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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.

Author information

1
Department of Radiology, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA. rh2616@columbia.edu.
2
Department of Radiology, T32 Training Grant (NIH T32EB001631), UC San Francisco Medical Center, 505 Parnassus Ave, San Francisco, CA, 94143, USA.
3
Department of Radiology, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA.
4
Department of Medical Physics, Columbia University Medical Center, 177 Ft. Washington Ave., Milstein Bldg Room 3-124B, New York, NY, 10032-3784, USA.

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.

KEYWORDS:

Axillary metastasis; CNN; MRI

PMID:
29696472
PMCID:
PMC6261196
[Available on 2019-12-01]
DOI:
10.1007/s10278-018-0086-7

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