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Background: Radial scars/radial sclerosing lesions (RS) are benign breast lesions identified on core needle biopsy (CNB) which can upgrade to malignancy at excision. There is limited data on RS detection and upgrade rates with more sensitive imaging such as magnetic resonance imaging (MRI) and none during their detection for breast cancer workup and its implication on patient treatment decisions., Methods: A retrospective institutional study of RS diagnosed on CNB between January 2008 and December 2017 was conducted. Clinicopathologic and radiologic features of RS, patient treatment decisions, upgrade rates and long-term follow-up were examined., Results: We identified 133 patients with RS on CNB, of whom 106 opted for surgery for an upgrade rate to malignancy of 1.9%, 2 patients. Radial scar was diagnosed on mammogram in 60%, MRI in 25% and ultrasound in 15% of patients. In this cohort, 32 patients had their RS detected during breast cancer workup (coexistent group) and they were more likely to have their radial scar detected by MRI (60% vs. 14%, P < .001) and undergo more extensive surgery (94% vs. 75%, P = .02). Among the 27 patients electing observation of their RS, only one (3.7%) developed breast cancer., Conclusions: Our results show an extremely low upgrade rate to malignancy of RS, regardless if there is coexisting breast cancer elsewhere. Despite this, RS still prompted more extensive surgical excisions. The findings do not support excision of RS even among breast cancer patients when identified at a separate site from their cancer., Competing Interests: Disclosure The authors have stated that they have no conflicts of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Axillary staging in breast cancer is performed with physical exam and axillary imaging. Suspicious lymph nodes are biopsied and clipped prior to surgery to identify the previously biopsied node during surgery. Conventional mapping techniques of blue dye and radioisotope do not always accurately identify the clipped lymph node and therefore many techniques have been described to improve the identification of the clipped lymph node. Most of these techniques rely on axillary ultrasound to identify the previously biopsied lymph node or previously placed clip. A marker such as a fiducial reflector is then placed percutaneously. In this case report we describe a novel technique of Computed tomography (CT) identification of a previously clipped lymph node with CT guided placement of a fiducial reflector (SAVI SCOUT). The SAVI SCOUT was then identified and successfully excised with the previously biopsied lymph node. A PRECIS: CT guided placement of SAVI SCOUT is a novel technique for the identification and retrieval of a previously biopsied axillary lymph node in breast cancer patients., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: To investigate the ability of our convolutional neural network (CNN) to predict axillary lymph node metastasis using primary breast cancer ultrasound (US) images., Methods: In this IRB-approved study, 338 US images (two orthogonal images) from 169 patients from 1/2014-12/2016 were used. Suspicious lymph nodes were seen on US and patients subsequently underwent core-biopsy. 64 patients had metastatic lymph nodes. A custom CNN was utilized on 248 US images from 124 patients in the training dataset and tested on 90 US images from 45 patients. The CNN was implemented entirely of 3 × 3 convolutional kernels and linear layers. The 9 convolutional kernels consisted of 6 residual layers, totaling 12 convolutional layers. Feature maps were down-sampled using strided convolutions. Dropout with a 0.5 keep probability and L2 normalization was utilized. Training was implemented by using the Adam optimizer and a final SoftMax score threshold of 0.5 from the average of raw logits from each pixel was used for two class classification (metastasis or not)., Results: Our CNN achieved an AUC of 0.72 (SD ± 0.08) in predicting axillary lymph node metastasis from US images in the testing dataset. The model had an accuracy of 72.6% (SD ± 8.4) with a sensitivity and specificity of 65.5% (SD ± 28.6) and 78.9% (SD ± 15.1) respectively. Our algorithm is available to be shared for research use. (https://github.com/stmutasa/MetUS)., Conclusion: It's feasible to predict axillary lymph node metastasis from US images using a deep learning technique. This can potentially aid nodal staging in patients with breast cancer., (Copyright © 2022. Published by Elsevier Ltd.)

Introduction: We investigated whether our convolutional neural network (CNN)-based breast cancer risk model is modifiable by testing it on women who had undergone risk-reducing chemoprevention treatment., Materials and Methods: We conducted a retrospective cohort study of patients diagnosed with atypical hyperplasia, lobular carcinoma in situ, or ductal carcinoma in situ at our institution from 2007 to 2015. The clinical characteristics, chemoprevention use, and mammography images were extracted from the electronic health records. We classified two groups according to chemoprevention use. Mammograms were performed at baseline and subsequent follow-up evaluations for input to our CNN risk model. The 2 chemoprevention groups were compared for the risk score change from baseline to follow-up. The change categories included stayed high risk, stayed low risk, increased from low to high risk, and decreased from high to low risk. Unordered polytomous regression models were used for statistical analysis, with P < .05 considered statistically significant., Results: Of 541 patients, 184 (34%) had undergone chemoprevention treatment (group 1) and 357 (66%) had not (group 2). Using our CNN breast cancer risk score, significantly more women in group 1 had shown a decrease in breast cancer risk compared with group 2 (33.7% vs. 22.9%; P < .01). Significantly fewer women in group 1 had an increase in breast cancer risk compared with group 2 (11.4% vs. 20.2%; P < .01). On multivariate analysis, an increase in breast cancer risk predicted by our model correlated negatively with the use of chemoprevention treatment (P = .02)., Conclusions: Our CNN-based breast cancer risk score is modifiable with potential utility in assessing the efficacy of known chemoprevention agents and testing new chemoprevention strategies., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: Evaluate possible association between BPE and breast cancer tumor type/prognostic markers., Methods: IRB approved retrospective study from 1/2010-1/2014 identified 328 patients who had breast MRI and available clinical/pathology data. BPE was categorized according to BI-RADS. The association between BPE and breast cancer molecular subtype/prognostic factors was evaluated., Results: No significant association was present between high BPE and the following: HER2+ tumors, basal tumors, tumors with axillary nodal disease, high nuclear grade tumors, high Ki-67 index tumors or larger tumors., Conclusion: Patients with high BPE may be at increased risk for breast cancer but not necessarily for those cancer subtypes with a poor prognosis., (Copyright © 2018. Published by Elsevier Inc.)

Purpose: To compare surgical outcomes of SAVI SCOUT reflector localization (SSL) versus wire localization (WL) for breast tumors., Methods: Retrospective review of 42 SSL cases and 42 WL cases. WL patients were consecutively matched for clinical-pathologic features. Final surgical outcome measures were tumor specimen volume, margin status, and re-excision rates., Results: No significant differences were present in median specimen volumes (SSL-15.2cm 3 vs. WL-16.3cm 3 ), positive margin rate (SSL-9.5% vs. WL-7.1%), close margin rate (SSL-7.1% vs. WL-11.9%) or re-excision rate (SSL-7.1% vs. WL-9.5%)., Conclusion: SSL is an acceptable alternative to WL with no significant differences in surgical outcomes., (Copyright © 2017 Elsevier Inc. All rights reserved.)