Virtual Visiting Professorship Program as an Opportunity for Academic and Clinical Advancement Beyond the COVID-19 Pandemic: A Survey of Participants.

OBJECTIVE: To identify structure, benefits, and shortcomings of a multi-institutional virtual visiting professorship (VVP) program from 2020 to 2022, 2 years after inception and after gradual resumption of an in-person, prepandemic academic environment. METHODS: An IRB-exempt, 70-question survey about structure, benefits, and shortcomings of the VVP program was distributed to its participants (14 breast imaging departments across the U.S.), using the snowball sampling technique. RESULTS: A total of 72 responses were received; 54.2% (32/59) radiologists >5 years of experience, 18.6% (11/59) radiologists <5 years of experience, 15.3% (9/59) residents, and 8.5% (5/59) fellows. Radiologists' attendance increased from 8% (5/59) to 53% (31/59) over 2 years, with 69% (41/59) of respondents supporting continued participation. The most important factors for attendance were expanding breast imaging knowledge (86.4% [51/59]) and the virtual format (76.2% [45/59]). The number of presented lectures increased from 1 to 3 lectures in 43.7% (7/16) of programs in year 1 and from 4 to 9 lectures in 50% (8/16) of programs in year 2. The greatest professional benefits were collaborations on publications for organizers (56.3% [9/16]) and building academic portfolios for presenters (50% [7/14]). For trainees, attending the program increased their knowledge (64.3% [9/14]) and enthusiasm for breast imaging (50% [7/14]). CONCLUSION: The VVP program facilitated scholarly collaboration among breast imaging radiologists, promoted academic portfolios for junior faculty, and increased enthusiasm for breast imaging for trainees. These accomplishments extended beyond the COVID-19 pandemic, as evidenced by the growth of the program after resumption of an in-person academic environment. Future expansion to other programs would benefit more practicing radiologists.

Immediate and delayed risk of breast cancer associated with classic lobular carcinoma in situ and its variants.

OBJECTIVE: To determine the risk of breast cancer due to lobular carcinoma in situ (LCIS). METHODS: This retrospective IRB-approved study identified cases of LCIS after percutaneous breast biopsy from 7/2005 to 7/2022. Excluded were cases with less than 2 years of imaging surveillance or a concurrent ipsilateral breast cancer diagnosis within 6 months of the LCIS diagnosis. Final outcomes of cancer versus no cancer were determined by pathology at surgical excision or the absence of cancer on imaging surveillance. RESULTS: A total of 116 LCIS lesions were identified. The primary imaging findings targeted for percutaneous biopsy included calcifications (50.0%, 58/116), MR enhancing lesions (25.0%, 29/116), noncalcified mammographic architectural distortions (10.3%, 12/116), or masses (14.7%, 17/116). Surgical excision was performed in 49.1% (57/116) and imaging surveillance was performed in 50.9% (59/116) of LCIS cases. There were 22 cancers of which 11 cancers were discovered at immediate excision [19.3% (11/57) immediate upgrade] and 11 cancers developed later while on imaging surveillance [18.6% (11/59) delayed risk for cancer]. Among all 22 cancers, 63.6% (14/22) occurred at the site of LCIS (11 at immediate excision and 3 at surveillance) and 36.4% (8/22) occurred at a location away from the site of LCIS (6 in a different quadrant and 2 in the contralateral breast). CONCLUSION: LCIS has both an immediate risk (19.3%) and a delayed risk (18.6%) for cancer with 90.9% occurring in the ipsilateral breast (63.6% at and 27.3% away from the site of LCIS) and 9.1% occurring in the contralateral breast.

Breast Imaging Fellowship Virtual Interviews: The Program Director's Perspective.

OBJECTIVE: To evaluate breast imaging fellowship program directors' perspectives on the virtual interview process. METHODS: A 20-question survey constructed by members of the Fellowship Match Committee of the Society of Breast Imaging was distributed to all 99 breast imaging program directors registered with the Society. An initial e-mail with a link to the survey was distributed on September 9, 2022, and the survey was closed on October 1, 2022. Results were compiled and a descriptive statistical analysis was performed utilizing Microsoft Excel. RESULTS: There were 63 total responses (63/99, 64% response rate). There was a wide distribution in both the number of applications received and the number of interviews each program offered. Just under a fifth (12/63, 19%) of programs received 1 to 5 applications, whereas a quarter (16/63, 25%) received over 40 applications. In contrast, over a quarter (17/63, 27%) of programs interviewed 1 to 5 applicants, and only a small number (3/63, 5%) interviewed over 40 applicants. When reporting what worked best with the virtual interview process, the responses fell into the following 4 categories: efficiency, flexibility, virtual format, or other. When reporting what did not work well, the most common response (14/37, 38%) was conveying the atmosphere of the program in the virtual setting. CONCLUSION: This study provides an assessment of the virtual interview experience from the perspective of breast imaging fellowship programs, which may be useful in optimizing future interview experiences for programs and applicants.

Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic Therapy.

BACKGROUND: Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST). PURPOSE: To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST. STUDY TYPE: Prospective. POPULATION: Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR). FIELD STRENGTH/SEQUENCE: 3.0 T/reduced field of view single-shot echo-planar DTI sequence. ASSESSMENT: Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery. STATISTICAL TESTS: Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant. RESULTS: 47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm2 /s). DATA CONCLUSION: Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 4.

Clinical outcomes after 1 versus 2-3 lines of neoadjuvant therapy in stage III inflammatory breast cancer.

PURPOSE: Many stage III inflammatory breast cancer (IBC) patients experience a sufficient response to first-line (1L) neoadjuvant chemotherapy (NAC) to allow surgery, while some require additional NAC. We evaluated the pathologic complete response (pCR), breast cancer-free survival (BCFS) and overall survival (OS) among patients requiring 1 vs. 2-3 lines (L) of NAC prior to surgery. METHODS: Stage III IBC patients from 2 institutions who received 1L or 2-3L of NAC prior to surgery were identified. Hormone receptor and HER2 status, grade, and pCR were evaluated. BCFS and OS were evaluated by the Kaplan-Meier method. Multivariable Cox models were utilized to estimate the hazard ratio (HR). RESULTS: 808 eligible patients (1997-2020) were identified (median age 51 years, median follow-up 69 months). 733 (91%) had 1L and 75 (9%) had 2-3L of NAC. Grade III, triple-negative and HER2-positive disease were more prevalent in 2-3L patients. 178 (24%) 1L and 14 (19%) 2-3L patients had pCR. 376 1L patients and 41 2-3L patients had recurrences. The 5-year BCFS was worse for the 2-3L group (33 vs. 46%, HR = 1.37; 95% CI 0.99-1.91). However, in 192 patients with a pCR, BCFS was similar (76 vs. 83% in 1L vs. 2-3L, respectively). There were 308 deaths (276 among 1L and 32 among 2-3L patients). The 5-year OS in 1L vs. 2-3L was 60 vs. 53% (HR = 1.32, 95% CI 0.91-1.93). CONCLUSIONS: Among stage III IBC patients, pCR rates were similar, irrespective of the NAC lines number, and BCFS and OS were comparable with pCR after 1L and 2-3L.

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs.

We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.

Isolated Flat Epithelial Atypia: Upgrade Outcomes After Multidisciplinary Review-Based Management Using Excision or Imaging Surveillance.

Objective: To compare flat epithelial atypia (FEA) upgrade rates after excision versus surveillance and to identify variables associated with upgrade. Methods: This single-institution retrospective study identified isolated FEA cases determined by percutaneous biopsy from April 2005 through July 2022 with excision or ≥2 years surveillance. All cases were recommended for excision or surveillance based on multidisciplinary discussion of clinical, imaging, and pathologic variables with emphasis on sampling adequacy and significant atypia. Truth was determined by pathology at excision or the absence of cancer on surveillance. Upgrade was defined as cancer occurring ≤2 cm from the biopsy site. Demographic, imaging, and biopsy variables were compared between those that did and did not upgrade. Results: Among 112 cases of isolated FEA, imaging findings included calcifications in 81.3% (91/112), MRI lesions in 11.6% (13/112), and distortions or masses in 7.1% (8/112). Excision was recommended in 12.5% (14/112) and surveillance in 87.5% (98/112) of cases. Among those recommended for excision, 28.6% (4/14) of cases were upgraded, all to ductal carcinoma in situ. In those recommended for surveillance, 1.0% (1/98) were upgraded to invasive cancer. Overall, FEA had a 4.5% (5/112) upgrade rate, and 2.7% (3/112) also developed cancer >2 cm from the FEA. There were no significant differences in demographic, imaging, and biopsy variables between those that did and did not upgrade to cancer. Conclusion: Multidisciplinary management of isolated FEA distinguishes those at higher risk of upgrade to cancer (28.6%) in whom surgery is warranted from those at low risk of upgrade (1.0%) who can be managed non-operatively.

Analysis of Technical Repeat Studies in Screening Mammography.

Objective: To identify causes of technical repeats, determine whether differences exist between mobile and fixed mammography units, and evaluate the rate of improvement on repeat imaging. Methods: IRB approval was obtained for retrospective review of Enhancing Quality Using the Inspection Program (EQUIP) logs of screening mammography technical repeats performed from March 2017 to December 2018 at a hospital breast imaging center and from April 2017 to December 2018 on mobile mammography units. Frequency tables and Fisher's exact tests were used for statistical analysis. Results: Technical deficiencies were reported in 483 cases and reviewed by two or three auditors. Auditors identified no technical deficiencies in 31 cases, which were excluded. The remaining 452 cases were assigned a technical recall category: motion, positioning/excluded tissue, skin folds, artifacts, undercompression, or contrast (under/overexposure). Motion was the most common technical recall category (253/452, 56.0%). Positioning/excluded tissue was the second most common reason (150/452, 33.2%). Statistically significant differences in technical deficiencies were identified between mammograms performed on mobile versus fixed mammography units for motion (94/143, 65.7% vs 159/309, 51.5%, respectively, P = 0.0058), skin folds (16/143, 11.2% vs 15/309, 4.8%, respectively, P = 0.02), and positioning/excluded tissue (30/143, 21% vs 120/309, 38.8%, respectively, P = 0.00016). Most recalls improved with repeat imaging (auditor 1: 451/483, 93% and auditor 2: 387/483, 80%). Conclusion: Motion and positioning/excluded tissue are the most common reasons for screening mammography technical recalls. The reasons for technical recall differ between patients imaged on mobile and fixed mammography units, likely because of differences in each location's patient population.

Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect.

Purpose: In women with biopsy-proven breast cancer, histologically normal areas of the parenchyma have shown molecular similarity to the tumor, supporting a potential cancer field effect. The purpose of this work was to investigate relationships of human-engineered radiomic and deep learning features between regions across the breast in mammographic parenchymal patterns and specimen radiographs. Approach: This study included mammograms from 74 patients with at least 1 identified malignant tumor, of whom 32 also possessed intraoperative radiographs of mastectomy specimens. Mammograms were acquired with a Hologic system and specimen radiographs were acquired with a Fujifilm imaging system. All images were retrospectively collected under an Institutional Review Board-approved protocol. Regions of interest (ROI) of 128×128 pixels were selected from three regions: within the identified tumor, near to the tumor, and far from the tumor. Radiographic texture analysis was used to extract 45 radiomic features and transfer learning was used to extract 20 deep learning features in each region. Kendall's Tau-b and Pearson correlation tests were performed to assess relationships between features in each region. Results: Statistically significant correlations in select subgroups of features with tumor, near to the tumor, and far from the tumor ROI regions were identified in both mammograms and specimen radiographs. Intensity-based features were found to show significant correlations with ROI regions across both modalities. Conclusions: Results support our hypothesis of a potential cancer field effect, accessible radiographically, across tumor and non-tumor regions, thus indicating the potential for computerized analysis of mammographic parenchymal patterns to predict breast cancer risk.

Imaging Features Following Breast Explant Surgery: A Pictorial Essay.

Breast implants can be removed with breast explantation surgery (BES) for various reasons, including patient dissatisfaction, capsular contracture, implant infection or rupture, breast implant-associated anaplastic large cell lymphoma, and a recently emerging phenomenon called breast implant illness. There is very limited data on the imaging appearance after BES. A retrospective chart review was performed for patients with BES findings on imaging reports for the period between October 2016 and October 2021. When assessing BES techniques, a key element is determining whether the implant's fibrous capsule requires removal. The second important question is if the patient requires an additional aesthetic procedure after BES. BES techniques include capsulotomy, and partial, total, or en bloc capsulectomy. Adjunctive aesthetic or reconstructive procedures after BES include fat grafting, mastopexy, augmentation, and reconstruction with flaps. The majority of post-BES breast imaging findings are related to the surgical scar/bed, thereby confirming that the type of explantation surgery is important. Imaging findings after BES include focal and global asymmetries, architectural distortions, calcifications, calcified and non-calcified fat necrosis, masses, hematomas, seromas, capsular calcifications, and silicone granulomas. Most importantly, since these patients have residual breast tissue, paying attention to imaging features that are suspicious for breast cancer is necessary.

Stepwise Implementation of 2D Synthesized Screening Mammography and Its Effect on Stereotactic Biopsy of Microcalcifications.

RATIONALE AND OBJECTIVES: Information evaluating the efficacy of 2D synthesized mammography (2Ds) reconstructions in microcalcification detection is limited. This study used stereotactic biopsy data for microcalcifications to evaluate the stepwise implementation of 2Ds in screening mammography. The study aim was to identify whether 2Ds + digital breast tomosynthesis (DBT) is non-inferior to 2D digital mammography (2DM) + 2Ds + DBT, 2DM + DBT, and 2DM in identifying microcalcifications undergoing further diagnostic imaging and stereotactic biopsy. MATERIALS AND METHODS: Retrospective stereotactic biopsy data were extracted following 151,736 screening mammograms of healthy women (average age, 56.3 years; range, 30-89 years), performed between 2012 and 2019. The stereotactic biopsy data were separated into 2DM, 2DM + DBT, 2DM + 2Ds + DBT, and 2Ds + DBT arms and examined using Fisher's exact test to compare the detection rates of all cancers, invasive cancers, DCIS, and ADH between modalities for patients undergoing stereotactic biopsy of microcalcifications. RESULTS: No statistical significance in cancer detection was seen for 2Ds + DBT among those calcifications that underwent stereotactic biopsy when comparing the 2Ds + DBT to 2DM, 2DM + DBT, and 2DM + 2Ds + DBT imaging combinations. CONCLUSION: These data suggest that 2Ds + DBT is non-inferior to 2DM + DBT in detecting microcalcifications that will undergo stereotactic biopsy.

Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction.

The identification of women at risk for sporadic breast cancer remains a clinical challenge. We hypothesize that the temporal analysis of annual screening mammograms, using a long short-term memory (LSTM) network, could accurately identify women at risk of future breast cancer. Women with an imaging abnormality, which had been biopsy-confirmed to be cancer or benign, who also had antecedent imaging available were included in this case-control study. Sequences of antecedent mammograms were retrospectively collected under HIPAA-approved guidelines. Radiomic and deep-learning-based features were extracted on regions of interest placed posterior to the nipple in antecedent images. These features were input to LSTM recurrent networks to classify whether the future lesion would be malignant or benign. Classification performance was assessed using all available antecedent time-points and using a single antecedent time-point in the task of lesion classification. Classifiers incorporating multiple time-points with LSTM, based either on deep-learning-extracted features or on radiomic features, tended to perform statistically better than chance, whereas those using only a single time-point failed to show improved performance compared to chance, as judged by area under the receiver operating characteristic curves (AUC: 0.63 ± 0.05, 0.65 ± 0.05, 0.52 ± 0.06 and 0.54 ± 0.06, respectively). Lastly, similar classification performance was observed when using features extracted from the affected versus the contralateral breast in predicting future unilateral malignancy (AUC: 0.63 ± 0.05 vs. 0.59 ± 0.06 for deep-learning-extracted features; 0.65 ± 0.05 vs. 0.62 ± 0.06 for radiomic features). The results of this study suggest that the incorporation of temporal information into radiomic analyses may improve the overall classification performance through LSTM, as demonstrated by the improved discrimination of future lesions as malignant or benign. Further, our data suggest that a potential field effect, changes in the breast extending beyond the lesion itself, is present in both the affected and contralateral breasts in antecedent imaging, and, thus, the evaluation of either breast might inform on the future risk of breast cancer.

Comparing Breast Hemangiomas to Breast Angiosarcomas: An Imaging Based Review.

Vascular tumors of the breast are rare, and most can be classified as either hemangiomas or angiosarcomas. Breast hemangiomas are benign vascular tumors that are often found incidentally at pathologic examination. Breast hemangiomas may mimic low grade angiosarcomas, which are malignant vascular tumors. This article illustrates the imaging characteristics of breast hemangiomas and breast angiosarcomas across multiple imaging modalities.

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.

Review of the Sonographic Features of Interpectoral (Rotter) Lymph Nodes in Breast Cancer Staging.

ABSTRACT: This article reviews the ultrasound evaluation and staging of breast cancer with respect to the involvement of interpectoral (Rotter) lymph nodes. The primary objective is to demonstrate and assess the characteristic sonographic findings of interpectoral (Rotter) lymph nodes to help provide accurate nodal staging information. We aim to provide a comprehensive review and serve as an imaging guide for the identification and evaluation of Rotter lymph nodes. The detection of abnormalities and pathologic features of metastatic axillary nodal disease in the interpectoral region is reviewed, and the impact on clinical management and treatment is discussed. In the radiology literature, there is no comprehensive review of the sonographic appearance and evaluation of Rotter lymph nodes.

Virtual Mentoring: A Guide to Navigating a New Age in Mentorship.

Mentorship is a fundamental part of professional and personal growth. Over time, the fabric of mentorship has been transforming from typical one-on-one mentor-mentee relationship to other types including peer, group, speed, and virtual mentoring. When the COVID-19 pandemic hit, it caused major disruptions in many facets of life and career, including mentoring. In response to the COVID-19 pandemic, live meetings were canceled, and social distancing measures were enacted at many institutions. Thus, the traditional set-up, with a face-to-face mentor and mentee interaction, was impossible. Many virtual platforms were utilized to navigate through these restrictions. In this review, we highlight challenges in mentorship during the COVID-19 pandemic and how we implemented different strategies to promote mentorship.