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      Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset

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          Abstract

          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.

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          Author and article information

          Contributors
          212-305-1948 , rh2616@columbia.edu
          Journal
          J Digit Imaging
          J Digit Imaging
          Journal of Digital Imaging
          Springer International Publishing (Cham )
          0897-1889
          1618-727X
          25 April 2018
          December 2018
          : 31
          : 6
          : 851-856
          Affiliations
          [1 ] ISNI 0000 0001 2109 4251, GRID grid.240324.3, Department of Radiology, ; 622 West 168th Street, PB-1-301, New York, NY 10032 USA
          [2 ] ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Radiology, T32 Training Grant (NIH T32EB001631), , UC San Francisco Medical Center, ; 505 Parnassus Ave, San Francisco, CA 94143 USA
          [3 ] ISNI 0000 0001 2285 2675, GRID grid.239585.0, Department of Medical Physics, , Columbia University Medical Center, ; 177 Ft. Washington Ave., Milstein Bldg Room 3-124B, New York, NY 10032-3784 USA
          Author information
          http://orcid.org/0000-0003-1704-2875
          Article
          PMC6261196 PMC6261196 6261196 86
          10.1007/s10278-018-0086-7
          6261196
          29696472
          89908551-a8eb-4ad4-aa27-00086cd31e82
          © Society for Imaging Informatics in Medicine 2018
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          © Society for Imaging Informatics in Medicine 2018

          CNN,MRI,Axillary metastasis
          CNN, MRI, Axillary metastasis

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