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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References
Ivens D, Hoe AL, Podd TJ, Hamilton CR, Taylor I, Royle GT: Assessment of morbidity from complete axillary dissection. Br J Cancer 66(1):136–138, 1992
Duff M, Hill AD, McGreal G, Walsh S, McDermott EW, O’Higgins NJ: Prospective evaluation of the morbidity of axillary clearance for breast cancer. Br J Surg 88(1):114–117, 2001
Weiser MR, Montgomery LL, Susnik B, Tan LK, Borgen PI, Cody HSI: routine intraoperative frozen-section examination of sentinel lymph nodes in breast cancer worthwhile? Ann Surg Oncol 7(9):651–655, 2000
Krishnamurthy S, Meric-Bernstam F, Lucci A, Hwang RF, Kuerer HM, Babiera G, Ames FC, Feig BW, Ross MI, Singletary E, Hunt KK, Bedrosian IA: prospective study comparing touch imprint cytology, frozen section analysis, and rapid cytokeratin immunostain for intraoperative evaluation of axillary sentinel lymph nodes in breast cancer. Cancer 115(7):1555–1562, 2009. https://doi.org/10.1002/cncr.24182.
Vanderveen KA, Ramsamooj R, Bold RJA: prospective, blinded trial of touch prep analysis versus frozen section for intraoperative evaluation of sentinel lymph nodes in breast cancer. Ann Surg Oncol 15(7):2006–2011, 2008. https://doi.org/10.1245/s10434-008-9944-8.
Pogacnik A, Klopcic U, Grazio-Frković S, Zgajnar J, Hocevar M, Vidergar-Kralj B: The reliability and accuracy of intraoperative imprint cytology of sentinel lymph nodes in breast cancer. Cytopathology 16(2):71–76, 2005
Akay CL, Albarracin C, Torstenson T, Bassett R, Mittendorf EA, Yi M, Kuerer HM, Babiera GV, Bedrosian I, Hunt KK, Hwang RF: Factors impacting the accuracy of intra-operative evaluation of sentinel lymph nodes in breast cancer. Breast J 24(1):28–34, 2018. https://doi.org/10.1111/tbj.12829
Ballal H, Hunt C, Bharat C, Murray K, Kamyab R, Saunders C: Arm morbidity of axillary dissection with sentinel node biopsy versus delayed axillary dissection. ANZ J Surg, 2018. https://doi.org/10.1111/ans.14382
Renaudeau C, Lefebvre-Lacoeuille C, Campion L, Dravet F, Descamps P, Ferron G, Houvenaeghel G, Giard S, Tunon de Lara C, Dupré PF, Fritel X, Ngô C, Verhaeghe JL, Faure C, Mezzadri M, Damey C, Classe JM: Evaluation of sentinel lymph node biopsy after previous breast surgery for breast cancer: GATA study. Breast 28:54–59, 2016. https://doi.org/10.1016/j.breast.2016.04.006.
An YS, Lee DH, Yoon JK, Lee SJ, Kim TH, Kang DK, Kim KS, Jung YS, Yim H: Diagnostic performance of 18F-FDG PET/CT, ultrasonography and MRI. Detection of axillary lymph node metastasis in breast cancer patients. Nuklearmedizin 53(3):89–94, 2014. https://doi.org/10.3413/Nukmed-0605-13-06.
Cooper KL, Meng Y, Harnan S, Ward SE, Fitzgerald P, Papaioannou D, Wyld L, Ingram C, Wilkinson ID, Lorenz E: Positron emission tomography (PET) and magnetic resonance imaging (MRI) for the assessment of axillary lymph node metastases in early breast cancer: systematic review and economic evaluation. Health Technol Assess 15(4):iii–iiv, 1–134, 2011. https://doi.org/10.3310/hta15040
Hwang SO, Lee SW, Kim HJ, Kim WW, Park HY, Jung JH: The comparative study of ultrasonography, contrast-enhanced MRI, and (18)F-FDG PET/CT for detecting axillary lymph node metastasis in T1 breast cancer. J Breast Cancer 16(3):315–321, 2013. https://doi.org/10.4048/jbc.2013.16.3.315
Scaranelo AM, Eiada R, Jacks LM, Kulkarni SR, Crystal P: Accuracy of unenhanced MR imaging in the detection of axillary lymph node metastasis: study of reproducibility and reliability. Radiology 262(2):425–434, 2012. https://doi.org/10.1148/radiol.11110639.
Hieken TJ, Trull BC, Boughey JC, Jones KN, Reynolds CA, Shah SS, Glazebrook KN: Preoperative axillary imaging with percutaneous lymph node biopsy is valuable in the contemporary management of patients with breast cancer. Surgery 154(4):831–838, 2013
Abe H, Schacht D, Kulkarni K, Shimauchi A, Yamaguchi K, Sennett CA, Jiang Y: Accuracy of axillary lymph node staging in breast cancer patients: an observer-performance study comparison of MRI and ultrasound. Acad Radiol 20(11):1399–1404, 2013. https://doi.org/10.1016/j.acra.2013.08.003
LeCun Y, Bengio Y, Hinton G: Deep learning. Nature 521(7553):436–444, 2015. https://doi.org/10.1038/nature14539.
Pieper S, Lorensen B, Schroeder W, et al: The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1:698–701, 2006.
Simonyan K, Zisserman A: Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations. 2015, p. 1–14
Nair V, Hinton GE: Rectified linear units improve restricted Boltzmann machines. https://www.cs.toronto.edu/~hinton/absps/reluICML.pdf
Ioffe S, Szegedy C: “Batch normalization: accelerating deep network training by reducing internal covariate shift.” International Conference on Machine Learning. 2015
Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R: Dropout : a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958, 2014
Kingma DP, Ba J: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014
He K, Zhang X, Ren S, et al: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. arXiv:1502.01852 https://arxiv.org/pdf/1502.01852.pdf
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This work has been accepted for oral presentation at the upcoming 2018 ARRS meeting.
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Ha, R., Chang, P., Karcich, J. et al. Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset. J Digit Imaging 31, 851–856 (2018). https://doi.org/10.1007/s10278-018-0086-7
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DOI: https://doi.org/10.1007/s10278-018-0086-7