Search

PURPOSE Triple-negative breast cancer (TNBC) is a heterogeneous disease with variable response to neoadjuvant therapy (NAT). Pathologic complete response (pCR) has become a prognostic marker for overall and disease-free survival. The aim of this study was to determine if dynamic contrast-enhanced (DCE)-MRI after 2 and/or 4 cycles of NAT can identify patients with a high likelihood of achieving pCR, triaging them to standard of care (SOC), or, when appropriate, to de-escalation trials. Conversely, we aimed to identify chemoresistant tumors that are unlikely to achieve pCR and may benefit from escalated targeted trials. METHOD AND MATERIALS 309 patients with stage I-III TNBC underwent DCE-MRI (temporal resolution: 9-12 sec) at baseline (BL), 2 cycles (C2), and 4 cycles (C4) of SOC doxorubicin/cyclophosphamide (AC) NAT as part of a prospective IRB-approved study (NCT02276443). Tumor volumes of the index lesion were calculated using 3 axis measurements during the early phase of the DCE-MRI (60s). Percent tumor volume reduction (TVR) between BL, C2, and C4 was calculated. Patients were randomly assigned to a training or a validation cohort in a 1:1 ratio. pCR was assessed at surgery after completion of SOC NAT. Correlation between pCR and TVR was evaluated using ROC analysis. RESULTS Of 309 TNBC patients, 136 (44%) achieved pCR. Following 2 cycles of NAT, TVR >80% was predictive of pCR (chemosensitivity), while TVR ≤ 55% was predictive of non-pCR (chemoresistance) with PPV 80%, NPV 89%, AUC 0.811 (0.73~0.893, p< 0.0001) in the training cohort, and PPV 82%, NPV 85%, AUC 0.815 (CI:0.736~0.894, p< 0.0001) in the validation cohort. Following 4 cycles of NAT, TVR >90% was predictive of pCR, while TVR ≤80% was predictive of non-pCR with PPV 80%, NPV 84%, AUC 0.827 (0.756~0.898, p< 0.0001) in the training cohort and with PPV 73%, NPV 82%, AUC 0.785 (CI:0.709~0.862, p< 0.001) in the validation cohort. Using this model, the pCR status was correctly classified in 50% of TNBC patients using C2 DCE-MRI in the training cohort, and 54% in the validation cohort. Only 8% were misclassified in the training cohort, and 10% in the validation cohort. Using C4 DCE-MRI, the pCR status of 61% and 57% of TNBC was correctly classified in the validation and the testing cohorts, respectively. 12% were misclassified in the validation cohort, and 21% in the testing cohort. CONCLUSION DCE-MRI after 2 and 4 cycles of AC-based NAT correctly predicted the pCR status of 54% and 57% of TNBC patients, respectively, as either excellent responders or nonresponders with high AUC 0.811 and 0.827. This may allow patients to be triaged to SOC NAT with option of de-escalation or early targeted therapies for non-responders. Citation Format: Mary S. Guirguis, Beatriz Adrada, Miral Patel, Frances Perez, Rosalind Candelaria, Wei Yang, Jia Sun, Rania M. Mohamed, Medine Boge, H. T. Carisa Le-Petross, Jessica Leung, Gary J. Whitman, Deanna L. Lane, Marion E. Scoggins, Tanya Moseley, Benjamin Musall, Jason White, Sanaz Pashapoor, Peng Wei, Jong Bum Son, Ken-Pin Hwang, Bikash Panthi, Mark Pagel, Lei Huo, Kelly K. Hunt, Elizabeth Ravenberg, Alastair M. Thompson, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Stacy Moulder, Clinton Yam, Jingfei Ma, Gaiane Rauch. DCE-MRI for early prediction of excellent response versus chemoresistance in triple negative breast cancer [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 P1-05-15.

MRI-guided breast biopsy is often necessary to distinguish between benign and malignant lesions depicted at MRI, and meticulous preparation and radiologic-pathologic correlation aid in definitive diagnosis.

Introduction: Neoadjuvant chemotherapy (NACT) is becoming standard of care for presurgical treatment of triple negative breast cancer (TNBC) patients. Achievement of pathological complete response (pCR) after NACT is associated with improved outcomes. There is currently an unmet need in development of imaging and clinical tools for prediction of pCR to NACT in TNBC. We investigated use of deep learning convolution neural networks (CNNs) for early prediction of pCR in a TNBC cohort on the basis of MRI acquired before the initiation and at the midpoint, after completion of four cycles of NACT (C4). Materials and Methods: Baseline and C4 MRIs of 112 TNBC patients were collected from an ongoing prospective clinical trial (NCT02276443). Four patients were excluded because they underwent different treatment for the second regimen. Among the 108 patients, 52 patients (48%) had pCR confirmed at surgery. Positive enhancement integral (PEI) derived from the early phases of DCE MRI, and apparent diffusion coefficients (ADC) derived from DWI MRI (b = 100 and 800 s/mm2), were used for our investigation. The images were aligned and the tumor regions were cropped from all images. All tumor patches were normalized between [0, 1], and were padded to form matrices of the same size of 192×192×64 for PEI, or the size of 192×192×16 for ADC. The CNN was constructed using stacked 3D convolution and MaxPooling layers. It consisted of up to four channels for the inputs (baseline and C4 PEI and ADC). Features extracted from each channel were concatenated and regressed for pCR prediction via three densely connected layers. Binary cross-entropy was used as the loss function for CNN training, and the loss was optimized using an Adam optimizer with the initial learning rate of 0.0001. Because of the currently limited sample size, four-fold cross-validation was used for CNN training and evaluation. The patients were divided into four groups, each group had 27 patients and the pCR:non-pCR ratio was controlled as 13:14. For each fold, one group was reserved as the independent testing group, and the other three groups were combined for network training and internal validation. Receiver operating characteristic (ROC) curve was plotted for each fold of testing, and area under the curve (AUC) was calculated. Final performance of the CNN was determined by averaging the AUCs of the four testing folds. Additionally, to test the prediction efficacy of each input, we trained the CNN under the same settings but used PEI or ADC only as input, and the results were compared. Results: The CNN trained with PEI only achieved an average AUC of 0.65 ± 0.09. The second CNN trained with ADC only achieved an average AUC of 0.72 ± 0.07. The third CNN trained with both PEI and ADC achieved an average AUC of 0.73 ± 0.06. Conclusion and Discussion: Using baseline and mid-treatment MRIs, deep learning CNN showed promising performance to predict pCR in the early course of NACT. The prediction AUC for the independent testing groups was largely improved by using ADC to train the network, indicating that ADC can have more critical information than PEI in assisting pCR prediction during the early course of NACT. Future work includes curation of a larger patient data for network training and evaluation to improve the prediction performance and further validate generalization of the network. We will also explore more advanced network structures, through which the prediction performance can be improved. Four-fold cross-validation AUCs of the network using different data as inputs.PEIADCPEI+ADCFold 10.570.640.66Fold 20.760.800.77Fold 30.660.700.68Fold 40.590.740.79Average0.65 ± 0.090.72 ± 0.070.73 ± 0.06 Citation Format: Zijian Zhou, Nabil A Elshafeey, David E Rauch, Beatriz E Adrada, Rosalind P Candelaria, Mary S Guirguis, Wei Yang, Medine Boge, Rania M Mohamed, Gary J Whitman, Deanna L Lane, Huong C Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral M Patel, Frances Perez, Debu Tripathy, Vicente Valero, Clinton Yam, Stacy Moulder, Jason B White, Jong Bum Son, Mark D Pagel, Jingfei Ma. Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers [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 P1-08-03.

Introduction Breast conservation surgery (BCS) is the treatment of choice for unifocal, early-stage breast cancer. The ability to offer BCS to a wider subset of patients, including those with multifocal/multicentric cancer as well as extensive ductal carcinoma in situ, has emerged over time, especially in those undergoing joint oncoplastic reconstruction and those treated with neoadjuvant therapy. However, localization techniques using multiple radioactive seeds for bracketing in this patient subset have not been validated. Materials and Methods A single-institution retrospective review was conducted of all patients with breast cancer who underwent BCS, guided by multiple bracketed iodine I 125 radioactive seeds between January 2014 and April 2017. Results Bracketing of breast cancer using 2 or more radioactive seeds was performed in 157 breasts in 156 patients. Negative margins were achieved in 124 of 157 (79%) breasts, including 33 cases (21%) that underwent targeted margin reexcision at the time of surgery after intraoperative, multidisciplinary margin assessment. Thirty-three cases (21%) resulted in close or positive margins, of which 11 (7%) and 10 (6.4%) underwent completion mastectomy or repeat lumpectomy, respectively. Twelve patients (7.6%) did not undergo reexcision. En bloc resection was successful in 134 of 157 (85.4%) lumpectomies. Eighty-nine percent of the procedures were coupled with oncoplastic reconstruction. Conclusion Bracketing techniques using multiple radioactive seeds expands the indications for breast conservation therapy in patients who would have traditionally required mastectomy. Intraoperative margin assessment improves surgical and pathologic success. Larger defects created by multifocal resection are optimally managed in concert with oncoplastic reconstruction to minimize asymmetries and aesthetic defects.

The standardization of the AJCC TNM staging system for breast cancer allows physicians to evaluate patients with breast cancer using standard language and criteria, assess treatment response, and c...

PURPOSE Triple negative breast cancer (TNBC) is an aggressive and heterogeneous subtype of breast cancer. Pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) predicts better survival. Early prediction of the treatment response can potentially triage non-responding patients to alternative protocol treatments, spare them of the unneeded toxicity, and improve pCR. We evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on the dynamic contrast enhanced (DCE) and diffusion-weighted imaging (DWI) MRI images obtained early during NAST to predict pCR. MATERIALS AND METHODS This IRB-approved prospective study (NCT02276443) included 182 patients with biopsy proven stage I-III TNBC who had multiparametric MRIs at baseline (BL), post 2 cycles (C2), and post 4 cycles (C4) of NAST before surgery. Tumors and peritumoral regions of 5 mm and 10 mm in thickness were segmented on the 2.5 minutes DCE subtraction images and on the b=800 DWI images. Ten histogram-based first order texture features including mean, minimum, maximum, standard deviation, kurtosis, skewness, 1st, 5th, 95th, and 99th percentile, and 300 radiomic Grey Level Co-occurrence matrix (GLCM) features along with their absolute and relative differences between the 3 imaging time points were extracted from the tumors and from the peritumoral regions with an in-house Matlab toolbox. Treatment response at surgery (pCR vs non-pCR) was documented. The samples were divided into training and testing datasets by a 2:1 ratio. Area under the receiver operating characteristics curve (AUC ROC) was calculated for univariate analysis in predicting pCR. Logistic regression with elastic net regularization was performed for texture feature selection. Parameter optimization was performed by using 5-fold cross-validation based on mean cross-validated AUC in the training set. RESULTS Of 182 TNBC patients, 88 (48%) had pCR and 94 (52%) did not achieve pCR. Eight multivariate models combining radiomic features from both DCE and DWI tumoral and peritumoral regions had AUC > 0.8 (0.807-0.831) with p-value < 0.001 in both training and testing sets. The highest AUC=0.831 was obtained from a model consisting of 15 radiomic features: tumor DWI (5 GLCM features) at C2, peritumoral region on DCE (skewness) at C2, tumor DCE (1st, 5th percentile) at C4, tumor DWI (3 GLCM features) at C4, peritumoral region DWI (1 GLCM feature) at C4, and the relative difference between C4/C2 on DCE (5th, 95th percentile and mean). CONCLUSION Multi-parametric MRI-based radiomics models from the tumor and the peritumoral regions showed high accuracy as potential early predictors of NAST response in TNBC patients. Citation Format: Rania M. Mohamed, Bikash Panthi, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Elizabeth Ravenberg, Alyson Clayborn, Mark Pagel, Huiqin Chen, Jia Sun, Peng Wei, Alastair M. Thompson, Stacy Moulder, Anil Korkut, Lei Huo, Kelly K. Hunt, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Clinton Yam, Jingfei Ma, Gaiane Rauch. Multi-Parametric MRI-Based Radiomics Models from Tumor and Peritumoral Regions as Potential Predictors of Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer Patients [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 P6-01-06.

Background and Purpose Triple negative breast cancer (TNBC) is a biologically aggressive tumor and a refractory subtype of breast cancer due to the lack of therapeutic targets, such as estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2. In this study, we investigated the accuracy of radiomic models based on the dynamic contrast enhanced (DCE) MRI images obtained after the completion of NAST as discriminators of treatment response in TNBC patients. Materials and Methods This IRB-approved prospective study (ARTEMIS trial, NCT02276443) included 181 patients with biopsy proven stage I-III TNBC who Had MRIs after completion of NAST and before surgery. Patients were classified as pathologic complete response (pCR) and non-pCR at the surgery. Tumors were segmented on the 2.5 minutes DCE subtraction images. Regions with necrosis or clip artifacts were excluded from the contour. If tumors were not visible, the tumor bed was contoured. Whole-tumor histogram-based first order texture features (p=10) including mean, minimum, maximum, Standard deviation, kurtosis, skewness, 1st, 5th, 95th, and 99th percentiles, and radiomic (p=300) Grey Level Co-occurrence matrix (GLCM) features were extracted with an in-house Matlab toolbox. The samples were split into training and testing data sets by a 2:1 ratio. For univariate analysis area under the receiver operating characteristics curve (AUC ROC) was performed for pCR status prediction. For texture feature selection logistic regression with elastic net regularization was performed. Parameter optimization was performed by using 5-fold cross-validation based on mean cross-validated AUC in the training set. A P-value less than 0.05 was considered statistically significant. Results Of the total 181 patients, 88 (49%) had pCR and 93 (51%) had non-pCR. Univariate analysis identified 7 statistically significant first order imaging features (Minimum, Maximum, Mean, 1st Percentile, 5th Percentile, 95th Percentile, and 99th Percentile) with AUC >= 0.7 (p< 0.001), in both training and testing data sets. Percentile 5 showed highest AUC = 0.78 (p< 0.001). Two multivariate models were statistically significant at cross-validation with AUC>=0.7. The first model combined 2 first order data (Percentile 1 and Percentile 5) with AUC = 0.73 (p< 0.001). The second model combined 8 first order features (Percentile 1, 5, 95, 99, Mean, Minimum, Maximum, and Skewness) and 24 GLCM features with AUC = 0.7 (p=0.003). Conclusion DCE-MRI radiomic features from tumor and tumor bed regions in TNBC may be helpful imaging biomarkers for predicting treatment response after NAST. Citation Format: Rania M. Mohamed, Bikash Panthi, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Huiqin Chen, Jia Sun, Peng Wei, Jennifer K. Litton, Vicente Valero, Clinton Yam, Mark Pagel, Jingfei Ma, Gaiane Rauch. A Pre-operative Dynamic Contrast Enhanced MRI-Based Radiomics Models as Predictors of Treatment Response after Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer Patients [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 P6-01-35.

Background and Purpose Early prediction of neoadjuvant systemic therapy (NAST) response in triple negative breast cancer (TNBC) patients could potentially aid in the selection of alternative therapies and avoid unnecessary toxicity in patients unlikely to achieve pathologic complete response (pCR) with NAST. In this study, we investigated the radiomic features of the peritumoral and the tumoral regions from dynamic contrast enhanced (DCE) MRI acquired at different time points of NAST for early treatment response prediction in TNBC. Methods and Materials This study included 182 biopsy-confirmed stage I-III TNBC patients enrolled in an IRB approved prospective clinical trial (NCT02276433). All patients underwent DCE-MRI on a GE 3T MRI scanner at baseline (BL), after two (C2) and four (C4) cycles of doxorubicin/cyclophosphamide based chemotherapy and before surgery. The peritumoral and the tumoral regions were segmented manually by two fellowship-trained radiologists using early phase (2.5 min) DCE-MRI subtraction images. Ten first order radiomic features, 300 grey-level-co-occurrence matrix (GLCM) features along with their absolute and relative differences (C4/BL, C2/BL, C4/C2) between the 3 imaging time points were extracted from the peritumoral and the tumoral regions. Patients were randomly divided into training and testing sets in a 2:1 ratio. For univariate analysis, area under the receiver operating characteristics curve (AUC ROC) was measured to determine the features most predictive of pCR/non-pCR. Wilcoxon Rank Sum test was used to test the statistical significance of predictive performance. In multivariate analysis, radiomic models were established using logistic regression with elastic net regularization followed by 5-fold cross validation for performance assessment. Results Eighty-eight (48%) patients had pCR (59 training, 29 testing) and 94 (52%) patients had non-pCR (63 training, 31 testing). Twenty-five radiomic features (4 from peritumoral C4, 5 from tumoral C4, 4 from peritumoral C4/BL, 6 from tumoral C4/BL, 2 from peritumoral C4/C2 and 4 from tumoral C4/C2) were statistically significant with AUC ≥ 0.75 in both the training and the testing sets at the univariate analysis. The significant features at C4 had AUCs of 0.75-0.79 for the training set and 0.76-0.81 for the testing set. Changes measured between C4 and BL or C2 showed AUC of 0.76-0.84 in the training and 0.75-0.81 in the testing datasets. Eleven multivariate regression models comprised of radiomic features at BL, C2, C4 and their changes (C4/BL, C4/C2 and C2/BL) showed an AUC of 0.80-0.84 for cross validation and an AUC of 0.80-0.82 for independent testing. Conclusions Radiomic models using longitudinal DCE MRI parameters of peritumoral and tumoral regions during NAST have the potential to predict pCR in TNBC patients undergoing NAST. Citation Format: Bikash Panthi, Rania M. Mohamed, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Elizabeth Ravenberg, Alyson Clayborn, Mark Pagel, Huiqin Chen, Jia Sun, Peng Wei, Alastair M. Thompson, Stacy Moulder, Anil Korkut, Lei Huo, Kelly K. Hunt, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Clinton Yam, Jingfei Ma, Gaiane Rauch. Longitudinal DCE-MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy (NAST) in Triple Negative Breast Cancer (TNBC) Patients [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 P6-01-34.

Breast augmentation is one of the most common aesthetic procedures performed in the United States. Several techniques of breast augmentation have been developed, including the implantation of breast prostheses and the injection of autologous fat and other materials. The most common method of breast augmentation is to implant a prosthesis. There are different types of breast implants that vary in shape, composition, and the number of lumina. The rupture of breast implants is the leading cause of implant removal. The rupture rate increases substantially with the increasing age of the implant. Most implant ruptures are asymptomatic. Implant complications can be grouped into two categories: local complications in the breast and adjacent soft tissue, and systemic complications associated with rheumatologic or neurologic symptoms. The onset of local complications may be early (infection and periprosthetic collections including seromas, hematomas, or abscesses) or late (capsular contraction, implant rupture, gel bleed, or breast implant-associated anaplastic large cell lymphoma). Although mammography is the imaging modality for breast cancer screening, noncontrast breast MRI is the imaging modality of choice for evaluation of the integrity of breast implants and the complications of breast augmentation, for equivocal findings at conventional imaging, and as a supplement to mammography in patients with free injectable materials. The fifth edition of the Breast Imaging Reporting and Data System (BI-RADS) provides a systematic outline for MRI evaluation of patients with breast implants. Silicone- and water-selective sequences provide useful supplemental information to confirm intracapsular and extracapsular rupture. Breast MRI for evaluation of implant integrity does not require intravenous contrast material. The use of MRI contrast material in patients with breast augmentation is indicated when infection or malignancy is suspected. Radiologists should have a thorough understanding of the different techniques for breast augmentation, normal imaging features, and complications specific to breast augmentation.

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann–Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.

INTRODUCTION: Breast conservation surgery (BCS) is the treatment of choice for unifocal, early-stage breast cancer. The ability to offer BCS to a wider subset of patients, including those with multifocal/multicentric cancer as well as extensive DCIS has emerged over time, especially in those undergoing joint oncoplastic reconstruction and those treated with neoadjuvant therapy. However, localization techniques using multiple radioactive seeds for bracketing in this patient subset has not been validated. MATERIALS AND METHODS: A single-institution retrospective review was conducted of all patients with breast cancer who underwent BCS, guided by multiple bracketed I-125 radioactive seeds between January 2014 and April 2017. RESULTS: Bracketing of breast cancer using two or more radioactive seeds was performed in 157 breasts in 156 patients. Negative margins were achieved in 124/157 (79%) breasts, including 33 cases (21%) that underwent targeted margin re-excision at the time of surgery after intraoperative, multidisciplinary margin assessment. Thirty-three cases (21.0%) resulted in close or positive margins, of which 11 (7%) and 10 (6.4%) underwent completion mastectomy or repeat lumpectomy, respectively. Twelve patients (7.6%) did not undergo re-excision. En bloc resection was successful in 134/157 (85.4%) lumpectomies. Eighty-nine percent of the procedures were coupled with oncoplastic reconstruction. CONCLUSION: Bracketing techniques using multiple radioactive seeds expands the indications for breast conservation therapy (BCT) in patients who would have traditionally required mastectomy. Intraoperative margin assessment improves surgical and pathologic success. Larger defects created by multifocal resection are optimally managed in concert with oncoplastic reconstruction to minimize asymmetries and aesthetic defects.

Ultrasound evaluation of the axilla plays a critical role in the setting of newly diagnosed breast cancer as surgical management evolves toward more targeted axillary nodal resection. Regional nodal involvement by metastatic carcinoma is one of most important prognostic factors in breast cancer and guides local, regional, and systemic treatment. Ultrasound also evaluates response to neoadjuvant chemotherapy. This article will review ultrasound techniques and the anatomy and the morphology of axillary lymph nodes. Lymph node staging in breast cancer will also be discussed. Ultrasound-guided interventions and localizations and emerging technologies of elastography and contrast-enhanced ultrasound will be discussed. In addition, this article will discuss the role of ultrasound as it applies to management of the axilla since the American College of Surgeons Oncology Group Z011 and Z1071 trials. Finally, other causes of benign and malignant axillary lymphadenopathy, not related to breast cancer, are discussed.

Background: Triple negative breast cancer (TNBC) has a poor prognosis. In particular, TNBC patients who have significant residual disease at the time of surgery following completion of neoadjuvant systemic therapy (NST) have an especially poor prognosis. In an effort to identify patients who are unlikely to achieve pathologic complete response (pCR), we investigated if pre-treatment breast MRI morphological characteristics and imaging response patterns during NST can predict pCR in TNBC patients. Materials and Methods: As part of a prospective IRB-approved clinical trial (ARTEMIS, NCT02276443), 199 patients with biopsy-proven stage I-III TNBC received NST and were classified as pCR or non-pCR based on histopathology at surgery. Patients underwent breast MRI at baseline (BL), after 2 cycles (C2), and 4 cycles (C4) of Adriamycin-based chemotherapy (AC). Subsequently, patients received either taxane-based NST or targeted therapy guided by mid-treatment imaging response. MRI studies were reviewed by two fellowship-trained breast radiologists who were blinded to the pathology results. ACR MRI BIRADS lexicon (5th Ed) was used to describe BL tumor morphology. Imaging response pattern at C2 and C4 MRI was classified as follows: type 0 (complete), type 1 (concentric shrinkage), type 2 (crumble), type 3 (diffuse enhancement), type 4 (stable), or type 5 (progression). Morphological baseline features and response patterns were summarized and compared to the pCR status on surgical pathology using Fisher’s exact test. P values less than 0.05 were considered statistically significant. Results: Median age was 53 years, range 24-79. Of 199 patients, 95 (48%) had pCR and 104 (52%) had non-pCR. At BL MRI, an irregularly-shaped mass and homogenous or clumped non-mass enhancement were associated with pCR (p=0.026 and p=0.013, respectively). Multifocality, peritumoral edema, and intratumoral necrosis were independent of pCR. Following NST, the most common MRI response pattern was type 1, seen with equal frequency in pCR and non-pCR at C2 (58% and 42%, respectively) and C4 (47% and 53%, respectively). The following response pattern associations were found: type 0 was associated with pCR at both C2 and C4 timepoints (p Citation Format: Mary S Guirguis, Beatriz E Adrada, Rosalind P Candelaria, Jia Sun, Gary J Whitman, Wei T Yang, Medine Boge, Rania M Mohamed, Nabil A Elshafeey, Deanna L Lane, Huong Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral Patel, Frances Perez, Peng Wei, Debu Tripathy, Jason White, Elizabeth Ravenberg, Lei Huo, Jennifer Litton, Banu Arun, Vincente Valero, Alastair Thompson, Stacy Moulder, Clinton Yam, Gaiane M Rauch. Prediction of response to neoadjuvant systemic therapy in triple negative breast cancer using baseline tumor MRI characteristics and imaging patterns of response [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 P3-03-06.

68Ga-DOTA peptides have revolutionized the imaging of neuroendocrine tumors because the agents are specific to somatostatin receptors. However, other tumors, including breast cancer, have been shown to express somatostatin receptors. We present the case of a 74-year-old woman with primary cardiac paraganglioma, who was found to have 68Ga-DOTATATE activity in the breast on staging PET/CT. Subsequent breast imaging workup and biopsy confirmed primary invasive lobular breast cancer, which was not 18F-FDG-avid on prior FDG PET/CT. Our case is in alignment with prior studies that suggest that 68Ga-DOTA peptides may play a future role in imaging breast cancer patients.

Multinodular lung disease is routinely encountered on chest computed tomography (CT). Pulmonary nodules may be categorized as perilymphatic, random, or centrilobular, based on their CT distribution. Recognition of the pattern of distribution allows the differential diagnosis to be narrowed to a few common diseases. This review illustrates the CT appearance and provides a practical differential diagnosis of each pattern of nodules.

The complication rate following radiofrequency catheter ablation for atrial fibrillation is low (

A 72-year-old man with a history of vertebroplasty 3 months ago presented with fever. Chest radiograph revealed thin radioopaque linear and branching opacities (arrows, Figure 1A). Findings were compatible with cement pulmonary embolism. Abdominal radiograph revealed evidence of prior vertebroplasty (arrows, Figure 1B). Percutaneous vertebroplasty and balloon kyphoplasty are procedures performed to treat painful vertebral fractures. These procedures involve the injection of polymethylmethacrylate (PMMA) into the vertebral body. Cement pulmonary embolism is a potential complication of these procedures with incidence rates ranging from 3.5% to 23%. Cement pulmonary embolism results from the leakage of PMMA into the perivertebral veins. The PMMA eventually travels from the venous system into the pulmonary arteries. In most cases, cement pulmonary emboli are asymptomatic and discovered incidentally on postoperative chest radiographs. Treatment depends on the presence of symptoms and may include clinical observation, anticoagulation, or in rare instances, surgical removal. The patient in this case was not treated because he was asymptomatic.