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Background: To provide a systematic review and meta-analysis that evaluates the diagnostic accuracy of contrast-enhanced mammography (CEM) compared to standard contrast-enhanced breast magnetic resonance imaging (breast MRI). Like breast MRI, CEM enables tumour visualization by contrast accumulation. CEM seems to be a viable substitute for breast MRI.Methods: This systematic search assessed the diagnostic accuracy of these techniques in women with suspicious breast lesions on prior imaging or physical examination, who have undergone both breast MRI and CEM. CEM had to be performed on a commercially available system. The MRI sequence parameters had to be described sufficiently to ensure that standard breast MRI sequence protocols were used. Pooled values of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR), were estimated using bivariate mixed-effects logistic regression modeling. Hierarchical summary receiver operating characteristic curves for CEM and breast MRI were also constructed.Results: Six studies (607 patients with 775 lesions) met the predefined inclusion criteria. Pooled sensitivity was 96% for CEM and 97% for breast MRI. Pooled specificity was 77% for both modalities. DOR was 79.5 for CEM and 122.9 for breast MRI. Between-study heterogeneity expressed as the I2-index was substantial with values over 80%.Conclusion: Pooled sensitivity was high for both CEM and breast MRI, with moderate specificity. The pooled DOR estimates, however, indicate higher overall diagnostic performance of breast MRI compared to CEM. Nonetheless, current scientific evidence is too limited to prematurely discard CEM as an alternative for breast MRI.

Accumulating evidence shows that contrast-enhanced mammography (CEM) has higher diagnostic performance than digital mammography and ultrasound, and comparable diagnostic performance as MRI, for various indications. CEM also offers certain practical advantages for patients. Nevertheless, the clinical implementation of CEM has been limited due to a range of factors. This AJR Expert Panel Narrative Review explores such factors hindering CEM implementation. These include: 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 when performing CEM examinations, incomplete availability of CEM-guided biopsy systems, and reimbursement challenges. Considerations that currently or are expected to mitigate these factors are also highlighted.

Contrast-enhanced mammography (CEM) may provide an alternative to magnetic resonance imaging as a diagnostic exam in women with known or suspected breast cancer or as a screening exam in women at increased risk of breast cancer. Women with breast augmentation, either for oncologic or cosmetic reasons, may fall into this increased risk population and need safe and effective screening and diagnostic imaging tools. Here, we present our clinical practice data in order to demonstrate the feasibility of CEM in women with breast implants. An institutional review board-approved, Health Insurance Portability and Accountability Act-compliant, retrospective review of our tertiary cancer center's database yielded 104 women with breast implants who underwent 198 CEM exams from November 2014 to March 2020. All 198/198 (100%) exams were successfully completed in 104 women. Exam indications included: 174/198 (88%) screening due to increased risk, 10/198 (5%) to evaluate a palpable abnormality, 9/198 (5%) to evaluate disease extent following neoadjuvant chemotherapy for a known breast malignancy, and 5/198 (3%) for a 6-month follow-up. 97/104 (93%) women had dense breasts. Routine and implant-displaced low-energy views were obtained with contrast-enhanced images obtained on displaced views for all patients. 197/198 (99.5%) exams yielded no complications. In one exam, the patient experienced mild vasovagal symptoms following the administration of contrast. In conclusion, it is feasible to utilize CEM in both diagnostic and screening capacities in women with breast implants.

Background: The Lobular Breast Cancer Alliance (LBCA) is an advocacy organization guided by an expert advisory board. Since it launched in 2017, LBCA has been committed to raising awareness of the distinct aspects of invasive lobular carcinoma (ILC) and promoting ILC research. While ILC is the second most common histological breast cancer type representing 15% of breast cancer diagnoses in the United States annually, it is understudied compared to the more common invasive ductal carcinoma (IDC). ILC tumor cell density and lack of desmoplastic stromal reaction can pose challenges to detection by physical examination and mammography. Many studies have shown decreased accuracy of breast imaging tools for ILC, and those with ILC are often diagnosed at later stages and require more extensive surgery. We sought to hear from members of the LBCA community with ILC about their experiences with detection of their tumors. Methods: LBCA conducted an anonymous survey online. A link to the survey on the SurveyMonkey platform was made available for two weeks in June. It was sent to LBCA newsletter subscribers and posted to the LBCA website and ILC social media groups. The survey questions included stage and size of tumors at initial diagnosis and after surgery, and questions related to imaging for advanced ILC. It specifically asked whether and what type of mammogram initially detected the ILC. Results: Within 24 hours over 1,000 people responded, and a total of 1,476 people previously diagnosed with ILC responded by survey close. The majority of participants, 1,052 (72%), were from the US, followed by participants from 35 additional countries. 170 (12%) reported having metastatic ILC. 1,035 (70%) of respondents were under 60 at diagnosis. Of respondents reporting tumor size and stage, 936 (67%) of respondents indicated having a tumor larger than 2cm at diagnosis, and 897 (63%) reported stage 2 or higher, including 73 (5%) reporting stage 4 at initial diagnosis. In the subset of participants initially diagnosed with stage 2 or higher, 410 (46%) of respondents reported the tumor was not detected by mammogram. In the subset of participants who noticed changes in their breasts prior to the diagnosis, 521 (79%) had tumors larger than 2cm, and 490 (74%) were diagnosed at stage 2 or higher. Of the 1,241 respondents who had no preoperative therapy, 531 (43%) had tumors larger than initially seen on imaging and/or 321 (26%) had more tumors than initially found on imaging. Discussion: To the best of our knowledge this represents the largest survey seeking to understand patients’ experience with imaging to diagnose or visualize ILC. The results from this community survey of patients diagnosed with invasive lobular carcinoma confirmed patients’ perceptions of mammography as an imperfect screening tool to detect ILC. Of note were the findings that mammography failed to visualize 46% of ILC cases at stage 2 or higher, and the majority of respondents had tumors diagnosed/detected at stage 2 or higher and at sizes greater than 2cm. Moreover, participants often reported that the actual tumor size on the final pathology specimen was larger than seen on diagnostic imaging, and a significant number of respondents were diagnosed de novo metastatic. In summary, our findings affirm patients’ perception about the limitations of diagnostic and monitoring imaging of ILC. Our impressively large response also demonstrates the urgent need from patients’ perspectives for more research on better methods for detecting and monitoring ILC to improve outcomes for patients diagnosed with lobular disease. Citation Format: Laurie B Hutcheson, Janice Axelrod, Colleen L Fitzwater, Maxine S Jochelson, Gitte H Joergensen, Theresa Langdon, Julia K Levine, Otto Metzger, Mason Mitchell-Daniels, Barbara F Neilsen. Imaging and invasive lobular carcinoma: A survey study conducted by the lobular breast cancer alliance [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-15-03.

Microcalcifications, primarily biogenic apatite, occur in cancerous and benign breast pathologies and are key mammographic indicators. Outside the clinic, numerous microcalcification compositional metrics (e.g., carbonate and metal content) are linked to malignancy, yet microcalcification formation is dependent on microenvironmental conditions, which are notoriously heterogeneous in breast cancer. We interrogate multiscale heterogeneity in 93 calcifications from 21 breast cancer patients using an omics-inspired approach: For each microcalcification, we define a “biomineralogical signature” combining metrics derived from Raman microscopy and energy-dispersive spectroscopy. We observe that (i) calcifications cluster into physiologically relevant groups reflecting tissue type and local malignancy; (ii) carbonate content exhibits substantial intratumor heterogeneity; (iii) trace metals including zinc, iron, and aluminum are enhanced in malignant-localized calcifications; and (iv) the lipid-to-protein ratio within calcifications is lower in patients with poor composite outcome, suggesting that there is potential clinical value in expanding research on calcification diagnostic metrics to include “mineral-entrapped” organic matrix.

Breast cancer screening recommendations for transgender and gender nonconforming individuals are based on the sex assigned at birth, risk factors, and use of exogenous hormones. Insufficient evidence exists to determine whether transgender people undergoing hormone therapy have an overall lower, average, or higher risk of developing breast cancer compared to birth-sex controls. Furthermore, there are no longitudinal studies evaluating the efficacy of breast cancer screening in the transgender population. In the absence of definitive data, current evidence is based on data extrapolated from cisgender studies and a limited number of cohort studies and case reports published on the transgender community. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Knowledge of axillary nodal status is highly important for correct staging and treatment planning in patients with breast cancer. Axillary US is a recognized highly specific and cost-effective tool for assessing nodal status and guiding appropriate treatment. Axillary US imaging with US-guided biopsy is routinely performed throughout the world. However, because of recent developments in the surgical management of the axilla in patients with newly diagnosed breast cancer (American College of Surgeons Oncology Group [ACOSOG] Z0011 trial) and in patients with breast cancer receiving neoadjuvant systemic therapy (ACOSOG Z1071, SENTinel NeoAdjuvant [SENTINA] and Sentinel Node biopsy aFter NeoAdjuvant Chemotherapy [SN FNAC] trials), some have questioned the utility of axillary US for nodal staging. Here, we review the evidence to date supporting the additional value of axillary US for patients with breast cancer. Nodal US in patients with newly diagnosed breast cancer is useful for staging; in a significant proportion of patients, nodal US identifies additional axillary level II or level III nodal disease, which allows for appropriate treatment of disease. Furthermore, ongoing clinical trials may show that axillary surgery can be omitted in patients with negative findings on axillary US. In patients with lymph node–positive disease undergoing neoadjuvant systemic therapy, nodal US can guide the approach to axillary surgery. A more personalized patient approach, taking into the account tumor biology, among other factors, may help to mitigate the controversy surrounding the role of axillary US in breast cancer patients.

Axillary lymph node status in patients with breast cancer is an important prognostic indicator and often guides vital treatment decisions. Although in many cases imaging plays a crucial role in the assessment of the axilla, it is essential that targeted axillary US and/or image-guided biopsy of an axillary lymph node be performed by the radiologist only when clinically appropriate. This decision-making process requires multidisciplinary communication and collaboration among radiologists, surgeons, medical oncologists, and radiation oncologists. Our goal is to review axillary imaging for patients with breast cancer in two distinct clinical settings, the patient with newly diagnosed breast cancer who meets American College of Surgeons Oncology Group Z0011 criteria and patients undergoing neoadjuvant chemotherapy, with a specific focus on potential reasons to not perform axillary US and/or image-guided biopsy in these settings.

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.

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. (C) RSNA, 2021

Background: The Lobular Breast Cancer Alliance Inc. (LBCA) is committed to raising awareness of the distinctive characteristics of invasive lobular carcinoma (ILC) and promoting and funding ILC research. Comprising 15% of all breast cancers, ILC tumors often form in a linear, sometimes diffuse fashion both within the breast and in metastatic sites, making them difficult to diagnose, monitor, and treat. LBCA surveyed individuals living with metastatic ILC (mILC) about their experiences with detection and monitoring of mILC. Methods: LBCA conducted an anonymous, online survey of persons living with mILC using SurveyMonkey. The survey was shared with LBCA newsletter subscribers, sister organizations, and via social media. Survey questions asked about metastatic site locations, imaging and monitoring modalities, and patient experience with disease progression and clinician discussions about mILC. An independent IRB review determined the study was exempt from full IRB review. Results: 241 people living with mILC completed the survey. 77% were from the US and Canada. 71% were between 35-64 years of age. 41% had been diagnosed with de novo metastatic ILC. Bone was the most common site of initial metastasis with 75% diagnosed de novo (DN) and 59% diagnosed with a distant recurrence (DR). GI metastases (including metastases to stomach, colon, bowel, peritoneum, or rectum) were reported by 11% of the DN and 14% of the DR groups, respectively. Unusual sites for breast cancer metastasis (to genitourinary organs, eye, or skin) were reported by 11% of the DN and 16% of the DR groups. Metastatic progression was reported by 47% of respondents including to bone (42%), to the liver (22%), and 40% reported progression within the initial metastatic site. 36% of individuals with DN mILC reported progression as compared to 54% among those with a DR. Both groups reported living with mILC for similar durations (on average 3.9 years for DN; 3.3 for DR). 36% of respondents reported that at least one imaging modality failed to visualize one or more of their metastatic sites at initial diagnosis of mILC. 54% of respondents with bone metastases and 19% of those with GI metastases indicated their metastases had not been visualized by standard imaging modalities. 48% of all surveyed stated their mILC was an unexpected or an incidental finding made during another medical procedure, of those, 64% were bone metastases. 25% of respondents whose metastases progressed indicated imaging failed to detect one or more of their sites of progression. In both the DN and DR groups, the most frequently utilized tests and/or procedures used to monitor for progression or changes in metastases were routine blood and tumor marker tests. Respondents with DN mILC reported an average 12 months between first report of symptoms and mILC diagnosis. Those with DR reported an average 8 months between symptom reporting and diagnosis. 42% of all respondents reported that a doctor had told them what symptoms to report at any time. 58% of respondents reported feeling that non-oncologists caring for them (primarily PCPs, radiologists, and gastroenterologists) needed to be better informed about ILC. Conclusion: Surveyed individuals confirmed the perception that mILC can occur in unique locations and be difficult to diagnose and that mILC may be challenging to monitor, and standard surveillance methods may fail to visualize mILC. While a large percent of respondents reported that their mILC diagnoses were unexpected or incidental findings during another medical procedure, this may be due to different understandings of “incidental.” This and the fact that more respondents with DR mILC reported progression than those with DN mILC warrant further study. Citation Format: Laurie B. Hutcheson, Janice Axelrod, Ann Camden, Donna J. Charlevoix, Tracy A. Cushing, Maxine S. Jochelson, Megan L. Kruse, Theresa Langdon, Julia K. Levine, Christine McKay, Otto Metzger, Mason Mitchell-Daniels, Jason Mouabbi, Barbara F. Neilsen. Lobular Breast Cancer Alliance Inc. Survey of Individuals with Metastatic Invasive Lobular Carcinoma [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-05-50.

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.

To assess whether no enhancement on pre-treatment MRI can rule out malignancy of additional US mass(es) initially assessed as BI-RADS 3 or 4 in women with newly diagnosed breast cancer. This retrospective study included consecutive women from 2010–2018 with newly diagnosed breast cancer; at least one additional breast mass (distinct from index cancer) assigned a BI-RADS 3 or 4 on US; and a bilateral contrast-enhanced breast MRI performed within 90 days of US. All malignant masses were pathologically proven; benign masses were pathologically proven or defined as showing at least 2 years of imaging stability. Incidence of malignant masses and NPV were calculated on a per-patient level using proportions and exact 95% CIs. In 230 patients with 309 additional masses, 140/309 (45%) masses did not enhance while 169/309 (55%) enhanced on MRI. Of the 140 masses seen in 105 women (mean age, 54 years; range 28–82) with no enhancement on MRI, all had adequate follow-up and 140/140 (100%) were benign, of which 89/140 (63.6%) were pathologically proven and 51/140 (36.4%) demonstrated at least 2 years of imaging stability. Pre-treatment MRI demonstrating no enhancement of US mass correlate(s) had an NPV of 100% (95% CI 96.7–100.0). All BI-RADS 3 and 4 US masses with a non-enhancing correlate on pre-treatment MRI were benign. The incorporation of MRI, when ordered by the referring physician, may decrease unnecessary follow-up imaging and/or biopsy if the initial US BI-RADS assessment and management recommendation were to be retrospectively updated. • Of 309 BI-RADS 3 or 4 US masses with a corresponding mass on MRI, 140/309 (45%) demonstrated no enhancement whereas 169/309 (55%) demonstrated enhancement • All masses classified as BI-RADS 3 or 4 on US without enhancement on MRI were benign • MRI can rule out malignancy in non-enhancing US masses with an NPV of 100%

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 p p = 0.01), and adjuvant endocrine treatment duration (p Conclusions Qualitative evaluation of BPE, FGT, and mammographic MD changes cannot predict which patients are more likely to benefit from adjuvant endocrine treatment.

ObjectivesTo assess DWI for tumor visibility and breast cancer detection by the addition of different synthetic b-values.MethodsEighty-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/mm2images 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.ResultsSixty-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/mm2were preferred for lesion conspicuity, especially in dense breast. b-800 and synthetic b-1000/b-1200 s/mm2values 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.ConclusionThe 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.

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.

This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.

Simple Summary To our knowledge, this is the first study assessing radiomics coupled with machine learning from MRI-derived features to predict PD-L1 expression status in biopsy-proven triple negative breast cancers and comparing the performance of this approach with the performance of qualitative assessment by two radiologists. This pilot study shows that radiomics analysis coupled with machine learning of DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment. This technique could also be used to monitor PD-L1 expression, as it can vary over time and between different regions of the tumor, thus avoiding repeated biopsies. Abstract The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1− patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1− tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.

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.

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.

Contrast-enhanced mammography (CEM) is a promising vascular-based breast imaging technique with high diagnostic performance in detecting breast cancer. Dual-energy acquisition using low and high energy x-ray spectra following intravenous iodinated contrast injection provides both anatomic and functional information in the same examination. The low-energy images are equivalent to standard digital mammography and the post-processed recombined images depict enhancement analogous to contrast-enhanced breast magnetic resonance imaging (MRI). Thus, CEM has the potential to detect abnormal morphologic features as well as neovascularity associated with breast cancer. Since its emergence in 2011, CEM has consistently demonstrated superior performance compared with standard mammography and mammography plus ultrasound, particularly in women with dense breasts, with high sensitivity approaching that of MRI, supporting its use as a cost-effective diagnostic and screening tool. CEM has been primarily used in the diagnostic setting to evaluate patients with screening abnormalities or with symptomatic breasts, to perform preoperative staging of newly diagnosed breast cancer, and to evaluate response to neoadjuvant chemotherapy. More recently, CEM has been performed to screen women who have an intermediate to high lifetime risk of developing breast cancer. In addition to its high diagnostic performance, CEM is less expensive and more accessible than MRI and potentially better tolerated by patients. Minor drawbacks to CEM include a slightly increased radiation dose compared with standard mammography and a low risk for contrast allergy reaction. The aim of this study is to review the background, current literature, and future applications of CEM in breast cancer screening.

BACKGROUND: Contrast agent–enhanced digital mammography (CEDM) has been shown to be more sensitive and specific than two-dimensional full-field digital mammography in the diagnostic setting. Few studies have reported on its performance in the screening setting. PURPOSE: To evaluate the performance of CEDM for breast cancer screening. MATERIALS AND METHODS: This retrospective study included women who underwent dual-energy CEDM for breast cancer screening from December 2012 through April 2016. Medical records were reviewed for age, risk factors, short-interval follow-up and biopsies recommended, and cancers detected. Sensitivity, specificity, positive predictive value of abnormal findings at screening (PPV(1)), positive predictive value of biopsy performed (PPV(3)), and negative predictive value were determined. RESULTS: In the study period 904 baseline CEDMs were performed. Mean age was 51.8 years ± 9.4 (standard deviation). Of 904 patients, 700 (77.4%) had dense breasts, 247 (27.3%) had a family history of breast cancer in a first-degree relative age 50 years or younger, and 363 (40.2%) a personal history of breast cancer. The final Breast Imaging Reporting and Data System score was 1 or 2 in 832 of 904 (92.0%) patients, score of 3 in 25 of 904 (2.8%) patients, and score of 4 or 5 in 47 of 904 (5.2%) patients. By using CEDM, 15 cancers were diagnosed in 14 of 904 women (cancer detection rate, 15.5 of 1000). PPV(3) was 29.4% (15 of 51). At least 1-year follow up was available in 858 women. There were two interval cancers. Sensitivity was 50.0% (eight of 16; 95% confidence interval [CI]: 24.7%, 75.3%) on the low-energy images compared with 87.5% (14 of 16; 95% CI: 61.7%, 98.4%) for the entire study (low-energy and iodine images; P = .03). Specificity was 93.7% (789 of 842; 95% CI: 91.8%, 95.2%); PPV(1) was 20.9% (14 of 67; 95% CI: 11.9%, 32.6%), and negative predictive value was 99.7% (789 of 791; 95% CI: 99.09%, 99.97%). CONCLUSION: Contrast-enhanced digital mammography is a promising technique for screening women with higher-than-average risk for breast cancer. © RSNA, 2019

To develop a multiparametric [18F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue. In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [18F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [18F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC). There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5–10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P

Purpose To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. Procedures In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, 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 applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. Results For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). Conclusions Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

BACKGROUND: BRCA mutation carriers have an elevated lifetime breast cancer risk and remain at risk for interval cancer development. We sought to compare BRCA mutation carriers with screen-detected versus interval breast cancers. METHODS: Women with a known BRCA mutation prior to a breast cancer diagnosis were identified. Clinical and pathologic factors, and imaging within 18 months of diagnosis were compared among screen-detected versus interval cancers. Interval cancers were those detected by physical exam among women undergoing regular screening. RESULTS: Of 124 breast cancers, 92 were screen and 22 clinically detected, of which 11 were interval cancers among regular screeners, and 10 were incidentally found on prophylactic mastectomy. Women with interval cancers were younger, had lower body mass indexes, and were more likely to be Black than those with screen-detected cancers (p

Background Favorable progression-free survival (PFS) and overall survival (OS) results were previously reported on a phase II trial of patients with human epidermal growth receptor 2 (HER2)-positive metastatic breast cancer (MBC), treated with weekly paclitaxel in combination with trastuzumab and pertuzumab in the first- and second-line setting, with a median follow-up of 33 months. Here, we report updated PFS and OS results with more than 2 years of additional follow-up. Materials and methods In this phase II study, adult patients with HER2-positive MBC who received no or one prior therapy received intravenous paclitaxel (80 mg/m2 weekly) with trastuzumab (8 mg/kg loading dose followed by 6 mg/kg every 3 weeks) and pertuzumab (840 mg loading dose followed by 420 mg every 3 weeks), administered in 21-day cycles. Primary endpoint was 6-month PFS, and secondary endpoints included median PFS and OS. Results From January 2011 to December 2013, 69 patients were enrolled: 51 (74%) and 18 (26%) were treated in first- and second-line metastatic settings, respectively. As of August 21, 2017, the median follow-up was 59 months (range, 20-75 months; 67 [97%] patients were evaluable for efficacy). The 6-month PFS was 86% (95% confidence interval [CI] 0.76-0.93). The median PFS was 24.2 months (95% CI 17-35) for the overall population; it was 25.7 months (95% CI 17.0 to not reached) and 20.1 months (95% CI 8.5-33.0) for patients with no and one prior treatment, respectively. The median OS was not reached for the overall group; it was not reached and 39.7 months (95% CI 32.9-66.7) for patients with no and one prior treatment, respectively. Treatment was well tolerated with no additional safety concerns. Conclusion With a longer follow-up of almost 5 years, combination of weekly paclitaxel, trastuzumab, and pertuzumab remains effective with a favorable median PFS and a median OS not reached. Implications for practice The combination of weekly paclitaxel, trastuzumab, and pertuzumab has been endorsed by the National Comprehensive Cancer Network as one of the first-line treatment options in patients with human epidermal growth receptor 2 (HER2)-positive metastatic breast cancer (MBC). However, the long-term safety and efficacy are still unknown. Findings from this phase II study provide favorable preliminary data on the safety and efficacy of trastuzumab and pertuzumab in combination with weekly paclitaxel at 5-year follow-up, and it remains an effective first-line treatment option for patients with HER2-positive MBC.

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.

Simple Summary Breast cancer survivors are at risk for recurrence, and the early detection of recurrence improves survival. Therefore, imaging surveillance is performed for women who have breast-conserving surgery. The aim of our retrospective study was to compare routine mammography with contrast-enhanced mammography in the screening (asymptomatic) post-treatment setting. We confirmed that when screening women with breast conservation surgery, contrast-enhanced mammography had a higher cancer detection rate (15.4/1000) and positive predictive value of biopsies (42.9%) than full-field digital mammography (6.2/1000 and 37.5%, respectively). Abstract To investigate the value of contrast-enhanced mammography (CEM) compared to full-field digital mammography (FFDM) in screening breast cancer patients after breast-conserving surgery (BCS), this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved retrospective, single-institution study included 971 CEM exams in 541 asymptomatic patients treated with BCS who underwent screening CEM between January 2013 and November 2018. Histopathology, or at least a one-year follow-up, was used as the standard of reference. Twenty-one of 541 patients (3.9%) were diagnosed with ipsi- or contralateral breast cancer: six (28.6%) cancers were seen with low-energy images (equivalent to FFDM), an additional nine (42.9%) cancers were detected only on iodine (contrast-enhanced) images, and six interval cancers were identified within 365 days of a negative screening CEM. Of the 10 ipsilateral cancers detected on CEM, four were detected on low-energy images (40%). Of the five contralateral cancers detected on CEM, two were detected on low-energy images (40%). Overall, the cancer detection rate (CDR) for CEM was 15.4/1000 (15/971), and the positive predictive value (PPV3) of the biopsies performed was 42.9% (15/35). For findings seen on low-energy images, with or without contrast, the CDR was 6.2/1000 (6/971), and the PPV3 of the biopsies performed was 37.5% (6/16). In the post-BCS screening setting, CEM has a higher CDR than FFDM.

Mastectomy may be performed to treat breast cancer or as a prophylactic approach in women with a high risk of developing breast cancer. In addition, mastectomies may be performed with or without reconstruction. Reconstruction approaches differ and may be autologous, involving a transfer of tissue (skin, subcutaneous fat, and muscle) from other parts of the body to the chest wall. Reconstruction may also involve implants. Implant reconstruction may occur as a single procedure or as multistep procedures with initial use of an adjustable tissue expander allowing the mastectomy tissues to be stretched without compromising blood supply. Ultimately, a full-volume implant will be placed. Reconstructions with a combination of autologous and implant reconstruction may also be performed. Other techniques such as autologous fat grafting may be used to refine both implant and flap-based reconstruction. This review of imaging in the setting of mastectomy with or without reconstruction summarizes the literature and makes recommendations based on available evidence. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

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 Wilcoxon 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.

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR−), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR− breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR− breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with &gt, 20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77&ndash, 0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75&ndash, 0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.

Contrast-enhanced mammography (CEM) combines conventional mammography with iodinated contrast material to improve cancer detection. CEM has comparable performance to breast MRI without the added cost or time of conventional MRI protocols. Thus, this technique may be useful for indications previously reserved for MRI, such as problem-solving, determining disease extent in patients with newly diagnosed cancer, monitoring response to neoadjuvant therapy, evaluating the posttreatment breast for residual or recurrent disease, and potentially screening in women at intermediate- or high-risk for breast cancer. This article will provide a comprehensive overview on the past, present, and future of CEM, including its evolving role in the diagnostic and screening settings.

The rationale was to assess whether there are differences in multiparametric 18F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board-approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined 18F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer 18F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from 18F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in 18F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly (P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance (P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) (P < 0.001). Mean apparent diffusion coefficient did not differ between groups (P = 0.19). Conclusion: Differences in multiparametric 18F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy.

Screening mammography saves lives. The mainstay of screening has been mammography. Multiple alternative options, however, for supplemental imaging are now available. Some are just improved anatomic delineation whereas others include physiology added to anatomy. A third group (molecular imaging) is purely physiologic. This article describes and compares the available options and for which patient populations they should be used.

Purpose Contrast-enhanced digital mammography (CEDM) is an advanced breast imaging technique using iodinated intravenous contrast to detect breast cancer. This article describes imaging features of a skin contamination artifact on CEDM that mimics in-situ carcinoma in a case series. Materials and methods Five patients were identified whose CEDM images demonstrated apparent calcifications and non-mass enhancement suspicious for in-situ carcinoma, with no subsequent evidence of disease. Retrospective image analysis was performed on the unprocessed image data, processed images, and imaging parameters. Dual-energy mammographic technique was performed on two breast phantoms with varying degrees of topical contrast contamination. Results Temporal analysis confirmed the suspicious finding was neither an abnormality of the compression paddle nor the receptor. Comparison of LE and HE images demonstrated the suspicious finding attenuated near the K-edge of iodine, suggesting contrast contamination. Iodinated contrast applied to the surface of breast phantoms replicated the artifact, with a pattern of apparent enhancement similar to the appearance of in-situ carcinoma. Conclusion Skin contamination with iodinated contrast can result in an artifact on post-contrast digital mammography that mimics the appearance of in-situ carcinoma.