Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset

We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review of our database from January 2009 to June 2016 identified 141 locally advanced breast cancer patients who (1) underwent breast MRI prior to the initiation of NAC, (2) successfully completed adriamycin/taxane-based NAC, and (3) underwent surgical resection with available final surgical pathology data. Patients were classified into three groups based on their NAC response confirmed on final surgical pathology: complete (group 1), partial (group 2), and no response/progression (group 3). A total of 3107 volumetric slices of 141 tumors were evaluated. Breast tumor was identified on first T1 postcontrast dynamic images and underwent 3D segmentation. CNN consisted of ten convolutional layers, four max-pooling layers, and dropout of 50% after a fully connected layer. Dropout, augmentation, and L2 regularization were implemented to prevent overfitting of data. Non-linear functions were modeled by a rectified linear unit (ReLU). Batch normalization was used between the convolutional and ReLU layers to limit drift of layer activations during training. A three-class neoadjuvant prediction model was evaluated (group 1, group 2, or group 3). The CNN achieved an overall accuracy of 88% in three-class prediction of neoadjuvant treatment response. Three-class prediction discriminating one group from the other two was analyzed. Group 1 had a specificity of 95.1% ± 3.1%, sensitivity of 73.9% ± 4.5%, and accuracy of 87.7% ± 0.6%. Group 2 (partial response) had a specificity of 91.6% ± 1.3%, sensitivity of 82.4% ± 2.7%, and accuracy of 87.7% ± 0.6%. Group 3 (no response/progression) had a specificity of 93.4% ± 2.9%, sensitivity of 76.8% ± 5.7%, and accuracy of 87.8% ± 0.6%. It is feasible for current deep CNN architectures to be trained to predict NAC treatment response using a breast MRI dataset obtained prior to initiation of chemotherapy. Larger dataset will likely improve our prediction model.

Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement

The aim of this study is to develop a fully automated convolutional neural network (CNN) method for quantification of breast MRI fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). An institutional review board-approved retrospective study evaluated 1114 breast volumes in 137 patients using T1 precontrast, T1 postcontrast, and T1 subtraction images. First, using our previously published method of quantification, we manually segmented and calculated the amount of FGT and BPE to establish ground truth parameters. Then, a novel 3D CNN modified from the standard 2D U-Net architecture was developed and implemented for voxel-wise prediction whole breast and FGT margins. In the collapsing arm of the network, a series of 3D convolutional filters of size 3 × 3 × 3 are applied for standard CNN hierarchical feature extraction. To reduce feature map dimensionality, a 3 × 3 × 3 convolutional filter with stride 2 in all directions is applied; a total of 4 such operations are used. In the expanding arm of the network, a series of convolutional transpose filters of size 3 × 3 × 3 are used to up-sample each intermediate layer. To synthesize features at multiple resolutions, connections are introduced between the collapsing and expanding arms of the network. L2 regularization was implemented to prevent over-fitting. Cases were separated into training (80%) and test sets (20%). Fivefold cross-validation was performed. Software code was written in Python using the TensorFlow module on a Linux workstation with NVIDIA GTX Titan X GPU. In the test set, the fully automated CNN method for quantifying the amount of FGT yielded accuracy of 0.813 (cross-validation Dice score coefficient) and Pearson correlation of 0.975. For quantifying the amount of BPE, the CNN method yielded accuracy of 0.829 and Pearson correlation of 0.955. Our CNN network was able to quantify FGT and BPE within an average of 0.42 s per MRI case. A fully automated CNN method can be utilized to quantify MRI FGT and BPE. Larger dataset will likely improve our model.

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset

The aim of this study is to evaluate the role of convolutional neural network (CNN) in predicting axillary lymph node metastasis, using a breast MRI dataset. An institutional review board (IRB)-approved retrospective review of our database from 1/2013 to 6/2016 identified 275 axillary lymph nodes for this study. Biopsy-proven 133 metastatic axillary lymph nodes and 142 negative control lymph nodes were identified based on benign biopsies (100) and from healthy MRI screening patients (42) with at least 3 years of negative follow-up. For each breast MRI, axillary lymph node was identified on first T1 post contrast dynamic images and underwent 3D segmentation using an open source software platform 3D Slicer. A 32 × 32 patch was then extracted from the center slice of the segmented tumor data. A CNN was designed for lymph node prediction based on each of these cropped images. The CNN consisted of seven convolutional layers and max-pooling layers with 50% dropout applied in the linear layer. In addition, data augmentation and L2 regularization were performed to limit overfitting. Training was implemented using the Adam optimizer, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Code for this study was written in Python using the TensorFlow module (1.0.0). Experiments and CNN training were done on a Linux workstation with NVIDIA GTX 1070 Pascal GPU. Two class axillary lymph node metastasis prediction models were evaluated. For each lymph node, a final softmax score threshold of 0.5 was used for classification. Based on this, CNN achieved a mean five-fold cross-validation accuracy of 84.3%. It is feasible for current deep CNN architectures to be trained to predict likelihood of axillary lymph node metastasis. Larger dataset will likely improve our prediction model and can potentially be a non-invasive alternative to core needle biopsy and even sentinel lymph node evaluation.

Evaluation of association between degree of background parenchymal enhancement on MRI and breast cancer subtype

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.

The unusual suspects: A review of unusual benign and malignant male breast imaging cases

Male breast disease is uncommon. Men presenting with breast symptoms may represent unique diagnostic challenges for the radiologist, particularly if imaging findings are not classic for gynecomastia or carcinoma. In this paper we review 10 unusual male breast cases, 5 benign and 5 malignant, including the radiologic findings, differential diagnosis, pathology and management.

Does breast MRI background parenchymal enhancement indicate metabolic activity? Qualitative and 3D quantitative computer imaging analysis

PURPOSE

To investigate whether the degree of breast magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) is associated with the amount of breast metabolic activity measured by breast parenchymal uptake (BPU) of 18F-FDG on positron emission tomography / computed tomography (PET/CT).

MATERIALS AND METHODS

An Institutional Review Board (IRB)-approved retrospective study was performed. Of 327 patients who underwent preoperative breast MRI from 1/1/12 to 12/31/15, 73 patients had 18F-FDG PET/CT evaluation performed within 1 week of breast MRI and no suspicious findings in the contralateral breast. MRI was performed on a 1.5T or 3.0T system. The imaging sequence included a triplane localizing sequence followed by sagittal fat-suppressed T₂ -weighted sequence, and a bilateral sagittal T₁ -weighted fat-suppressed fast spoiled gradient-echo sequence, which was performed before and three times after a rapid bolus injection (gadobenate dimeglumine, Multihance; Bracco Imaging; 0.1 mmol/kg) delivered through an IV catheter. The unaffected contralateral breast in these 73 patients underwent BPE and BPU assessments. For PET/CT BPU calculation, a 3D region of interest (ROI) was drawn around the glandular breast tissue and the maximum standardized uptake value (SUV_max ) was determined. Qualitative MRI BPE assessments were performed on a 4-point scale, in accordance with BI-RADS categories. Additional 3D quantitative MRI BPE analysis was performed using a previously published in-house technique. Spearman's correlation test and linear regression analysis was performed (SPSS, v. 24).

RESULT

The median time interval between breast MRI and 18F-FDG PET/CT evaluation was 3 days (range, 0-6 days). BPU SUV_max mean value was 1.6 (SD, 0.53). Minimum and maximum BPU SUV_max values were 0.71 and 4.0. The BPU SUV_max values significantly correlated with both the qualitative and quantitative measurements of BPE, respectively (r(71) = 0.59, P < 0.001 and r(71) = 0.54, P < 0.001). Qualitatively assessed high BPE group (BI-RADS 3/4) had significantly higher BPU SUV_max of 1.9 (SD = 0.44) compared to low BPE group (BI-RADS 1/2) with an average BPU SUV_max of 1.17 (SD = 0.32) (P < 0.001). On linear regression analysis, BPU SUV_max significantly predicted qualitative and quantitative measurements of BPE (β = 1.29, t(71) = 3.88, P < 0.001 and β = 19.52, t(71) = 3.88, P < 0.001).

CONCLUSION

There is a significant association between breast BPU and BPE, measured both qualitatively and quantitatively. Increased breast cancer risk in patients with high MRI BPE could be due to elevated basal metabolic activity of the normal breast tissue, which may provide a susceptible environment for tumor growth.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:753-759.

Beyond Wires and Seeds: Reflector-guided Breast Lesion Localization and Excision

Purpose To evaluate outcomes of Savi Scout (Cianna Medical, Aliso Viejo, Calif) reflector-guided localization and excision of breast lesions by analyzing reflector placement, localization, and removal, along with target excision and rates of repeat excision (referred to as re-excision). Materials and Methods A single-institution retrospective review of 100 women who underwent breast lesion localization and excision by using the Savi Scout surgical guidance system from June 2015 to May 2016 was performed. By using image guidance 0-8 days before surgery, 123 nonradioactive, infrared-activated, electromagnetic wave reflectors were percutaneously inserted adjacent to or within 111 breast targets. Twenty patients had two or three reflectors placed for bracketing or for localizing multiple lesions, and when ipsilateral, they were placed as close as 2.6 cm apart. Target and reflector were localized intraoperatively by one of two breast surgeons who used a handpiece that emitted infrared light and electromagnetic waves. Radiographs of the specimen and pathologic analysis helped verify target and reflector removal. Target to reflector distance was measured on the mammogram and radiograph of the specimen, and reflector depth was measured on the mammogram. Pathologic analysis was reviewed. Re-excision rates and complications were recorded. By using statistics software, descriptive statistics were generated with 95% confidence intervals (CIs) calculated. Results By using sonographic (40 of 123; 32.5%; 95% CI: 24.9%, 41.2%) or mammographic (83 of 123; 67.5%; 95% CI: 58.8% 75.1%) guidance, 123 (100%; 95% CI: 96.4%, 100%) reflectors were placed. Mean mammographic target to reflector distance was 0.3 cm. All 123 (100%; 95% CI: 96.4%, 100%) targets and reflectors were excised. Pathologic analysis yielded 54 of 110 malignancies (49.1%; 95% CI: 39.9%, 58.3%; average, 1.0 cm; range, 0.1-5 cm), 32 high-risk lesions (29.1%; 95% CI: 21.4%, 38.2%), and 24 benign lesions (21.8%; 95% CI: 115.1%, 30.4%). Four of 54 malignant cases (7.4%; 95% CI: 2.4%, 18.1%) demonstrated margins positive for cancer that required re-excision. Five of 110 radiographs of the specimen (4.5%; 95% CI: 1.7%, 10.4%) demonstrated increased distance between the target and reflector distance of greater than 1.0 cm (range, 1.1-2.6 cm) compared with postprocedure mammogram the day of placement, three of five were associated with hematomas, two of five migrated without identifiable cause. No related postoperative complications were identified. Conclusion Savi Scout is an accurate, reliable method to localize and excise breast lesions with acceptable margin positivity and re-excision rates. Bracketing is possible with reflectors as close as 2.6 cm. Savi Scout overcomes many limitations of other localization methods, which warrants further study. ^© RSNA, 2017.

The Contact Zone: A Common Site of Tumor Recurrence in a Patient Who Underwent Skin-Sparing Mastectomy and Myocutaneous Flap Reconstruction

Routine magnetic resonance imaging (MRI) screening is not typically warranted in asymptomatic patients with a history of breast cancer and myocutaneous flap reconstruction due to the rare incidence of local tumor recurrence. We present a case of recurrent invasive ductal carcinoma along the contact zone between the transverse rectus abdominis myocutaneous (TRAM) flap and the native breast tissue that was incidentally detected on a routine high-risk screening-MRI of the breast in an asymptomatic patient with a history of breast cancer.

Abstract P4-14-11: National trend in African American women breast cancer research productivity from 1992-2012

INTRODUCTION: Despite extensive progress in breast cancer prevention, diagnosis and treatment over the past 20 years, African American women suffer disproportionate breast cancer morbidity and mortality. In 1998, breast cancer deaths among African American women were 28% higher than in white women and the five-year survival rate for African American women was 71% compared with 86% for white women. The purpose of this bibliometric study is to assess our national research efforts to combat this racial disparity by analyzing breast cancer research productivity in the United States focusing on African American women from 1992 to 2012.

METHODS: This retrospective bibliometric analysis of public data was exempt from Institutional Review Board approval. Articles with "Breast Neoplasm" as a major medical subject heading (MeSH) term published between 1992 and 2012 were identified in the National Library of Medicine MEDLINE database. In addition, articles with "African Continental Ancestry Group" which included African American as a major MeSH term were identified. Country of origin, methodology, journal name, first author specialty and funding sources were recorded. Growth in number of publications was analyzed using linear and nonlinear regression statistical analysis.

RESULTS: A total of 113,721 journal articles were identified with "Breast Neoplasm" as a major MeSH term worldwide, of which 34,155 (30.0%) were published from the United States. Among United States publications, 668 (2.0%) were specific to African ancestral populations. From 1992 to 2012, both African ancestral and non-African ancestral specific articles displayed linear growth patterns (p &lt; 0.0001). National Institute of Health (NIH) funded studies displayed an exponential growth pattern (p &lt; 0.0001) for African ancestral specific articles and displayed a linear growth pattern (p &lt; 0.0001) for non-African ancestral articles. The largest specialty contributor of African ancestral specific articles was Epidemiology and Public Health (33.2%), followed by Medicine (internal medicine, family medicine, obstetrics & gynecology, nursing) (24.7%), Basic Sciences (17.4%), Surgery (15%), Medical and Radiation Oncology (4.7%), Pathology (2.9%) and Radiology (2.1%). African ancestral specific articles were most frequently published in Cancer (10.8%).

CONCLUSION: Among breast cancer publications from the United States, only 2% of the articles were specific to African ancestral population, which is concerning given recent data indicating the persistent ethnic disparity in survival. However, the trend in research productivity in this population is encouraging, largely due to the exponential growth of NIH-funded studies specific for African American women in the past 20 years. NIH-funded research accounts for 52% of all published African American specific breast cancer studies as compared to 40.5% of studies that are non-specific to this population. Ultimately, improvements in breast cancer incidence, mortality, and survival rates in African American women will undoubtedly result from quality research and should continue to be a priority in our nation’s breast cancer research agenda.

Citation Format: Ralph T Wynn, Daniel S Chow, Victoria L Mango, Lauren C Friedlander, Richard Ha. National trend in African American women breast cancer research productivity from 1992-2012 [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-14-11.

Retrospective study of alemtuzumab vs ATG-based conditioning without irradiation for unrelated and matched sibling donor transplants in acquired severe aplastic anemia: a study from the British Society for Blood and Marrow Transplantation

This retrospective national study compared the use of alemtuzumab-based conditioning regimens for hematopoietic SCT (HSCT) in acquired severe aplastic anemia with antithymocyte globulin (ATG)-based regimens. One hundred patients received alemtuzumab and 55 ATG-based regimens. A matched sibling donor (MSD) was used in 87 (56%), matched unrelated donor (MUD) in 60 (39%) and other related or mismatched unrelated donor (UD) in 8 (5%) patients. Engraftment failure occurred in 9% of the alemtuzumab group and 11% of the ATG group. Five-year OS was 90% for the alemtuzumab and 79% for the ATG groups, P=0.11. For UD HSCT, OS of patients was better when using alemtuzumab (88%) compared with ATG (57%), P=0.026, although smaller numbers of patients received ATG. Similar outcomes for MSD HSCT using alemtuzumab or ATG were seen (91% vs 85%, respectively, P=0.562). A lower risk of chronic GVHD (cGVHD) was observed in the alemtuzumab group (11% vs 26%, P=0.031). On multivariate analysis, use of BM as stem cell source was associated with better OS and EFS, and less acute and cGVHD; young age was associated with better EFS and lower risk of graft failure. This large study confirms successful avoidance of irradiation in the conditioning regimens for MUD HSCT patients.

Underestimation of Atypical Ductal Hyperplasia at MRI-Guided 9-Gauge Vacuum-Assisted Breast Biopsy

OBJECTIVE

The purposes of this study were to determine the frequency of diagnosis of atypical ductal hyperplasia (ADH) at MRI-guided 9-gauge vacuum-assisted breast biopsy and to assess the rate of underestimation of ADH at subsequent surgical excision.

MATERIALS AND METHODS

We conducted a retrospective review of medical records of 237 lesions consecutively detected with MRI and then subjected to MRI-guided 9-gauge vacuum-assisted breast biopsy during a 33-month period. Underestimated ADH was defined as a lesion yielding ADH at vacuum-assisted biopsy and cancer at surgery. Scientific tables were used to calculate 95% CI.

RESULTS

Histologic analysis of MRI-guided vacuum-assisted breast biopsy specimens yielded ADH without cancer in 15 (6%) of 237 lesions. Among 15 patients in whom vacuum-assisted breast biopsy yielded ADH, the median age was 52 years (range, 46-68 years). The median number of specimens obtained was nine (range, 8-18 lesions). Median MRI lesion diameter was 1.3 cm (range, 0.7-7.0 cm). Among 15 MRI lesions, 10 (67%) were nonmasslike enhancement and five (33%) were masses. Surgical excision was performed on 13 lesions. Surgical histologic findings were malignancy in five (38%) of the cases, all ductal carcinoma in situ; high-risk lesion in six (46%) of the cases, including ADH without other high-risk lesions (n = 2), ADH and lobular carcinoma in situ (LCIS) (n = 1), ADH, LCIS, and papilloma (n =1), ADH and papilloma (n = 1), and LCIS (n = 1); and benign in two (15%) of the cases. These data indicated an ADH underestimation rate of 38% (95% CI, 14-68%).

CONCLUSION

ADH without cancer was encountered in 6% of MRI-guided 9-gauge vacuum-assisted breast biopsies. ADH at MRI-guided vacuum-assisted breast biopsy is an indication for surgical excision because of the high (38%) frequency of underestimation of these lesions.