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J Digit Imaging. 2018 Dec;31(6):851-856. doi: 10.1007/s10278-018-0086-7.

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.

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Department of Radiology, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA.
Department of Radiology, T32 Training Grant (NIH T32EB001631), UC San Francisco Medical Center, 505 Parnassus Ave, San Francisco, CA, 94143, USA.
Department of Radiology, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA.
Department of Medical Physics, Columbia University Medical Center, 177 Ft. Washington Ave., Milstein Bldg Room 3-124B, New York, NY, 10032-3784, USA.


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.


Axillary metastasis; CNN; MRI

[Available on 2019-12-01]

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