 24.2%, 29.9%) to 76.4% (95% CI: 73.6%, 79.1%; P < .001) without loss in sensitivity (95% CI: -1.5%, 1.5%; P > .999); and PPV from 8.9% (95% CI: 7.0%, 11.2%) to 23.2% (95% CI: 18.5%, 28.5%; P < .001), while avoiding 67.7% (471 of 696) of unnecessary biopsies for nonmalignant lesions. Conclusion Addition of elastography and color Doppler US to B-mode US can increase the PPV of screening US in women with dense breasts while reducing the number of false-positive findings without missing cancers. ^© RSNA, 2017 Online supplemental material is available for this article.

Ultrasound as the Primary Screening Test for Breast Cancer: Analysis From ACRIN 6666

BACKGROUND

Mammography is not widely available in all countries, and breast cancer incidence is increasing. We considered performance characteristics using ultrasound (US) instead of mammography to screen for breast cancer.

METHODS

Two thousand eight hundred nine participants were enrolled at 20 sites in the United States, Canada, and Argentina in American College of Radiology Imaging 6666. Two thousand six hundred sixty-two participants completed three annual screens (7473 examinations) with US and film-screen (n = 4351) or digital (n = 3122) mammography and had biopsy or 12-month follow-up. Cancer detection, recall, and positive predictive values were determined. All statistical tests were two-sided.

RESULTS

One hundred ten women had 111 breast cancer events: 89 (80.2%) invasive cancers, median size 12 mm. The number of US screens to detect one cancer was 129 (95% bootstrap confidence interval [CI] = 110 to 156), and for mammography 127 (95% CI = 109 to 152). Cancer detection was comparable for each of US and mammography at 58 of 111 (52.3%) vs 59 of 111 (53.2%, P = .90), with US-detected cancers more likely invasive (53/58, 91.4%, median size 12 mm, range = 2-40 mm), vs mammography at 41 of 59 (69.5%, median size 13 mm, range = 1-55 mm, P < .001). Invasive cancers detected by US were more frequently node-negative, 34 of 53 (64.2%) vs 18 of 41 (43.9%) by mammography (P = .003). For 4814 incidence screens (years 2 and 3), US had higher recall and biopsy rates and lower PPV of biopsy (PPV3) than mammography: The recall rate was 10.7% (n = 515) vs 9.4% (n = 453, P = .03), the biopsy rate was 5.5% (n = 266) vs 2.0% (n = 97, P < .001), and PPV3 was 11.7% (31/266) vs 38.1% (37/97, P < .001).

CONCLUSIONS

Cancer detection rate with US is comparable with mammography, with a greater proportion of invasive and node-negative cancers among US detections. False positives are more common with US screening.