Radial sclerosing lesions found on core needle biopsy: excision can be safely avoided.

AIMS: Radial sclerosing lesions (RSLs) are benign breast lesions composed of glandular and epithelial proliferations with stellate architecture and fibro-elastotic stroma, which can mimic invasive carcinoma on imaging. Surgical management following a core biopsy diagnosis of RSLs remains controversial. METHODS AND RESULTS: We retrospectively identified core biopsies with RSLs without atypia who underwent subsequent surgical excision between 2015 and 2021. All core biopsy slides were reviewed to confirm the diagnosis. Imaging was reviewed to determine radiological-pathological concordance. An upgrade was defined as invasive carcinoma or ductal carcinoma in situ (DCIS) in the excision. The final cohort consisted of 130 core biopsies from 124 women (median age = 52 years, range = 27-76). The imaging modality was mammogram in 52 (40%) cases, MRI in 52 (40%) and ultrasound in 26 (20%). One hundred and seven (82%) core biopsies were vacuum-assisted and 23 (18%) were ultrasound-guided without vacuum assistance. The median lesion size on imaging was 9 mm (range = 2-41). Overall, two (1%) cases were upgraded at excision, including one microinvasive lobular carcinoma and one 2 mm focus of invasive mammary carcinoma with associated DCIS. In both cases, the upgraded foci of carcinoma were not closely associated with the biopsy site and were considered incidental upgrades. CONCLUSIONS: This study adds to the body of literature supporting observation, rather than routine excision of radial sclerosing lesions without atypia.

A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research.

Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.

Breast MRI in patients with implantable loop recorder: initial experience.

OBJECTIVES: To investigate the feasibility of breast MRI exams and guided biopsies in patients with an implantable loop recorder (ILR) as well as the impact ILRs may have on image interpretation. MATERIALS AND METHODS: This retrospective study examined breast MRIs of patients with ILR, from April 2008 to September 2022. Radiological reports and electronic medical records were reviewed for demographic characteristics, safety concerns, and imaging findings. MR images were analyzed and compared statistically for artifact quantification on the various pulse sequences. RESULTS: Overall, 40/82,778 (0.049%) MRIs during the study period included ILR. All MRIs were completed without early termination. No patient-related or device-related adverse events occurred. ILRs were most commonly located in the left lower-inner quadrant (64.6%). The main artifact was a signal intensity (SI) void in a dipole formation in the ILR bed with or without areas of peripheral high SI. Artifacts appeared greatest in the cranio-caudal axis (p < 0.001), followed by the anterior-posterior axis (p < 0.001), and then the right-left axis. High peripheral rim-like SI artifacts appeared on the post-contrast and subtracted T1-weighted images, mimicking suspicious enhancement. Artifacts were most prominent on diffusion-weighted (p < 0.001), followed by T2-weighted and T1-weighted images. In eight patients, suspicious findings were found on MRI, resulting in four additional malignant lesions. Of six patients with left breast cancer, the tumor was completely visible in five cases and partially obscured in one. CONCLUSION: Breast MRI is feasible and safe among patients with ILR and may provide a significant diagnostic value, albeit with localized, characteristic artifacts. CLINICAL RELEVANCE STATEMENT: Indicated breast MRI exams and guided biopsies can be safely performed in patients with implantable loop recorder. Nevertheless, radiologists should be aware of associated limitations including limited assessment of the inner left breast and pseudo-enhancement artifacts. KEY POINTS: • Breast MRI in patients with an implantable loop recorder is an infrequent, feasible, and safe procedure. • Despite limited breast visualization of the implantable loop recorder bed and characteristic artifacts, MRI depicted additional lesions in 8/40 (20%) of cases, half of which were malignant. • Breast MRI in patients with an implantable loop recorder should be performed when indicated, taking into consideration typical associated artifacts.

Contrast-enhanced mammography in the assessment of residual disease after neoadjuvant treatment.

PURPOSE: To investigate the utility of contrast-enhanced mammography (CEM) as an alternative to breast MRI for the evaluation of residual disease after neoadjuvant treatment (NAT). METHODS: This prospective study enrolled consecutive women undergoing NAT for breast cancer from July 2017-July 2019. Breast MRI and CEM exams performed after completion of NAT were read independently by two breast radiologists. Residual disease and lesion size on MRI and CEM recombined (RI) and low-energy images (LEI) were compared. Histopathology was considered the reference standard. Statistical analysis was performed using McNemar's and Leisenring's tests. Multiple comparison adjustment was made using Bonferroni procedure. Lesion sizes were correlated using Kendall's tau coefficient. RESULTS: There were 110 participants with 115 breast cancers. Residual disease (invasive cancer or ductal carcinoma in situ) was detected in 83/115 (72%) lesions on pathology, 71/115 (62%) on MRI, 55/115 (48%) on CEM RI, and 75/115 (65%) on CEM LEI. When using multiple comparison adjustment, no significant differences were detected between MRI combined with CEM LEI and CEM RI combined with CEM LEI, in terms of accuracy (MRI: 77%, CEM: 72%; p ≥ 0.99), sensitivity (MRI: 88%, CEM: 81%; p ≥ 0.99), specificity (MRI: 47%, CEM: 50%; p ≥ 0.99), PPV (MRI: 81%, CEM: 81%; p ≥ 0.99), or NPV (MRI: 60%, CEM: 50%; p ≥ 0.99). Size correlation between pathology and both MRI combined with CEM LEI and CEM RI combined with CEM LEI was moderate: τ = 0. 36 vs 0.33 (p ≥ 0.99). CONCLUSION: Contrast-enhanced mammography is an acceptable alternative to breast MRI for the detection of residual disease after neoadjuvant treatment.

Association of Breast Cancer Odds with Background Parenchymal Enhancement Quantified Using a Fully Automated Method at MRI: The IMAGINE Study.

Background Background parenchymal enhancement (BPE) at breast MRI has been associated with increased breast cancer risk in several independent studies. However, variability of subjective BPE assessments have precluded its use in clinical practice. Purpose To examine the association between fully objective measures of BPE at MRI and odds of breast cancer. Materials and Methods This prospective case-control study included patients who underwent a bilateral breast MRI examination and were receiving care at one of three centers in the United States from November 2010 to July 2017. Breast volume, fibroglandular tissue (FGT) volume, and BPE were quantified using fully automated software. Fat volume was defined as breast volume minus FGT volume. BPE extent was defined as the proportion of FGT voxels with enhancement of 20% or more. Spearman rank correlation between quantitative BPE extent and Breast Imaging Reporting and Data System (BI-RADS) BPE categories assigned by an experienced board-certified breast radiologist was estimated. With use of multivariable logistic regression, breast cancer case-control status was regressed on tertiles (low, moderate, and high) of BPE, FGT volume, and fat volume, with adjustment for covariates. Results In total, 536 case participants with breast cancer (median age, 48 years [IQR, 43-55 years]) and 940 cancer-free controls (median age, 46 years [IQR, 38-55 years]) were included. BPE extent was positively associated with BI-RADS BPE (rs = 0.54; P < .001). Compared with low BPE extent (range, 2.9%-34.2%), high BPE extent (range, 50.7%-97.3%) was associated with increased odds of breast cancer (odds ratio [OR], 1.74 [95% CI: 1.23, 2.46]; P for trend = .002) in a multivariable model also including FGT volume (OR, 1.39 [95% CI: 0.97, 1.98]) and fat volume (OR, 1.46 [95% CI: 1.04, 2.06]). The association of high BPE extent with increased odds of breast cancer was similar for premenopausal and postmenopausal women (ORs, 1.75 and 1.83, respectively; interaction P = .73). Conclusion Objectively measured BPE at breast MRI is associated with increased breast cancer odds for both premenopausal and postmenopausal women. Clinical trial registration no. NCT02301767 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bokacheva in this issue.

NCCN Guidelines® Insights: Breast Cancer Screening and Diagnosis, Version 1.2023.

The NCCN Guidelines for Breast Cancer Screening and Diagnosis provide health care providers with a practical, consistent framework for screening and evaluating a spectrum of clinical presentations and breast lesions. The NCCN Breast Cancer Screening and Diagnosis Panel is composed of a multidisciplinary team of experts in the field, including representation from medical oncology, gynecologic oncology, surgical oncology, internal medicine, family practice, preventive medicine, pathology, diagnostic and interventional radiology, as well as patient advocacy. The NCCN Breast Cancer Screening and Diagnosis Panel meets at least annually to review emerging data and comments from reviewers within their institutions to guide updates to existing recommendations. These NCCN Guidelines Insights summarize the panel's decision-making and discussion surrounding the most recent updates to the guideline's screening recommendations.

Barriers to Implementation of Contrast-Enhanced Mammography in Clinical Practice: AJR Expert Panel Narrative Review.

Accumulating evidence shows that contrast-enhanced mammography (CEM) has higher diagnostic performance than digital mammography and ultrasound and comparable diagnostic performance to MRI for various indications. CEM also offers certain practical advantages for patients. Nevertheless, the clinical implementation of CEM has been limited because of a range of factors. This AJR Expert Panel Narrative Review explores such factors hindering CEM implementation. These factors include the following: the risks of iodinated contrast media, increased radiation exposure, indications for which CEM is not the preferred test or for which further evidence is needed, workflow adjustments needed when performing CEM examinations, incomplete availability of CEM-guided biopsy systems, and reimbursement challenges. Considerations that currently mitigate or are expected to mitigate these factors are also highlighted.

Yield of flow cytometry in addition to cytology for lymph node sampling in patients with incidental axillary adenopathy without a concurrent diagnosis of primary breast malignancy.

PURPOSE: Non-specific lymphadenopathy is increasingly being reported especially given the COVID-19 vaccination campaign and is a diagnostic dilemma especially in oncology patients. The purpose of this study was to evaluate the diagnostic accuracy and discordance rate between fine-needle aspiration (FNA) cytology and flow cytometry (FC) immunophenotyping in axillary FNA in patients with morphologically abnormal axillary lymph nodes on imaging and no concurrent diagnosis of primary breast malignancy. METHODS: This retrospective study included 222 patients who underwent screening or diagnostic axillary ultrasound that yielded suspicious lymphadenopathy without concurrent or recent prior diagnosis of breast cancer and who had subsequent image-guided axillary FNA and FC. Diagnostic accuracy, sensitivity, specificity, and positive and negative predictive value (PPV and NPV) were reported for FNA with cytology alone, and FC alone, and in combination. Discordance rate between FNA cytology and FC was assessed. Discordant cases were evaluated with histology or clinical and imaging follow-up. RESULTS: Diagnostic sensitivity, specificity, PPV, NPV, and diagnostic accuracy were 88%, 92%, 77%, 96%, and 91%, for FNA alone, 98%, 98%, 92%, 99%, and 98% for FC alone, and 100%, 92%, 79%, 100%, and 94% when combined. The overall discordance rate between FNA and FC was 7% (16/222). 7/16 (44%) patients with discordant results were diagnosed with lymphoma, while 9/16 (56%) patients with discordant results had benign findings. CONCLUSION: With a diagnostic accuracy of 91%, FNA with cytology is sufficient to screen patients with indeterminate and incidental lymphadenopathy. Flow cytometry could be initially deferred in patients with low pretest probability of lymphoma.

Comparison of False-Positive Versus True-Positive Findings on Contrast-Enhanced Digital Mammography.

BACKGROUND. Contrast-enhanced digital mammography (CEDM) has been shown to outperform standard mammography while performing comparably to contrast-enhanced MRI. OBJECTIVE. The purpose of our study was to compare imaging characteristics of false-positive and true-positive findings on CEDM. METHODS. This retrospective study included women who underwent baseline screening CEDM between January 2013 and December 2018 assessed as BI-RADS category 0, 3, 4, or 5 and who underwent biopsy with histopathologic diagnosis or had a 2-year imaging follow-up. Lesion characteristics were extracted from CEDM reports. A true-positive finding was defined as a lesion in which biopsy yielded malignancy. A false-positive finding was defined as a lesion in which biopsy yielded benign or benign high-risk pathology or in which 2-year imaging follow-up was negative. RESULTS. Of 157 patients (median age, 52 years), 24 had a total of 26 true-positive lesions, and 133 had a total of 147 false-positive lesions. Of the 26 true-positive lesions, one (4%) exhibited only a mammographic finding on low-iodine images, 13 (50%) exhibited only a contrast finding on iodine images, and 12 (46%) exhibited both a mammographic finding on low-energy images and a contrast finding on iodine images. A true-positive result was more likely (p = .02) for lesions present on both low-energy images and iodine images (31%) than on low-energy images only (4%) or iodine images only (12%). Among lesions present on both low-energy and iodine images, a true-positive result was more likely (p < .001) when the type of mammographic finding was an asymmetry (46%) or calcification (80%) than a mass (11%) or distortion (0%). A true-positive result was more likely (p = .01) among those with, versus those without, an ultrasound correlate (36% vs 9%) and also was more likely (p = .02) among those with, versus those without, an MRI correlate (18% vs 2%). Of 25 false-positive calcifications, 24 had no associated mammographic enhancement; of five true-positive calcifications, four had mammographic enhancement. CONCLUSION. A low-energy mammographic finding with associated enhancement or a finding with a sonographic or MRI correlate predicts a true-positive result. Calcifications with associated enhancement had a high malignancy rate. Nonetheless, half of true-positive lesions enhanced on iodine images without a mammographic finding on low-energy images. CLINICAL IMPACT. These observations inform radiologists' management of abnormalities detected on screening CEDM.

MRI Screening of BRCA Mutation Carriers: Comparison of Standard Protocol and Abbreviated Protocols With and Without T2-Weighted Images.

BACKGROUND. Increasing evidence supports the role of abbreviated MRI protocols for breast cancer detection. However, abbreviated protocols have been poorly studied in patients who are BRCA1 or BRCA2 mutation carriers. Furthermore, the need for T2-weighted sequences in abbreviated protocols remains controversial. OBJECTIVE. The purpose of this study was to compare, in the evaluation of patients with BRCA mutations, the diagnostic performance of a standard full breast MRI protocol with the performance of abbreviated protocols that included and did not include a T2-weighted sequence. METHODS. This retrospective study included 292 patients (mean age, 47.9 years) who were BRCA1 or BRCA2 mutation carriers who underwent 427 screening breast MRI examinations according to a standard full protocol who could be classified as having benign (n = 407) or malignant (n = 20) findings based on histopathology or imaging follow-up. Four readers independently assessed examinations in three separate sessions (theoretic abbreviated protocol, which included the first postcontrast acquisition; theoretic abbreviated protocol with addition of a T2-weighted sequence; and the standard full protocol) and assigned BI-RADS categories. Categories 3-5 were considered to represent positive examinations. Interreader agreement was assessed, and diagnostic performance was compared by use of pooled reader data. RESULTS. Interreader agreement on BI-RADS category, expressed as kappa values, was 0.55 for the standard, 0.45 for the abbreviated, and 0.57 for the abbreviated plus T2-weighted protocols. Pooled sensitivity was 94% for the standard, 92% for the abbreviated, and 90% for the abbreviated plus T2-weighted protocols (all p > .001). Pooled specificity was 80% for the standard, 71% for the abbreviated, and 83% for the abbreviated plus T2-weighted protocols (p < .001 for abbreviated plus T2-weighted compared with both standard and abbreviated). Pooled PPV was 19% for the standard, 14% for the abbreviated, and 20% for the abbreviated plus T2-weighted protocols (p < .001 for abbreviated compared with both standard and abbreviated). Pooled NPV was 100% for the standard, 99% for the abbreviated, and 99% for the abbreviated plus T2-weighted (all p > .001) protocols. Pooled accuracy was 80% for the standard, 73% for the abbreviated, and 83% for the abbreviated plus T2-weighted protocols (p < .001 for abbreviated compared with both standard and abbreviated plus T2-weighted). CONCLUSION. The abbreviated protocol without T2-weighted imaging had suboptimal performance. However, addition of the T2-weighted sequence yielded comparable sensitivity and accuracy and a small increase in specificity compared with the full protocol. CLINICAL IMPACT. The findings support implementation of abbreviated MRI with T2-weighted imaging for breast cancer screening of patients with BRCA mutations.

Diagnostic value of radiomics and machine learning with dynamic contrast-enhanced magnetic resonance imaging for patients with atypical ductal hyperplasia in predicting malignant upgrade.

PURPOSE: To investigate whether radiomics features extracted from magnetic resonance imaging (MRI) of patients with biopsy-proven atypical ductal hyperplasia (ADH) coupled with machine learning can differentiate high-risk lesions that will upgrade to malignancy at surgery from those that will not, and to determine if qualitatively and semi-quantitatively assessed imaging features, clinical factors, and image-guided biopsy technical factors are associated with upgrade rate. METHODS: This retrospective study included 127 patients with 139 breast lesions yielding ADH at biopsy who were assessed with multiparametric MRI prior to biopsy. Two radiologists assessed all lesions independently and with a third reader in consensus according to the BI-RADS lexicon. Univariate analysis and multivariate modeling were performed to identify significant radiomic features to be included in a machine learning model to discriminate between lesions that upgraded to malignancy on surgery from those that did not. RESULTS: Of 139 lesions, 28 were upgraded to malignancy at surgery, while 111 were not upgraded. Diagnostic accuracy was 53.6%, specificity 79.2%, and sensitivity 15.3% for the model developed from pre-contrast features, and 60.7%, 86%, and 22.8% for the model developed from delta radiomics datasets. No significant associations were found between any radiologist-assessed lesion parameters and upgrade status. There was a significant correlation between the number of specimens sampled during biopsy and upgrade status (p = 0.003). CONCLUSION: Radiomics analysis coupled with machine learning did not predict upgrade status of ADH. The only significant result from this analysis is between the number of specimens sampled during biopsy procedure and upgrade status at surgery.

Contrast-enhanced Mammography: State of the Art.

Contrast-enhanced mammography (CEM) has emerged as a viable alternative to contrast-enhanced breast MRI, and it may increase access to vascular imaging while reducing examination cost. Intravenous iodinated contrast materials are used in CEM to enhance the visualization of tumor neovascularity. After injection, imaging is performed with dual-energy digital mammography, which helps provide a low-energy image and a recombined or iodine image that depict enhancing lesions in the breast. CEM has been demonstrated to help improve accuracy compared with digital mammography and US in women with abnormal screening mammographic findings or symptoms of breast cancer. It has also been demonstrated to approach the accuracy of breast MRI in preoperative staging of patients with breast cancer and in monitoring response after neoadjuvant chemotherapy. There are early encouraging results from trials evaluating CEM in the screening of women who are at an increased risk of breast cancer. Although CEM is a promising tool, it slightly increases radiation dose and carries a small risk of adverse reactions to contrast materials. This review details the CEM technique, diagnostic and screening uses, and future applications, including artificial intelligence and radiomics.

Diagnostic value of diffusion-weighted imaging with synthetic b-values in breast tumors: comparison with dynamic contrast-enhanced and multiparametric MRI.

OBJECTIVES: To assess DWI for tumor visibility and breast cancer detection by the addition of different synthetic b-values. METHODS: Eighty-four consecutive women who underwent a breast-multiparametric-MRI (mpMRI) with enhancing lesions on DCE-MRI (BI-RADS 2-5) were included in this IRB-approved retrospective study from September 2018 to March 2019. Three readers evaluated DW acquired b-800 and synthetic b-1000, b-1200, b-1500, and b-1800 s/mm2 images for lesion visibility and preferred b-value based on lesion conspicuity. Image quality (1-3 scores) and breast composition (BI-RADS) were also recorded. Diagnostic parameters for DWI were determined using a 1-5 malignancy score based on qualitative imaging parameters (acquired + preferred synthetic b-values) and ADC values. BI-RADS classification was used for DCE-MRI and quantitative ADC values + BI-RADS were used for mpMRI. RESULTS: Sixty-four malignant (average = 23 mm) and 39 benign (average = 8 mm) lesions were found in 80 women. Although b-800 achieved the best image quality score, synthetic b-values 1200-1500 s/mm2 were preferred for lesion conspicuity, especially in dense breast. b-800 and synthetic b-1000/b-1200 s/mm2 values allowed the visualization of 84-90% of cancers visible with DCE-MRI performing better than b-1500/b-1800 s/mm2. DWI was more specific (86.3% vs 65.7%, p < 0.001) but less sensitive (62.8% vs 90%, p < 0.001) and accurate (71% vs 80.7%, p = 0.003) than DCE-MRI for breast cancer detection, where mpMRI was the most accurate modality accounting for less false positive cases. CONCLUSION: The addition of synthetic b-values enhances tumor conspicuity and could potentially improve tumor visualization particularly in dense breast. However, its supportive role for DWI breast cancer detection is still not definite. KEY POINTS: • The addition of synthetic b-values (1200-1500 s/mm2) to acquired DWI afforded a better lesion conspicuity without increasing acquisition time and was particularly useful in dense breasts. • Despite the use of synthetic b-values, DWI was less sensitive and accurate than DCE-MRI for breast cancer detection. • A multiparametric MRI modality still remains the best approach having the highest accuracy for breast cancer detection and thus reducing the number of unnecessary biopsies.

Contrast-Enhanced Digital Mammography Screening for Intermediate-Risk Women With a History of Lobular Neoplasia.

OBJECTIVE. The objective of this study was to assess to the role of contrast-enhanced digital mammography (CEDM) as a screening tool in women at intermediate risk for developing breast cancer due to a personal history of lobular neoplasia without additional risk factors. MATERIALS AND METHODS. In this institutional review board-approved, observational, retrospective study, we reviewed our radiology department database to identify patients with a personal history of breast biopsy yielding lobular neoplasia who underwent screening CEDM at our institution between December 2012 and February 2019. A total of 132 women who underwent 306 CEDM examinations were included. All CEDM examinations were interpreted by dedicated breast imaging radiologists in conjunction with a review of the patient's clinical history and available prior breast imaging. In statistical analysis, sensitivity, specificity, NPV, positive likelihood ratio, and accuracy of CEDM in detecting cancer were determined, with pathology or 12-month imaging follow-up serving as the reference standard. RESULTS. CEDM detected cancer in six patients and showed an overall sensitivity of 100%, specificity of 88% (95% CI, 84-92%), NPV of 100%, and accuracy of 88% (95% CI, 84-92%). The positive likelihood ratio of 8.33 suggested that CEDM findings are 8.3 times more likely to be positive in an individual with breast cancer when compared with an individual without the disease. CONCLUSION. CEDM shows promise as a breast cancer screening examination in patients with a personal history of lobular neoplasia. Continued investigation with a larger patient population is needed to determine the true sensitivity and positive predictive value of CEDM for these patients.

Utility of Targeted Ultrasound to Predict Malignancy Among Lesions Detected on Contrast-Enhanced Digital Mammography.

BACKGROUND. Targeted ultrasound (US) can be performed to characterize and potentially biopsy areas of enhancement detected on contrast-enhanced mammography (CEM). OBJECTIVE. The purpose of this study was to assess the utility of targeted US in predicting malignancy of lesions with indeterminate or suspicious enhancement on CEM. METHODS. One thousand consecutive CEM examinations with same-day targeted breast US at one institution between October 2013 and May 2018 were retrospectively reviewed. All patients with indeterminate or suspicious enhancement detected on CEM that underwent US evaluation were included. Patients with palpable or symptomatic lesions, those with suspicious findings on low-energy mammograms or images obtained with another modality, and those with less than 1 year of follow-up were excluded. Medical records, imaging, and pathology data were reviewed. Histopathologic analysis was used as the reference standard for biopsied lesions, and follow-up imaging was used for unbiopsied lesions. Associations between pathologic diagnosis, presence of a US correlate, and lesion characteristics were assessed by Fisher exact, chi-square, and Wilcox-on rank sum tests. RESULTS. Among 153 enhancing lesions detected on CEM in 144 patients, 47 (31%) had a US correlate. The frequency of a correlate between CEM and US was significantly higher among enhancing masses (28/43 [65%]) than among lesions exhibiting nonmass enhancement (19/110 [17%]) (p < .001). The likelihood of malignancy was significantly greater among lesions with a US correlate (12/47 [26%]) than among those without a US correlate (11/106 [10%]) (p = .03), and among mass lesions (11/43 [26%]) than among nonmass lesions (12/110 [11%]) (p = .04). The PPV of US-guided biopsy after CEM-directed US was 32%. CONCLUSION. Enhancing CEM-detected lesions that have a US correlate are more likely to be malignant and can be evaluated with US-guided biopsy to obviate additional breast MRI. CLINICAL IMPACT. CEM-directed US of enhancing lesions is useful given that lesions with a US correlate are more likely to be malignant and can be used as targets for US-guided biopsy until a CEM biopsy system becomes commercially available.

MRI background parenchymal enhancement, fibroglandular tissue, and mammographic breast density in patients with invasive lobular breast cancer on adjuvant endocrine hormonal treatment: associations with survival.

BACKGROUND: To investigate if baseline and/or changes in contralateral background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) measured on magnetic resonance imaging (MRI) and mammographic breast density (MD) can be used as imaging biomarkers for overall and recurrence-free survival in patients with invasive lobular carcinomas (ILCs) undergoing adjuvant endocrine treatment. METHODS: Women who fulfilled the following inclusion criteria were included in this retrospective HIPAA-compliant IRB-approved study: unilateral ILC, pre-treatment breast MRI and/or mammography from 2000 to 2010, adjuvant endocrine treatment, follow-up MRI, and/or mammography 1-2 years after treatment onset. BPE, FGT, and mammographic MD of the contralateral breast were independently graded by four dedicated breast radiologists according to BI-RADS. Associations between the baseline levels and change in levels of BPE, FGT, and MD with overall survival and recurrence-free survival were assessed using Kaplan-Meier survival curves and Cox regression analysis. RESULTS: Two hundred ninety-eight patients (average age = 54.1 years, range = 31-79) fulfilled the inclusion criteria. The average follow-up duration was 11.8 years (range = 2-19). Baseline and change in levels of BPE, FGT, and MD were not significantly associated with recurrence-free or overall survival. Recurrence-free and overall survival were affected by histological subtype (p < 0.0001), number of metastatic axillary lymph nodes (p < 0.0001), age (p = 0.01), and adjuvant endocrine treatment duration (p < 0.001). CONCLUSIONS: Qualitative evaluation of BPE, FGT, and mammographic MD changes cannot predict which patients are more likely to benefit from adjuvant endocrine treatment.

Obesity-Associated Extracellular Matrix Remodeling Promotes a Macrophage Phenotype Similar to Tumor-Associated Macrophages.

Obesity is associated with adipose inflammation, defined by macrophages encircling dead adipocytes, as well as extracellular matrix (ECM) remodeling and increased risk of breast cancer. Whether ECM affects macrophage phenotype in obesity is uncertain. A better understanding of this relationship could be strategically important to reduce cancer risk or improve outcomes in the obese. Using clinical samples, computational approaches, and in vitro decellularized ECM models, this study quantified the relative abundance of pro-inflammatory (M1) and anti-inflammatory (M2) macrophages in human breast adipose tissue, determined molecular similarities between obesity and tumor-associated macrophages, and assessed the regulatory effect of obese versus lean ECM on macrophage phenotype. Our results suggest that breast adipose tissue contains more M2- than M1-biased macrophages across all body mass index categories. Obesity further increased M2-biased macrophages but did not affect M1-biased macrophage density. Gene Set Enrichment Analysis suggested that breast tissue macrophages from obese versus lean women are more similar to tumor-associated macrophages. These changes positively correlated with adipose tissue interstitial fibrosis, and in vitro experiments indicated that obese ECM directly stimulates M2-biased macrophage functions. However, mammographic density cannot be used as a clinical indicator of these changes. Collectively, these data suggest that obesity-associated interstitial fibrosis promotes a macrophage phenotype similar to tumor-associated macrophages, which may contribute to the link between obesity and breast cancer.

Do Body Mass Index and Breast Density Impact Cancer Risk Among Women with Lobular Carcinoma In Situ?

PURPOSE: Both body mass index (BMI) and breast density impact breast cancer risk in the general population. Whether obesity and density represent additive risk factors in women with lobular carcinoma in situ (LCIS) is unknown. METHODS: Patients diagnosed with LCIS from 1988 to 2017 were identified from a prospectively maintained database. BMI was categorized by World Health Organization classification. Density was captured as the mammographic Breast Imaging Reporting and Data System (BIRADS) value. Other covariates included age at LCIS diagnosis, menopausal status, family history, chemoprevention, and prophylactic mastectomy. Cancer-free probability was estimated using the Kaplan-Meier method, and Cox regression models were used for univariable and multivariable analyses. RESULTS: A total of 1222 women with LCIS were identified. At a median follow-up of 7 years, 179 women developed breast cancer (121 invasive, 58 ductal carcinoma in situ); 5- and 10-year cumulative incidences of breast cancer were 10% and 17%, respectively. In multivariable analysis, increased breast density (BIRADS C/D vs. A/B) was significantly associated with increased hazard of breast cancer (hazard ratio [HR] 2.42, 95% confidence interval [CI] 1.52-3.88), whereas BMI was not. On multivariable analysis, chemoprevention use was associated with a significantly decreased hazard of breast cancer (HR 0.49, 95% CI 0.29-0.84). Exploratory analyses did not demonstrate significant interaction between BMI and menopausal status, BMI and breast density, BMI and chemoprevention use, or breast density and chemoprevention. CONCLUSIONS: Breast cancer risk among women with LCIS is impacted by breast density. These results aid in personalizing risk assessment among women with LCIS and highlight the importance of chemoprevention counseling for risk reduction.

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers.

OBJECTIVES: To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. METHODS: In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. RESULTS: Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). CONCLUSIONS: Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. KEY POINTS: • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone.

Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results.

BACKGROUND: To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. METHODS: One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference. RESULTS: In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%). CONCLUSIONS: In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.