Upgrade Rates of Variant Lobular Carcinoma In Situ Compared to Classic Lobular Carcinoma In Situ Diagnosed in Core Needle Biopsies: A 10-Year Single Institution Retrospective Study

The primary aim of this study was to determine the upgrade rates of variant lobular carcinoma in situ (V-LCIS, ie, combined florid [F-LCIS] and pleomorphic [P-LCIS]) compared with classic LCIS (C-LCIS) when diagnosed on core needle biopsy (CNB). The secondary goal was to determine the rate of progression/development of invasive carcinoma on long-term follow-up after primary excision. After institutional review board approval, our institutional pathology database was searched for patients with "pure" LCIS diagnosed on CNB who underwent subsequent excision. Radiologic findings were reviewed, radiologic-pathologic (rad-path) correlation was performed, and follow-up patient outcome data were obtained. One hundred twenty cases of LCIS were identified on CNB (C-LCIS = 97, F-LCIS = 18, and P-LCIS = 5). Overall upgrade rates after excision for C-LCIS, F-LCIS, and P-LCIS were 14% (14/97), 44% (8/18), and 40% (2/5), respectively. Of the total cases, 79 (66%) were deemed rad-path concordant. Of these, the upgrade rate after excision for C-LCIS, F-LCIS, and P-LCIS was 7.5% (5 of 66), 40% (4 of 10), and 0% (0 of 3), respectively. The overall upgrade rate for V-LCIS was higher than for C-LCIS (P = .004), even for the cases deemed rad-path concordant (P value: .036). Most upgraded cases (23 of 24) showed pT1a disease or lower. With an average follow-up of 83 months, invasive carcinoma in the ipsilateral breast was identified in 8/120 (7%) cases. Six patients had died: 2 of (contralateral) breast cancer and 4 of other causes. Because of a high upgrade rate, V-LCIS diagnosed on CNB should always be excised. The upgrade rate for C-LCIS (even when rad-path concordant) is higher than reported in many other studies. Rad-path concordance read, surgical consultation, and individualized decision making are recommended for C-LCIS cases. The risk of developing invasive carcinoma after LCIS diagnosis is small (7% with ∼7-year follow-up), but active surveillance is required to diagnose early-stage disease.

Breast Radiologists' Perceptions on the Detection and Management of Invasive Lobular Carcinoma: Most Agree Imaging Beyond Mammography Is Warranted

OBJECTIVE

To determine breast radiologists' confidence in detecting invasive lobular carcinoma (ILC) on mammography and the perceived need for additional imaging in screening and preoperative settings.

METHODS

A 16-item anonymized survey was developed, and IRB exemption obtained, by the Society of Breast Imaging (SBI) Patient Care and Delivery Committee and the Lobular Breast Cancer Alliance. The survey was emailed to 2946 radiologist SBI members on February 15, 2023. The survey recorded demographics, perceived modality-specific sensitivity for ILC to the nearest decile, and opinions on diagnosing ILC in screening and staging imaging. Five-point Likert scales were used (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree).

RESULTS

Response rate was 12.4% (366/2946). Perceived median (interquartile range) modality-specific sensitivities for ILC were MRI 90% (80-90), contrast-enhanced mammography 80% (70-90), molecular breast imaging 80% (60-90), digital breast tomosynthesis 70% (60-80), US 60% (50-80), and 2D mammography 50% (30-60). Only 25% (85/340) respondents were confident in detecting ILC on screening mammography in dense breasts, while 67% (229/343) were confident if breasts were nondense. Most agreed that supplemental screening is needed to detect ILC in women with dense breasts (272/344, 79%) or a personal history of ILC (248/341, 73%), with 34% (118/334) indicating that supplemental screening would also benefit women with nondense breasts. Most agreed that additional imaging is needed to evaluate extent of disease in women with newly diagnosed ILC, regardless of breast density (dense 320/329, 97%; nondense 263/329, 80%).

CONCLUSION

Most breast radiologists felt that additional imaging beyond mammography is needed to more confidently screen for and stage ILC.

Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk

Background Background parenchymal enhancement (BPE) at dynamic contrast-enhanced (DCE) MRI of cancer-free breasts increases the risk of developing breast cancer; implications of quantitative BPE in ipsilateral breasts with breast cancer are largely unexplored. Purpose To determine whether quantitative BPE measurements in one or both breasts could be used to predict recurrence risk in women with breast cancer, using the Oncotype DX recurrence score as the reference standard. Materials and Methods This HIPAA-compliant retrospective single-institution study included women diagnosed with breast cancer between January 2007 and January 2012 (development set) and between January 2012 and January 2017 (internal test set). Quantitative BPE was automatically computed using an in-house-developed computer algorithm in both breasts. Univariable logistic regression was used to examine the association of BPE with Oncotype DX recurrence score binarized into high-risk (recurrence score >25) and low- or intermediate-risk (recurrence score ≤25) categories. Models including BPE measures were assessed for their ability to distinguish patients with high risk versus those with low or intermediate risk and the actual recurrence outcome. Results The development set included 127 women (mean age, 58 years ± 10.2 [SD]; 33 with high risk and 94 with low or intermediate risk) with an actual local or distant recurrence rate of 15.7% (20 of 127) at a minimum 10 years of follow-up. The test set included 60 women (mean age, 57.8 years ± 11.6; 16 with high risk and 44 with low or intermediate risk). BPE measurements quantified in both breasts were associated with increased odds of a high-risk Oncotype DX recurrence score (odds ratio range, 1.27-1.66 [95% CI: 1.02, 2.56]; P < .001 to P = .04). Measures of BPE combined with tumor radiomics helped distinguish patients with a high-risk Oncotype DX recurrence score from those with a low- or intermediate-risk score, with an area under the receiver operating characteristic curve of 0.94 in the development set and 0.79 in the test set. For the combined models, the negative predictive values were 0.97 and 0.93 in predicting actual distant recurrence and local recurrence, respectively. Conclusion Ipsilateral and contralateral DCE MRI measures of BPE quantified in patients with breast cancer can help distinguish patients with high recurrence risk from those with low or intermediate recurrence risk, similar to Oncotype DX recurrence score. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zhou and Rahbar in this issue.

Tubular Adenoma of the Breast: Radiologic-Pathologic Correlation

Breast tubular adenomas (TAs) are rare, benign glandular epithelial tumors that arise from a proliferation of acini in the terminal duct lobular units. In the literature, 40 TA cases have previously been reported, and we describe 5 additional cases in this article. In the small number of reported cases, TAs present most often in women of reproductive age but may also occur in postmenopausal women. Mammographically and sonographically, TAs are almost indistinguishable from fibroadenomas (FAs), and they typically present on US as hypoechoic, oval, circumscribed, parallel masses with variable internal vascularity. TAs can also be seen on mammography as oval masses with microlobulated margins, or as grouped coarse, heterogeneous microcalcifications with or without associated mass or asymmetry. On MRI, TAs present as heterogeneously enhancing, T2-hyperintense oval masses with persistent kinetics. Histopathologically, TAs consist of closely packed round tubules with minimal stroma, in distinction to FAs, which have a prominent stromal component that surrounds and can distort the associated tubules. Because of their benign classification and excellent prognosis, patients with biopsy-confirmed TAs may resume routine screening. Complete surgical excision may be considered for cosmetic purposes or for TAs exhibiting associated suspicious calcifications or rapid growth.

Implementing the National Dense Breast Reporting Standard, Expanding Supplemental Screening Using Current Guidelines, and the Proposed Find It Early Act

Thirty-eight states and the District of Columbia (DC) have dense breast notification laws that mandate varying levels of patient notification about breast density after a mammogram, and these cover over 90% of American women. On March 10, 2023, the Food and Drug Administration issued a final rule amending regulations under the Mammography Quality Standards Act for a national dense breast reporting standard for both patient results letters and mammogram reports. Effective September 10, 2024, letters will be required to tell a woman her breasts are "dense" or "not dense," that dense tissue makes it harder to find cancers on a mammogram, and that it increases the risk of developing cancer. Women with dense breasts will also be told that other imaging tests in addition to a mammogram may help find cancers. The specific density category can be added (eg, if mandated by a state "inform" law). Reports to providers must include the Breast Imaging Reporting and Data System density category. Implementing appropriate supplemental screening should be based on patient risk for missed breast cancer on mammography; such assessment should include consideration of breast density and other risk factors. This article discusses strategies for implementation. Currently 21 states and DC have varying insurance laws for supplemental breast imaging; in addition, Oklahoma requires coverage for diagnostic breast imaging. A federal insurance bill, the Find It Early Act, has been introduced that would ensure no-cost screening and diagnostic imaging for women with dense breasts or at increased risk and close loopholes in state laws.

Diabetic Fibrous Mastopathy: Imaging Features With Histopathologic Correlation

Diabetic fibrous mastopathy (DFM) is a rare benign fibrotic disease of the breast that develops in patients with longstanding and often uncontrolled diabetes mellitus. Clinically, patients may present with an irregular, firm, palpable mass, which may be solitary or multiple, occurring in one or both breasts. Diabetic fibrous mastopathy occurs most often in premenopausal women with heterogeneously or extremely dense breasts; mammography may show focal asymmetry or, less often, a noncalcified mass with indistinct or obscured margins, but there are usually no discrete findings. On US, DFM may have marked hypoechogenicity and posterior shadowing secondary to extensive fibrosis. Diabetic fibrous mastopathy features on contrast-enhanced MRI are also nonspecific, with gradual persistent nonmass enhancement reported. Because the clinical presentation and US features of DFM overlap with those of breast cancer, histopathologic correlation is needed to confirm diagnosis and exclude malignancy. These findings include collagenous stroma often with keloidal features and chronic perilobular and perivascular inflammation. Histopathologic findings of lymphocytic lobulitis and perivascular inflammation are common to other autoimmune conditions.

Analytic Hierarchy Process Analysis of Patient Preferences for Contrast-Enhanced Mammography Versus MRI as Supplemental Screening Options for Breast Cancer

OBJECTIVE

To guide implementation of supplemental breast screening by assessing patient preferences for contrast-enhanced mammography (CEM) versus MRI using analytic hierarchy process (AHP) methodology.

METHODS

In an institutional review board-approved, HIPAA-compliant protocol, from March 23 to June 3, 2022, we contacted 579 women who had both CEM screening and MRI. Women were e-mailed an invitation to complete an online survey developed using an AHP-based model to elicit preferences for CEM or MRI. Methods for categorical data analysis were used to evaluate factors affecting preferences, under the Bonferroni correction for multiplicity.

RESULTS

Complete responses were received from 222 (38.3%) women; the 189 women with a personal history of breast cancer had a mean age 61.8 years, and the 34 women without a personal history of breast cancer had a mean age of 53.6 years. Of 222 respondents, 157 (70.7%, confidence interval [CI]: 64.7-76.7) were determined to prefer CEM to MRI. Breast positioning was the most important criterion for 74 of 222 (33.3%) respondents, with claustrophobia, intravenous line placement, and overall stress most important for 38, 37, and 39 women (17.1%, 16.7%, and 17.6%), respectively, and noise level, contrast injection, and indifference being emphasized least frequently (by 10 [4.5%], 11 [5.0%], and 13 [5.9%] women, respectively). CEM preference was most prevalent (MRI least prevalent) for respondents emphasizing claustrophobia (37 of 38 [97%], CI: 86.2-99.9); CEM preference was least prevalent (MRI most prevalent) for respondents emphasizing breast positioning (40 of 74 [54%], CI: 42.1-65.7).

CONCLUSIONS

AHP-based modeling reveals strong patient preferences for CEM over MRI, with claustrophobia favoring preference for CEM and breast positioning relatively favoring preference for MRI. Our results should help guide implementation of screening CEM and MRI.

Response in axillary lymph nodes to neoadjuvant chemotherapy for breast cancers: correlation with breast response, pathologic features, and accuracy of radioactive seed localization

OBJECTIVES

This study examined the accuracy of radioactive seed localization (RSL) of lymph nodes (LNs) following neoadjuvant chemotherapy (NAC) for invasive breast carcinoma, recorded pathologic features of LNs following NAC, evaluated concordance of response between breast and LNs, and identified clinicopathologic factors associated with higher risk of residual lymph node involvement.

METHODS

Clinical records, imaging, and pathology reports and slides were retrospectively reviewed for 174 breast cancer patients who received NAC. Chi-square and Fisher's exact tests were used to compare differences in risk of residual lymph node disease.

RESULTS

Retrieval of biopsied pre-therapy positive LN was confirmed in 86/93 (88%) cases overall, and in 75/77 (97%) of cases utilizing RSL. Biopsy clip site was the best pathologic feature to confirm retrieval of a biopsied lymph node. Pre-therapy clinical N stage > 0, positive pre-therapy lymph node biopsy, estrogen and progesterone receptor positivity, Ki67 < 50%, HR + /HER2- tumors, and residual breast disease had higher likelihood of residual lymph node disease after NAC (p < 0.001).

CONCLUSIONS

RSL-guided LN excision improves retrieval of previously biopsied LNs following NAC. The pathologist can use histologic features to confirm retrieval of targeted LNs, and tumor characteristics can be used to predict a higher risk of residual LN involvement.

Toward AI-supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting

Background Most low- and middle-income countries lack access to organized breast cancer screening, and women with lumps may wait months for diagnostic assessment. Purpose To demonstrate that artificial intelligence (AI) software applied to breast US images obtained with low-cost portable equipment and by minimally trained observers could accurately classify palpable breast masses for triage in a low-resource setting. Materials and Methods This prospective multicenter study evaluated participants with at least one palpable mass who were enrolled in a hospital in Jalisco, Mexico, from December 2017 through May 2021. Orthogonal US images were obtained first with portable US with and without calipers of any findings at the site of lump and adjacent tissue. Then women were imaged with standard-of-care (SOC) US with Breast Imaging Reporting and Data System assessments by a radiologist. After exclusions, 758 masses in 300 women were analyzable by AI, with outputs of benign, probably benign, suspicious, and malignant. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. Results The mean patient age ± SD was 50.0 years ± 12.5 (range, 18-92 years) and mean largest lesion diameter was 13 mm ± 8 (range, 2-54 mm). Of 758 masses, 360 (47.5%) were palpable and 56 (7.4%) malignant, including six ductal carcinoma in situ. AI correctly identified 47 or 48 of 49 women (96%-98%) with cancer with either portable US or SOC US images, with AUCs of 0.91 and 0.95, respectively. One circumscribed invasive ductal carcinoma was classified as probably benign with SOC US, ipsilateral to a spiculated invasive ductal carcinoma. Of 251 women with benign masses, 168 (67%) imaged with SOC US were classified as benign or probably benign by AI, as were 96 of 251 masses (38%, P < .001) with portable US. AI performance with images obtained by a radiologist was significantly better than with images obtained by a minimally trained observer. Conclusion AI applied to portable US images of breast masses can accurately identify malignancies. Moderate specificity, which could triage 38%-67% of women with benign masses without tertiary referral, should further improve with AI and observer training with portable US. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Slanetz in this issue.

Prospective Multicenter Diagnostic Performance of Technologist-Performed Screening Breast Ultrasound After Tomosynthesis in Women With Dense Breasts (the DBTUST)

PURPOSE

To assess diagnostic performance of digital breast tomosynthesis (DBT) alone or combined with technologist-performed handheld screening ultrasound (US) in women with dense breasts.

METHODS

In an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant multicenter protocol in western Pennsylvania, 6,179 women consented to three rounds of annual screening, interpreted by two radiologist observers, and had appropriate follow-up. Primary analysis was based on first observer results.

RESULTS

Mean participant age was 54.8 years (range, 40-75 years). Across 17,552 screens, there were 126 cancer events in 125 women (7.2/1,000; 95% CI, 5.9 to 8.4). In year 1, DBT-alone cancer yield was 5.0/1,000, and of DBT+US, 6.3/1,000, difference 1.3/1,000 (95% CI, 0.3 to 2.1; P = .005). In years 2 + 3, DBT cancer yield was 4.9/1,000, and of DBT+US, 5.9/1,000, difference 1.0/1,000 (95% CI, 0.4 to 1.5; P < .001). False-positive rate increased from 7.0% for DBT in year 1 to 11.5% for DBT+US and from 5.9% for DBT in year 2 + 3 to 9.7% for DBT+US (P < .001 for both). Nine cancers were seen only by double reading DBT and one by double reading US. Ten interval cancers (0.6/1,000 [95% CI, 0.2 to 0.9]) were identified. Despite reduction in specificity, addition of US improved receiver operating characteristic curves, with area under receiver operating characteristic curve increasing from 0.83 for DBT alone to 0.92 for DBT+US in year 1 (P = .01), with smaller improvements in subsequent years. Of 6,179 women, across all 3 years, 172/6,179 (2.8%) unique women had a false-positive biopsy because of DBT as did another 230/6,179 (3.7%) women because of US (P < .001).

CONCLUSION

Overall added cancer detection rate of US screening after DBT was modest at 19/17,552 (1.1/1,000; CI, 0.5- to 1.6) screens but potentially overcomes substantial increases in false-positive recalls and benign biopsies.

Contrast-enhanced Mammography-guided Biopsy: Initial Trial and Experience

OBJECTIVE

Evaluate lesion visibility and radiologist confidence during contrast-enhanced mammography (CEM)-guided biopsy.

METHODS

Women with BI-RADS ≥4A enhancing breast lesions were prospectively recruited for 9-g vacuum-assisted CEM-guided biopsy. Breast density, background parenchymal enhancement (BPE), lesion characteristics (enhancement and conspicuity), radiologist confidence (scale 1-5), and acquisition times were collected. Signal intensities in specimens were analyzed. Patient surveys were collected.

RESULTS

A cohort of 28 women aged 40-81 years (average 57) had 28 enhancing lesions (7/28, 25% malignant). Breast tissue was scattered (10/28, 36%) or heterogeneously dense (18/28, 64%) with minimal (12/28, 43%), mild (7/28, 25%), or moderate (9/28, 32%) BPE on CEM. Twelve non-mass enhancements, 11 masses, 3 architectural distortions, and 2 calcification groups demonstrated weak (12/28, 43%), moderate (14/28, 50%), or strong (2/28, 7%) enhancement. Specimen radiography demonstrated lesion enhancement in 27/28 (96%). Radiologists reported complete lesion removal on specimen radiography in 8/28 (29%). Average time from contrast injection to specimen radiography was 18 minutes (SD = 5) and, to post-procedure mammogram (PPM), 34 minutes (SD = 10). Contrast-enhanced mammography PPM was performed in 27/28 cases; 13/19 (68%) of incompletely removed lesions on specimen radiography showed residual enhancement; 6/19 (32%) did not. Across all time points, average confidence was 2.2 (SD = 1.2). Signal intensities of enhancing lesions were similar to iodine. Patients had an overall positive assessment.

CONCLUSION

Lesion enhancement persisted through PPM and was visible on low energy specimen radiography, with an average "confident" score. Contrast-enhanced mammography-guided breast biopsy is easily implemented clinically. Its availability will encourage adoption of CEM.

Effect of an Educational Intervention on Women's Health Care Provider Knowledge Gaps About Breast Cancer Risk Model Use and High-risk Screening Recommendations

OBJECTIVE

To assess effectiveness of a web-based educational intervention on women's health care provider knowledge of breast cancer risk models and high-risk screening recommendations.

METHODS

A web-based pre- and post-test study including 177 U.S.-based women's health care providers was conducted in 2019. Knowledge gaps were defined as fewer than 75% of respondents answering correctly. Pre- and post-test knowledge differences (McNemar test) and associations of baseline characteristics with pre-test knowledge gaps (logistic regression) were evaluated.

RESULTS

Respondents included 131/177 (74.0%) physicians; 127/177 (71.8%) practiced obstetrics/gynecology. Pre-test, 118/177 (66.7%) knew the Gail model predicts lifetime invasive breast cancer risk; this knowledge gap persisted post-test [(121/177, 68.4%); P = 0.77]. Just 39.0% (69/177) knew the Gail model identifies women eligible for risk-reducing medications; this knowledge gap resolved. Only 48.6% (86/177) knew the Gail model should not be used to identify women meeting high-risk MRI screening guidelines; this deficiency decreased to 66.1% (117/177) post-test (P = 0.001). Pre-test, 47.5% (84/177) knew the Tyrer-Cuzick model is used to identify women meeting high-risk screening MRI criteria, 42.9% (76/177) to predict BRCA1/2 pathogenic mutation risk, and 26.0% (46/177) to predict lifetime invasive breast cancer risk. These knowledge gaps persisted but improved. For a high-risk 30-year-old, 67.8% (120/177) and 54.2% (96/177) pre-test knew screening MRI and mammography/tomosynthesis are recommended, respectively; 19.2% (34/177) knew both are recommended; and 53% (94/177) knew US is not recommended. These knowledge gaps resolved or reduced.

CONCLUSION

Web-based education can reduce important provider knowledge gaps about breast cancer risk models and high-risk screening recommendations.

Pseudoangiomatous Stromal Hyperplasia: Radiologic-Pathologic Correlation

Pseudoangiomatous stromal hyperplasia (PASH) is a benign mesenchymal proliferative lesion of the breast. PASH is postulated to be hormonally induced and predominantly occurs in premenopausal women and postmenopausal women on menopausal hormone therapy. Clinical presentation varies from screen-detected lesions to palpable masses. Imaging findings of PASH are nonspecific. The most common mammographic findings are an oval or round circumscribed non-calcified mass or developing asymmetry. On US, PASH is often seen as an oval hypoechoic mass that may be circumscribed and can have an echogenic rim, or, when manifest as mammographic asymmetry, US may show a corresponding non-mass focal area of echogenic tissue. Limited studies have investigated the MRI appearance, with PASH most often manifesting as non-mass enhancement, or, less often, as an oval or irregular mass with persistent kinetics. Histopathologically, PASH can be mistaken for a fibroadenoma or phyllodes tumor and has features overlapping low-grade angiosarcoma. Assessment of radiologic-pathologic concordance is particularly important as PASH is often an incidental finding, adjacent to the targeted lesion at histopathology. Surgical excision or repeat core-needle biopsy is necessary for discordant suspicious cases. After a benign, concordant diagnosis of PASH, the patient may resume routine screening.

Deep learning of longitudinal mammogram examinations for breast cancer risk prediction

Information in digital mammogram images has been shown to be associated with the risk of developing breast cancer. Longitudinal breast cancer screening mammogram examinations may carry spatiotemporal information that can enhance breast cancer risk prediction. No deep learning models have been designed to capture such spatiotemporal information over multiple examinations to predict the risk. In this study, we propose a novel deep learning structure, LRP-NET, to capture the spatiotemporal changes of breast tissue over multiple negative/benign screening mammogram examinations to predict near-term breast cancer risk in a case-control setting. Specifically, LRP-NET is designed based on clinical knowledge to capture the imaging changes of bilateral breast tissue over four sequential mammogram examinations. We evaluate our proposed model with two ablation studies and compare it to three models/settings, including 1) a "loose" model without explicitly capturing the spatiotemporal changes over longitudinal examinations, 2) LRP-NET but using a varying number (i.e., 1 and 3) of sequential examinations, and 3) a previous model that uses only a single mammogram examination. On a case-control cohort of 200 patients, each with four examinations, our experiments on a total of 3200 images show that the LRP-NET model outperforms the compared models/settings.

Coaching: A Strategy for Breast Radiologists' Professional Development

The continually increasing demands placed on physicians have led to high levels of burnout, and breast radiologists are no exception. Professional coaching is a means to guide and support the radiologist through a process of positive thinking, stress reduction, goal setting, mental growth, work-life balance, and behavioral change. Professional coaching may be effective in preparation for leadership roles or in response to workplace issues or conflict. The radiologist, with the help of a coach, establishes goals, expands perception and mindset, and collaboratively may find ways to resolve issues by taking new and different approaches. This article discusses why radiologists should seek out a certified coach and what a coach can offer radiologists during these trying times, as well as outlining the coaching process. Coaching has proven useful in addressing professional growth, workplace issues, and physician burnout. At the conclusion of the article, the readers will be able to discern whether coaching can support a better quality of life for them.

Nipple Adenoma: Correlation of Imaging Findings and Histopathology

Nipple adenomas (NAs) are benign neoplasms composed of papillary hyperplasia of the epithelium of the major lactiferous ducts. Patients with NA may report bloody nipple discharge and clinically may resemble Paget disease, raising concern for malignancy. Mammographically, NAs are often occult. US can show a hypervascular circumscribed mass centered within the nipple with varying echogenicity. Diagnosis is usually made on punch biopsy or excision, but breast radiologists should be aware of this entity. Malignancy can be found elsewhere in the ipsilateral or contralateral breast, or very rarely may directly extend to involve an NA, but published experience with concurrent malignancies is small. We describe the radiologic-pathologic correlation of NAs.

Personalising therapy for early-stage oestrogen receptor-positive breast cancer in older women

Age is one of the most important risk factors for the development of breast cancer. Nearly a third of all breast cancer cases occur in older women (aged ≥70 years), with most cases being oestrogen receptor-positive (ER+). Such tumours are often indolent and unlikely to be the ultimate cause of death for older women, particularly when considering other comorbidities. This Review focuses on unique clinical considerations for screening, detection, and treatment regimens for older women who develop ER+ breast cancers-specifically, we focus on recent trends for de-implementation of screening, staging, surgery, and adjuvant therapies along the continuum of care. Additionally, we also review emerging basic and translational research that will further uncover the unique underlying biology of these tumours, which develop in the context of systemic age-related inflammation and changing hormone profiles. With prevailing trends of clinical de-implementation, new insights into mechanistic biology might provide an opportunity for precision medicine approaches to treat patients with well tolerated, low-toxicity agents to extend patients' lives with a higher quality of life, prevent tumour recurrences, and reduce cancer-related burdens.

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

US is widely used in breast imaging for diagnostic purposes and is also used increasingly for supplemental screening in women with dense breasts. US frequently depicts masses that are occult on mammography, even after tomosynthesis, and the vast majority of such masses are benign. Many masses seen only on screening US are easily recognized as benign simple cysts. Probably benign, BI-RADS 3, or low suspicion, BI-RADS 4A masses are also common and often prompt short-interval follow-up or biopsy, respectively, yet the vast majority of these are benign. This review details appropriate characterization, classification, and new approaches to the management of probably benign masses seen on screening US that can reduce false positives and, thereby, reduce costs and patient anxiety.

Granular Cell Tumor of the Breast: Radiologic-Pathologic Correlation

Granular cell tumor (GCT) is an uncommon neoplasm arising from perineural Schwann cells that can arise anywhere in the body and is particularly rare in the breast. Imaging typically shows an irregular, noncalcified mass with high density on mammography and intense posterior shadowing on US that mimics malignancy. Benign GCTs can be locally aggressive and invade the skin or chest wall. Core biopsy is necessary for diagnosis. Polygonal- to spindle-shaped cells with prominent cytoplasmic eosinophilic granules show S-100 and CD68 staining on immunohistochemistry and lack cytokeratin, estrogen, or progesterone expression. The vast majority of GCTs are benign, albeit locally infiltrative, tumors cured by wide local excision.

Impact of Original and Artificially Improved Artificial Intelligence-based Computer-aided Diagnosis on Breast US Interpretation

OBJECTIVE

For breast US interpretation, to assess impact of computer-aided diagnosis (CADx) in original mode or with improved sensitivity or specificity.

METHODS

In this IRB approved protocol, orthogonal-paired US images of 319 lesions identified on screening, including 88 (27.6%) cancers (median 7 mm, range 1-34 mm), were reviewed by 9 breast imaging radiologists. Each observer provided BI-RADS assessments (2, 3, 4A, 4B, 4C, 5) before and after CADx in a mode-balanced design: mode 1, original CADx (outputs benign, probably benign, suspicious, or malignant); mode 2, artificially-high-sensitivity CADx (benign or malignant); and mode 3, artificially-high-specificity CADx (benign or malignant). Area under the receiver operating characteristic curve (AUC) was estimated under each modality and for standalone CADx outputs. Multi-reader analysis accounted for inter-reader variability and correlation between same-lesion assessments.

RESULTS

AUC of standalone CADx was 0.77 (95% CI: 0.72-0.83). For mode 1, average reader AUC was 0.82 (range 0.76-0.84) without CADx and not significantly changed with CADx. In high-sensitivity mode, all observers' AUCs increased: average AUC 0.83 (range 0.78-0.86) before CADx increased to 0.88 (range 0.84-0.90), P < 0.001. In high-specificity mode, all observers' AUCs increased: average AUC 0.82 (range 0.76-0.84) before CADx increased to 0.89 (range 0.87-0.92), P < 0.0001. Radiologists responded more frequently to malignant CADx cues in high-specificity mode (42.7% vs 23.2% mode 1, and 27.0% mode 2, P = 0.008).

CONCLUSION

Original CADx did not substantially impact radiologists' interpretations. Radiologists showed improved performance and were more responsive when CADx produced fewer false-positive malignant cues.

Effect of an educational intervention on women's healthcare provider knowledge gaps about breast density, breast cancer risk, and screening

OBJECTIVES

We sought to assess the effect of an educational intervention, based on DenseBreast-info.org website content, on women's healthcare provider knowledge of breast density, its risk and screening implications, and comfort level discussing these topics with patients.

METHODS

US-based women's healthcare providers participated in a web-based pretest/posttest study from May 14, 2019 to September 30, 2019. Pretest included demographics; comfort/knowledge discussing breast density impact on risk and screening; and educational material. Posttest contained the same knowledge and comfort questions. We assessed mean pretest/posttest score and comfort level differences (paired t tests) and pretest/posttest knowledge gap differences (McNemar test). We evaluated associations of baseline characteristics with pretest score and score improvement using multiple linear regression, and associations with knowledge gaps using logistic regression.

RESULTS

Of 177 providers analyzed, 74.0% (131/177) were physicians and 71.8% (127/177) practiced obstetrics/gynecology. Average test score increased from 40.9% (5.7/14) responses correct pretest to 72.1% (10.1/14) posttest (P < 0.001). Pretest, 56.5% (100/177) knew women with extremely dense breasts have four-to-six-fold greater breast cancer risk than those with fatty breasts; 29.4% (52/177) knew risk increases with increasing glandular tissue; only 5.6% (10/177) knew 3D/tomosynthesis does not improve cancer detection in extremely dense breasts over 2D mammography; and 70.6% (125/177) would consider supplemental ultrasound after mammography in an average-risk 50-year old with dense breasts. Postintervention, these knowledge gaps resolved or reduced (all P < 0.005) and comfort in discussing breast density implications increased (all P < 0.001).

CONCLUSIONS

Important knowledge gaps about implications of breast density exist among women's healthcare providers, which can be effectively addressed with web-based education.

Cancer Yield Exceeds 2% for BI-RADS 3 Probably Benign Findings in Women Older Than 60 Years in the National Mammography Database

Background Breast Imaging Reporting and Data System (BI-RADS) category 3 (BR3) (probably benign) mammographic assessments are reserved for imaging findings known to have likelihood of malignancy of 2% or less. Purpose To determine the effect of age, finding type, and prior mammography on cancer yield for BR3 findings in the National Mammography Database (NMD). Materials and Methods This HIPAA-compliant retrospective cohort institutional review board-exempt study evaluated women recalled from screening mammography followed by BR3 assessment at diagnostic evaluation from January 2009 to March 2018 and from 471 NMD facilities. Only the first BR3 occurrence was included for women with biopsy or imaging follow-up of at least 2 years. Women with a history of breast cancer or who underwent biopsy at time of initial BR3 assessment were excluded. Women were stratified by age in 10-year intervals. Cancer yield was calculated for each age group, with (for presumed new findings) and without prior mammographic comparison, and by lesion type, where available. Linear regression with weighted-age binning was performed to assess for differences between groups; P < .05 was indicative of a significant difference. Results A total of 1 380 652 (18.2%) women were recalled after screening mammography, of whom 157 130 (11.4%) were given a BR3 assessment within 90 days after screening. Of these, 43 628 women (median age, 55 years; age range, 25-90 years) had adequate follow-up for analysis. Cancer yield increased with increasing age decile, ranging from 0.51% (six of 1167) in women aged 30-39 years to 4.63% (41 of 885) in women aged 80-90 years; cancer yield exceeded 2% at and after age 59.7 years for baseline findings and at and after age 53.6 years for presumed new findings, although there was no effect on stage distribution. Cancer yield for baseline BR3 masses was 10 of 2111 (0.47% [95% CI: 0.24, 0.90]) versus 47 of 3003 (1.57% [95% CI: 1.16, 2.09]) with prior comparisons (P < .001); cancer yield for baseline calcifications was eight of 929 (0.86% [95% CI: 0.40, 1.76]) versus 84 of 2999 (2.80% [95% CI: 2.23, 3.47]) with prior comparisons (P < .001). Difference in cancer yield was 0.51% (95% CI: 0.16, 0.86) between women with and women without prior comparison at the same age (P = .006). Conclusion Cancer yield exceeded the 2% threshold for women aged 60 years or older and reached 4.6% for women aged 80-89 years. Breast Imaging Reporting and Data System 3 findings in women with a prior comparison had higher cancer yield than in those without a prior comparison at the same age. © RSNA, 2021 Online supplemental material is available for this article.

Training Radiologists to Interpret Contrast-enhanced Mammography: Toward a Standardized Lexicon

OBJECTIVE

Using terms adapted from the BI-RADS Mammography and MRI lexicons, we trained radiologists to interpret contrast-enhanced mammography (CEM) and assessed reliability of their description and assessment.

METHODS

A 60-minute presentation on CEM and terminology was reviewed independently by 21 breast imaging radiologist observers. For 21 CEM exams with 31 marked findings, observers recorded background parenchymal enhancement (BPE) (minimal, mild, moderate, marked), lesion type (oval/round or irregular mass, or non-mass enhancement), intensity of enhancement (none, weak, medium, strong), enhancement quality (none, homogeneous, heterogeneous, rim), and BI-RADS assessment category (2, 3, 4A, 4B, 4C, 5). "Expert" consensus of 3 other radiologists experienced in CEM was developed. Kappa statistic was used to assess agreement between radiologists and expert consensus, and between radiologists themselves, on imaging feature categories and final assessments. Reproducibility of specific feature descriptors was assessed as fraction of consensus-concordant responses.

RESULTS

Radiologists demonstrated moderate agreement for BPE, (mean kappa, 0.43; range, 0.05-0.69), and lowest reproducibility for "minimal." Agreement was substantial for lesion type (mean kappa, 0.70; range, 0.47-0.93), moderate for intensity of enhancement (mean kappa, 0.57; range, 0.44-0.76), and moderate for enhancement quality (mean kappa, 0.59; range, 0.20-0.78). Agreement on final assessment was fair (mean kappa, 0.26; range, 0.09-0.44), with BI-RADS category 3 the least reproducible. Decision to biopsy (BI-RADS 2-3 vs 4-5) showed moderate agreement with consensus (mean kappa, 0.54; range, -0.06-0.87).

CONCLUSION

With minimal training, agreement for description of CEM findings by breast imaging radiologists was comparable to other BI-RADS lexicons.

Screening Algorithms in Dense Breasts: AJR Expert Panel Narrative Review

Screening mammography reduces breast cancer mortality; however, when used to examine women with dense breasts, its performance and resulting benefits are reduced. Increased breast density is an independent risk factor for breast cancer. Digital breast tomosynthesis (DBT), ultrasound (US), molecular breast imaging (MBI), MRI, and contrast-enhanced mammography (CEM) each have shown improved cancer detection in dense breasts when compared with 2D digital mammography (DM). DBT is the preferred mammographic technique for producing a simultaneous reduction in recalls (i.e., additional imaging). US further increases cancer detection after DM or DBT and reduces interval cancers (cancers detected in the interval between recommended screening examinations), but it also produces substantial additional false-positive findings. MBI improves cancer detection with an effective radiation dose that is approximately fourfold that of DM or DBT but is still within accepted limits. MRI provides the greatest increase in cancer detection and reduces interval cancers and late-stage disease; abbreviated techniques will reduce cost and improve availability. CEM appears to offer performance similar to that of MRI, but further validation is needed. Dense breast notification will soon be a national standard; therefore, understanding the performance of mammography and supplemental modalities is necessary to optimize screening for women with dense breasts.

Encapsulated Papillary Carcinoma of the Breast: Imaging Features with Histopathologic Correlation

Encapsulated papillary carcinoma (EPC) is a rare, clinically indolent breast malignancy most common in postmenopausal women. Absence of myoepithelial cells at the periphery is a characteristic feature. Mammographically, EPC typically presents as a mostly circumscribed, noncalcified, dense mass that can have focally indistinct margins when there is associated frank invasive carcinoma. Ultrasound shows a circumscribed solid or complex cystic and solid mass, and occasional hemorrhage in the cystic component may produce a fluid-debris level; the solid components typically show intense washout enhancement on MRI. Color Doppler may demonstrate a prominent vascular pedicle and blood flow within solid papillary fronds. Encapsulated papillary carcinoma can exist in pure form; however, EPC is often associated with conventional ductal carcinoma in-situ and/or invasive ductal carcinoma, no special type. Adjacent in-situ and invasive disease may be only focally present at the periphery of EPC and potentially unsampled at core-needle biopsy. In order to facilitate diagnosis, the mass wall should be included on core-needle biopsy, which will show absence of myoepithelial markers. Staging and prognosis are determined by any associated frankly invasive component, with usually excellent long-term survival and rare distant metastases.

Radiologic Technologist and Radiologist Knowledge Gaps about Breast Density Revealed by an Online Continuing Education Course

OBJECTIVE

We sought to identify provider knowledge gaps and their predictors, as revealed by a breast density continuing education course marketed to the radiology community.

METHODS

The course, continually available online during the study period of November 2, 2016 and December 31, 2018, includes demographics collection; a monograph on breast density, breast cancer risk, and screening; and a post-test. Four post-test questions were modified during the study period, resulting in different sample sizes pre- and postmodification. Multiple logistic regression was used to identify predictors of knowledge gaps (defined as > 25% of responses incorrect).

RESULTS

Of 1649 analyzable registrants, 1363 (82.7%) were radiologic technologists, 226 (13.7%) were physicians, and 60 (3.6%) were other nonphysicians; over 90% of physicians and over 90% of technologists/nonphysicians specialized in radiology. Sixteen of 49 physicians (32.7%) and 80/233 (34.3%) technologists/nonphysicians mistakenly thought the Gail model should be used to determine "high-risk" status for recommending MRI or genetic testing. Ninety-nine of 226 (43.8%) physicians and 682/1423 (47.9%) technologists/nonphysicians misunderstood the inverse relationship between increasing age and lifetime breast cancer risk. Fifty-two of 166 (31.3%) physicians and 549/1151 (47.7%) technologists/nonphysicians were unaware that MRI should be recommended for women with a family history of BRCA1/BRCA2 mutations. Tomosynthesis effectiveness was overestimated, with 18/60 (30.0%) physicians and 95/272 (34.9%) technologists/nonphysicians believing sensitivity nearly equaled MRI. Knowledge gaps were more common in technologists/nonphysicians.

CONCLUSIONS

Important knowledge gaps about breast density, breast cancer risk assessment, and screening exist among radiologic technologists and radiologists. Continued education efforts may improve appropriate breast cancer screening recommendations.

Cancer Yield and Patterns of Follow-up for BI-RADS Category 3 after Screening Mammography Recall in the National Mammography Database

Background The literature supports the use of short-interval follow-up as an alternative to biopsy for lesions assessed as probably benign, Breast Imaging Reporting and Data System (BI-RADS) category 3, with an expected malignancy rate of less than 2%. Purpose To assess outcomes from 6-, 12-, and 24-month follow-up of probably benign findings first identified at recall from screening mammography in the National Mammography Database (NMD). Materials and Methods This retrospective study included women recalled from screening mammography with BI-RADS category 3 assessment at additional evaluation from January 2009 through March 2018 from 471 NMD facilities. Only the first BI-RADS category 3 occurrence for women aged 25 years or older with no personal history of breast cancer was analyzed, with biopsy or 2-year imaging follow-up. Cancer yield and positive predictive value of biopsies performed (PPV3) were determined at each follow-up. Results Among 45 202 women (median age, 55 years; range, 25-90 years) with a BI-RADS category 3 lesion, 1574 (3.5%) underwent biopsy at the time of lesion detection, yielding 72 cancers (cancer yield, 4.6%; 72 of 1574 women). For the remaining 43 628 women who accepted surveillance, 922 were seen within 90 days (with 78 lesions biopsied and 12 [15%] classified as malignant). The women still in surveillance (31 465 of 43 381 women [72.5%]) underwent follow-up mammography at 6 months. Of 3001 (9.5%) lesions biopsied, 456 (15.2%) were malignant (cancer yield, 1.5%; 456 of 31 465 women; 95% confidence interval [CI]: 1.3%, 1.6%). Among 18 748 of 25 997 women (72.1%) in surveillance who underwent follow-up at 12 months, 1219 (6.5%) underwent biopsy with 230 (18.9%) malignant lesions found (cancer yield, 1.2%; 230 of 18 748 women; 95% CI: 1.1%, 1.4%). Through 2-year follow-up, the biopsy rate was 11.2% (4894 of 43 628 women) with a cancer yield of 1.86% (810 malignancies found among 43 628 women; 95% CI: 1.73%, 1.98%) and a PPV3 of 16.6% (810 malignancies found among 4894 women). Conclusion In the National Mammography Database, Breast Imaging Reporting and Data System (BI-RADS) category 3 use is appropriate, with 1.86% cumulative cancer yield through 2-year follow-up. Of 810 malignancies, 468 (57.8%) were diagnosed at or before 6 months, validating necessity of short-interval follow-up of mammographic BI-RADS category 3 findings. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Moy in this issue.

A Prospective Study of Automated Breast Ultrasound Screening of Women with Dense Breasts in a Digital Breast Tomosynthesis-based Practice

OBJECTIVE

To assess prospectively the interpretative performance of automated breast ultrasound (ABUS) as a supplemental screening after digital breast tomosynthesis (DBT) or as a standalone screening of women with dense breast tissue.

METHODS

Under an IRB-approved protocol (written consent required), women with dense breasts prospectively underwent concurrent baseline DBT and ABUS screening. Examinations were independently evaluated, in opposite order, by two of seven Mammography Quality Standards Act-qualified radiologists, with the primary radiologist arbitrating disagreements and making clinical management recommendations. We report results for 1111 screening examinations (598 first year and 513 second year) for which all diagnostic workups are complete. Imaging was also retrospectively reviewed for all cancers. Statistical assessments used a 0.05 significance level and accounted for correlation between participants' examinations.

RESULTS

Of 1111 women screened, primary radiologists initially "recalled" based on DBT alone (6.6%, 73/1111, CI: 5.2%-8.2%), of which 20 were biopsied, yielding 6/8 total cancers. Automated breast ultrasound increased recalls overall to 14.4% (160/1111, CI: 12.4%-16.6%), with 27 total biopsies, yielding 1 additional cancer. Double reading of DBT alone increased the recall rate to 10.7% (119/1111), with 21 biopsies, with no improvement in cancer detection. Double reading ABUS increased the recall rate to 15.2% (169/1111, CI: 13.2%-17.5%) of women, of whom 22 were biopsied, yielding the detection of 7 cancers, including one seen only on double reading ABUS. Inter-radiologist agreement was similar for recall recommendations from DBT (κ = 0.24, CI: 0.14-0.34) and ABUS (κ = 0.23, CI: 0.15-0.32). Integrated assessments from both readers resulted in a recall rate of 15.1% (168/1111, CI: 13.1%-17.4%).

CONCLUSION

Supplemental or standalone ABUS screening detected cancers not seen on DBT, but substantially increased noncancer recall rates.

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.

Deep learning modeling using normal mammograms for predicting breast cancer risk

PURPOSE

To investigate two deep learning-based modeling schemes for predicting short-term risk of developing breast cancer using prior normal screening digital mammograms in a case-control setting.

METHODS

We conducted a retrospective Institutional Review Board-approved study on a case-control cohort of 226 patients (including 113 women diagnosed with breast cancer and 113 controls) who underwent general population breast cancer screening. For each patient, a prior normal (i.e., with negative or benign findings) digital mammogram examination [including mediolateral oblique (MLO) view and craniocaudal (CC) view two images] was collected. Thus, a total of 452 normal images (226 MLO view images and 226 CC view images) of this case-control cohort were analyzed to predict the outcome, i.e., developing breast cancer (cancer cases) or remaining breast cancer-free (controls) within the follow-up period. We implemented an end-to-end deep learning model and a GoogLeNet-LDA model and compared their effects in several experimental settings using two mammographic view images and inputting two different subregions of the images to the models. The proposed models were also compared to logistic regression modeling of mammographic breast density. Area under the receiver operating characteristic curve (AUC) was used as the model performance metric.

RESULTS

The highest AUC was 0.73 [95% Confidence Interval (CI): 0.68-0.78; GoogLeNet-LDA model on CC view] when using the whole-breast and was 0.72 (95% CI: 0.67-0.76; GoogLeNet-LDA model on MLO + CC view) when using the dense tissue, respectively, as the model input. The GoogleNet-LDA model significantly (all P < 0.05) outperformed the end-to-end GoogLeNet model in all experiments. CC view was consistently more predictive than MLO view in both deep learning models, regardless of the input subregions. Both models exhibited superior performance than the percent breast density (AUC = 0.54; 95% CI: 0.49-0.59).

CONCLUSIONS

The proposed deep learning modeling approach can predict short-term breast cancer risk using normal screening mammogram images. Larger studies are needed to further reveal the promise of deep learning in enhancing imaging-based breast cancer risk assessment.

Diagnostic Performance of MRI, Molecular Breast Imaging, and Contrast-enhanced Mammography in Women with Newly Diagnosed Breast Cancer

Background Staging newly diagnosed breast cancer by using dynamic contrast material-enhanced MRI is limited by access, high cost, and false-positive findings. The utility of contrast-enhanced mammography (CEM) and ^99mTc sestamibi-based molecular breast imaging (MBI) in this setting is largely unknown. Purpose To compare extent-of-disease assessments by using MRI, CEM, and MBI versus pathology in women with breast cancer. Materials and Methods In this HIPAA-compliant prospective study, women with biopsy-proven breast cancer underwent MRI, CEM, and MBI between October 2014 and April 2018. Eight radiologists independently interpreted each examination result prospectively and were blinded to interpretations of findings with the other modalities. Visibility of index malignancies, lesion size, and additional suspicious lesions (malignant or benign) were compared during pathology review. Accuracy of index lesion sizing and detection of additional lesions in women without neoadjuvant chemotherapy were compared. Results A total of 102 women were enrolled and 99 completed the study protocol (mean age, 51 years ± 11 [standard deviation]; range, 32-77 years). Lumpectomy or mastectomy was performed in 71 women (79 index malignancies) without neoadjuvant chemotherapy and in 28 women (31 index malignancies) with neoadjuvant chemotherapy. Of the 110 index malignancies, MRI, CEM, and MBI depicted 102 (93%; 95% confidence interval [CI]: 86%, 97%), 100 (91%; 95% CI: 84%, 96%), and 101 (92%; 95% CI: 85%, 96%) malignancies, respectively. In patients without neoadjuvant chemotherapy, pathologic size of index malignancies was overestimated with all modalities (P = .02). MRI led to overestimation of 24% (17 of 72) of malignancies by more than 1.5 cm compared with 11% (eight of 70) with CEM and 15% (11 of 72) with MBI. MRI depicted more (P = .007) nonindex lesions, with sensitivity similar to that of CEM or MBI, resulting in lower positive predictive value of additional biopsies (13 of 46 [28%; 95% CI: 17%, 44%] for MRI; 14 of 27 [52%; 95% CI: 32%, 71%] for CEM; and 11 of 25 [44%; 95% CI: 24%, 65%] for MBI (overall P = .01). Conclusion Contrast-enhanced mammography, molecular breast imaging, and MRI showed similar detection of all malignancies. MRI depicted more nonindex suspicious benign lesions than did contrast-enhanced mammography or molecular breast imaging, leading to lower positive predictive value of additional biopsies. All three modalities led to overestimation of index tumor size, particularly MRI. © RSNA, 2019 Online supplemental material is available for this article.

Palpable Breast Lump Triage by Minimally Trained Operators in Mexico Using Computer-Assisted Diagnosis and Low-Cost Ultrasound

Purpose

In low- to middle-income countries (LMICs), most breast cancers present as palpable lumps; however, most palpable lumps are benign. We have developed artificial intelligence–based computer-assisted diagnosis (CADx) for an existing low-cost portable ultrasound system to triage which lumps need further evaluation and which are clearly benign. This pilot study was conducted to demonstrate that this approach can be successfully used by minimally trained health care workers in an LMIC country.

Patients and Methods

We recruited and trained three nonradiologist health care workers to participate in an institutional review board–approved, Health Insurance Portability and Accountability Act–compliant pilot study in Jalisco, Mexico, to determine whether they could use portable ultrasound (GE Vscan Dual Probe) to acquire images of palpable breast lumps of adequate quality for accurate computer analysis. Images from 32 women with 32 breast masses were then analyzed with a triage-CADx system, generating an output of benign or suspicious (biopsy recommended). Triage-CADx outputs were compared with radiologist readings.

Results

The nonradiologists were able to acquire adequate images. Triage by the CADx software was as accurate as assessment by specialist radiologists, with two (100%) of two cancers considered suspicious and 30 (100%) of 30 benign lesions classified as benign.

Conclusion

A portable ultrasound system with CADx software can be successfully used by first-level health care workers to triage palpable breast lumps. These results open up the possibility of implementing practical, cost-effective triage of palpable breast lumps, ensuring that scarce resources can be dedicated to suspicious lesions requiring further workup.

Breast density implications and supplemental screening

Digital breast tomosynthesis (DBT) has been widely implemented in place of 2D mammography, although it is less effective in women with extremely dense breasts. Breast ultrasound detects additional early-stage, invasive breast cancers when combined with mammography; however, its relevant limitations, including the shortage of trained operators, operator dependence and small field of view, have limited its widespread implementation. Automated breast sonography (ABS) is a promising technique but the time to interpret and false-positive rates need to be improved. Supplemental screening with contrast-enhanced magnetic resonance imaging (MRI) in high-risk women reduces late-stage disease; abbreviated MRI protocols may reduce cost and increase accessibility to women of average risk with dense breasts. Contrast-enhanced digital mammography (CEDM) and molecular breast imaging improve cancer detection but require further validation for screening and direct biopsy guidance should be implemented for any screening modality. This article reviews the status of screening women with dense breasts. KEY POINTS: • The sensitivity of mammography is reduced in women with dense breasts. Supplemental screening with US detects early-stage, invasive breast cancers. • Tomosynthesis reduces recall rate and increases cancer detection rate but is less effective in women with extremely dense breasts. • Screening MRI improves early diagnosis of breast cancer more than ultrasound and is currently recommended for women at high risk. Risk assessment is needed, to include breast density, to ascertain who should start early annual MRI screening.

Dedicated Breast Gamma Camera Imaging and Breast PET: Current Status and Future Directions

Recent advances in nuclear medicine instrumentation have led to the emergence of improved molecular imaging techniques to image breast cancer: dedicated gamma cameras using γ-emitting ^99mTc-sestamibi and breast-specific PET cameras using ¹⁸F-fluorodeoxyglucose. This article focuses on the current role of such approaches in the clinical setting including diagnosis, assessing local extent of disease, monitoring response to therapy, and, for gamma camera imaging, possible supplemental screening in women with dense breasts. Barriers to clinical adoption and technologies and radiotracers under development are also discussed.

Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening

PURPOSE

False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish recalled but benign mammography images from negative exams and those with malignancy.

EXPERIMENTAL DESIGN

Deep learning convolutional neural network (CNN) models were constructed to classify mammography images into malignant (breast cancer), negative (breast cancer free), and recalled-benign categories. A total of 14,860 images of 3,715 patients from two independent mammography datasets: Full-Field Digital Mammography Dataset (FFDM) and a digitized film dataset, Digital Dataset of Screening Mammography (DDSM), were used in various settings for training and testing the CNN models. The ROC curve was generated and the AUC was calculated as a metric of the classification accuracy.

RESULTS

Training and testing using only the FFDM dataset resulted in AUC ranging from 0.70 to 0.81. When the DDSM dataset was used, AUC ranged from 0.77 to 0.96. When datasets were combined for training and testing, AUC ranged from 0.76 to 0.91. When pretrained on a large nonmedical dataset and DDSM, the models showed consistent improvements in AUC ranging from 0.02 to 0.05 (all P > 0.05), compared with pretraining only on the nonmedical dataset.

CONCLUSIONS

This study demonstrates that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish recalled-benign images from malignant and negative cases, which may lead to a computerized clinical toolkit to help reduce false recalls.

Use of Breast-Specific PET Scanners and Comparison with MR Imaging

The goals of this article are to discuss the role of breast-specific PET imaging of women with breast cancer, compare the clinical performance of positron emission mammography (PEM) and MR imaging for current indications, and provide recommendations for when women should undergo PEM instead of breast MR imaging.

Comment on Thigpen D. et al. The Role of Ultrasound in Screening Dense Breasts-A Review of the Literature and Practical Solutions for Implementation. Diagnostics 2018, 8, 20

We read with interest the article by Thigpen et al. [1]. With 34 states now having some form of density inform legislation[...].