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Vaccination-associated adenopathy is a frequent imaging finding after administration of COVID-19 vaccines that may lead to a diagnostic conundrum in patients with manifest or suspected cancer, in whom it may be indistinguishable from malignant nodal involvement. To help the medical community address this concern in the absence of studies and evidence-based guidelines, this paper offers recommendations developed by a multidisciplinary panel of experts from three of the leading tertiary care cancer centers in the United States. According to these recommendations, some routine imaging examinations, such as those for screening, should be scheduled before or at least 6 weeks after the final vaccination dose to allow for any reactive adenopathy to resolve. However, there should be no delay of other clinically indicated imaging (e.g., for acute symptoms, short-interval treatment monitoring, urgent treatment planning or complications) due to prior vaccination. The vaccine should be administered on the side contralateral to the primary or suspected cancer, and both doses should be administered in the same arm. Vaccination information (date(s) administered, injection site(s), laterality, and type of vaccine) should be included in every pre-imaging patient questionnaire, and this information should be made readily available to interpreting radiologists. Clear and effective communication between patients, radiologists, referring physician teams and the general public should be considered of the highest priority when managing adenopathy in the setting of COVID-19 vaccination. Summary COVID-19-vaccination–related adenopathy is a frequent imaging finding that may lead to a diagnostic conundrum in patients with manifest or suspected cancer, in whom it may be indistinguishable from malignant nodal involvement. This special report offers recommendations developed by a multidisciplinary panel of experts from three of the leading tertiary care cancer centers in the United States.

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a rare but increasingly important diagnosis as the incidence of breast implant placement, both elective and reconstructive, continues to rise. When detected and treated early, this indolent disease carries an excellent prognosis. However, because the clinical presentation is often nonspecific, it is crucial for radiologists to accurately identify the imaging findings associated with BIA-ALCL to facilitate a timely diagnosis. This article will provide radiologists with an overview of the diagnosis, imaging findings, and management of BIA-ALCL.

This study aims to evaluate ultrafast DCE-MRI-derived kinetic parameters that reflect contrast agent inflow effects in differentiating between subcentimeter BI-RADS 4–5 breast carcinomas and benign lesions. We retrospectively reviewed consecutive 3-T MRI performed from February to October 2017, during which ultrafast DCE-MRI was performed as part of a hybrid clinical protocol with conventional DCE-MRI. In total, 301 female patients with 369 biopsy-proven breast lesions were included. Ultrafast DCE-MRI was acquired continuously over approximately 60 s (temporal resolution, 2.7–7.1 s/phase) starting simultaneously with the start of contrast injection. Four ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS], contrast enhancement ratio [CER], bolus arrival time [BAT], and initial area under gadolinium contrast agent concentration [IAUGC]) and one conventional DCE-MRI-derived kinetic parameter (signal enhancement ratio [SER]) were calculated for each lesion. Wilcoxon rank sum test or Fisher’s exact test was performed to compare kinetic parameters, volume, diameter, age, and BI-RADS morphological descriptors between subcentimeter carcinomas and benign lesions. Univariate/multivariate logistic regression analyses were performed to determine predictive parameters for subcentimeter carcinomas. In total, 125 lesions (26 carcinomas and 99 benign lesions) were identified as BI-RADS 4–5 subcentimeter lesions. Subcentimeter carcinomas demonstrated significantly larger MS and SER and shorter BAT than benign lesions (p = 0.0117, 0.0046, and 0.0102, respectively). MS, BAT, and age were determined as significantly predictive for subcentimeter carcinoma (p = 0.0208, 0.0023, and

Background Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. Methods Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4–6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7–4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values Results Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1–2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P Conclusions Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

The United States is in the midst of an opioid use epidemic, which has severe medical, social, and economic consequences. Addictions to and abuse of prescription and illicit opioids are increasing, and emergency department radiologists are increasingly being faced with the task of examining patients who present with opioid-related complications. These complications may be the result of direct drug toxicity or nonsterile injection of the drugs. Neurologic, musculoskeletal, cardiopulmonary, genitourinary, and gastrointestinal complications may be evident at diagnostic imaging in emergent settings. Heroin-induced leukoencephalopathy, cerebral septic emboli, mycotic arterial aneurysms, soft-tissue infections, and infective endocarditis are some of the conditions that patients may be found to have after they present to the emergency department. In this article, the above topics, including clinical features, pathophysiology, imaging findings, and treatment options, are reviewed. Recognizing the limitations of diagnostic imaging modalities that are available to radiologists is equally important, as some conditions can be successfully diagnosed after the initial triage-for example, transesophageal echocardiography can be performed to diagnose infective endocarditis. The emergency department radiologist may be responsible for identifying acute conditions, which can be life threatening. Some of the more common emergent opioid-related conditions and complications are reviewed, with specific emphasis on cases in which emergency department radiologists encounter conditions for which additional expertise is required. Becoming familiar with the conditions directly related to the current opioid epidemic will enable the diagnosis of these entities in a timely and accurate manner.

PURPOSE Evaluate the clinical presentation and imaging findings of breast implant-associated anaplastic large cell lymphoma (BIA ALCL) at a large US cancer center. MATERIALS AND METHODS HIPAA-compliant IRB approved retrospective study, for which informed consent was waived. The Hospital Information System was screened for women who underwent implant reconstruction and were diagnosed with BIA ALCL between 2010 and 2016. Two radiologists reviewed images in consensus. Clinical and imaging characteristics were summarized using means and ranges for continuous variables and percentages for categorical variables. RESULTS Patient cohort included 11 women with BIA ALCL (mean age at diagnosis = 54 years, range: 35-77), including women with (9/11) and without (2/11) history of breast cancer. Mean time from breast implant placement to diagnosis was 10 years (range: 6-14). BIA ALCL was identified in patients with saline (4/11) and silicone (5/11) implants. Implants were textured in 7/11 (63%) and unknown in 4/11 (36%) cases. All patients presented with a peri-implant seroma, (9/11 documented on imaging). Two of 11 patients had a mass within this seroma. Ten of 11 patients (91%) presented with symptoms. CONCLUSIONS Saline and silicone breast implants may predispose patients to a rare lymphoma subtype, BIA ALCL, which presents on imaging as a peri-implant fluid collection ± mass.

To evaluate breast biopsy marker migration in stereotactic core needle biopsy procedures and identify contributing factors. This retrospective study analyzed 268 stereotactic biopsy markers placed in 263 consecutive patients undergoing stereotactic biopsies using 9G vacuum-assisted devices from August 2010-July 2013. Mammograms were reviewed and factors contributing to marker migration were evaluated. Basic descriptive statistics were calculated and comparisons were performed based on radiographically-confirmed marker migration. Of the 268 placed stereotactic biopsy markers, 35 (13.1%) migrated ≥1 cm from their biopsy cavity. Range: 1–6 cm; mean (± SD): 2.35 ± 1.22 cm. Of the 35 migrated biopsy markers, 9 (25.7%) migrated ≥3.5 cm. Patient age, biopsy pathology, number of cores, and left versus right breast were not associated with migration status (P> 0.10). Global fatty breast density (P= 0.025) and biopsy in the inner region of breast (P = 0.031) were associated with marker migration. Superior biopsy approach (P= 0.025), locally heterogeneous breast density, and t-shaped biopsy markers (P= 0.035) were significant for no marker migration. Multiple factors were found to influence marker migration. An overall migration rate of 13% supports endeavors of research groups actively developing new biopsy marker designs for improved resistance to migration. • Breast biopsy marker migration is documented in 13% of 268 procedures. • Marker migration is affected by physical, biological, and pathological factors. • Breast density, marker shape, needle approach etc. affect migration. • Study demonstrates marker migration prevalence; marker design improvements are needed.

Background: Applying the PAM50 classifier to targeted RNA-Sequencing data allows HER2+ tumors to be sub-categorized into intrinsic breast cancer subtypes. HER2+ breast cancers belonging to the HER2-enriched [HER2-E] subtype exhibit the highest rate of response to neoadjuvant therapy with combination of HER2-blockade and chemotherapy, as well as dual-HER2 blockade alone. A non-invasive predictor of PAM50 subtype from clinical dynamic contrast-enhanced MRI [DCE-MRI] could provide valuable clinical guidance in the treatment of HER2+ breast cancer. In this work, we identify a set of computer-extracted heterogeneity features computed within the lesion and its surrounding peritumoral region capable of distinguishing HER2-E from other HER2+ breast cancers [Non-HER2-E]. We then demonstrate that this imaging signature of HER2-E is also predictive of pathological complete response [pCR] in an independent HER2+ testing set, consistent with the HER2-E subtype's elevated response to HER2-targeted therapy. Methods: The training set consisted of 42 HER2+ patients with both 1.5 or 3 T DCE-MRI and targeted RNA sequencing collected prior to neoadjuvant treatment from a multicenter trial [BrUOG 211B, n=35] and The Cancer Genome Atlas-Breast Cancer project [TCGA-BRCA, n=7]. Intrinsic subtypes were assigned by unsupervised hierarchical clustering of the PAM50 gene set. 19 patients were determined to belong to the HER2-E subtype, while the remaining 23 represented non-HER2-E subtypes [19 HER2-Luminal, 4 HER2-basal]. Lesion boundaries were annotated by an expertly trained radiologist and expanded to 5 annular peritumoral regions in 3 mm increments out to a maximum radius of 15 mm. Computer-extracted heterogeneity features were computed voxelwise within intratumoral and peritumoral regions by first order statistics. A top HER2-E-associated feature from each region was identified by Wilcoxon feature selection and used to train a diagonal linear discriminant analysis [DLDA] classifier to predict HER2-E in a 3-fold cross-validation setting. This classifier was then applied to pCR prediction from DCE-MRI in a testing set of 28 HER2+ patients with available post neoadjuvant chemotherapy surgical specimens at one institution. 16 patients achieved pCR (ypT0/is), while the remainder had partial or no response (non-pCR). Results: A combination of heterogeneity features within the intratumoral region and annular peritumoral regions out to 12 mm from the tumor yielded optimal results within the training set, with an average HER2-E prediction AUC of .77 +/- .03. When applied to response prediction in an independent testing set, this HER2-E classifier was predictive of pCR (AUC = .72). Conclusions: Computer-extracted heterogeneity features calculated within the tumor and the surrounding peritumoral environment on DCE-MRI were able to distinguish the HER2-E PAM50 intrinsic subtype from other HER2+ breast cancers. HER2-E was characterized by elevated expression of intratumoral and peritumoral heterogeneity features, indicating a more disordered imaging phenotype within and around the tumor. Additional independent validation of these findings is needed. Citation Format: Braman N, Prasanna P, Singh S, Beig N, Gilmore H, Etesami M, Bates D, Gallagher K, Bloch BN, Somlo G, Sikov W, Harris L, Plecha D, Varadan V, Madabhushi A. Intratumoral and peritumoral MRI signatures of HER2-enriched subtype also predict pathological response to neoadjuvant chemotherapy in HER2+ breast cancers [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-02-06.

Background: It has been previously shown that computer-extracted heterogeneity features calculated within the peritumoral region of a breast cancer from baseline dynamic contrast-enhanced MRI [DCE-MRI] can predict treatment outcome. This approach may due to microenvironmental signatures of elevated immune response, a predictor of favorable response to neoadjuvant chemotherapy [NAC] in HER2+ breast cancer. We assessed the correlation between peritumoral radiomic features and a tissue-derived genomic immune index [II] in HER2+ breast cancer and whether these immune-correlated imaging features are predictive of pathologic response. Methods: 33 HER2+ patients with both 1.5 or 3 T DCE-MRI imaging and targeted RNA sequencing of biopsy samples collected prior to NAC from a multicenter trial [BrUOG 211B, n=26] and The Cancer Genome Atlas-Breast Cancer project [TCGA-BRCA, n=7] were retrospectively analyzed. II was derived from expression of a 140-gene immune signature using the ESTIMATE algorithm. An attending breast radiologist annotated lesion boundaries on the DCE-MRI phase of peak contrast enhancement. Beyond this intratumoral region, 5 annular peritumoral regions in 3 mm increments out to a maximum radius of 15 mm were analyzed. Computer-extracted heterogeneity descriptors computed within the intratumoral and peritumoral regions were summarized by first order statistics. Redundancy was reduced by eliminating correlated imaging features (R2>.6). From the remaining features, the 5 features that were collectively best correlated with II were selected by feed forward, leave-one-out multilinear regression. The regression model was applied to an independent test set of 28 HER2+ patients with post NAC surgical specimens. The estimated II was assessed for its ability to differentiate patients who achieved a pathologic complete response in the breast [pCR, ypT0/is] (n=16) and those who did not (n=12) by 2-sided Wilcoxon rank sum test of median and area under the receiver operating characteristic curve (AUC). Results: The set of top features that significantly correlated (p Conclusions: From a set of quantitative features characterizing heterogeneity within the peritumoral region on DCE-MRI, we identified peritumoral imaging features correlated with a genomic index of immune response in HER2+ breast cancer and were predictive of pathologic response in an independent testing set. Our findings suggest that the predictive capability of peritumoral radiomics may be tied to a patient's immune response to the cancer. In addition to providing insight to the biological basis of peritumoral radiomics, imaging signatures of immune response themselves possess clinical value as a potential means for the non-invasive prediction of HER2+ cancer biology and treatment outcome. Additional independent validation is needed on a larger test set to confirm our preliminary findings. Citation Format: Braman N, Prasanna P, Singh S, Beig N, Gilmore H, Etesami M, Bates D, Gallagher K, Bloch BN, Somlo G, Sikov W, Harris L, Plecha D, Varadan V, Madabhushi A. Radiogenomic analysis of HER2+ breast cancer reveals MRI features correlated with genomic immune index are predictive of neoadjuvant chemotherapy response [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-02-07.