Abstract
To develop a convolutional neural network (CNN) algorithm that can predict the molecular subtype of a breast cancer based on MRI features. An IRB-approved study was performed in 216 patients with available pre-treatment MRIs and immunohistochemical staining pathology data. First post-contrast MRI images were used for 3D segmentation using 3D slicer. A CNN architecture was designed with 14 layers. Residual connections were used in the earlier layers to allow stabilization of gradients during backpropagation. Inception style layers were utilized deeper in the network to allow learned segregation of more complex feature mappings. Extensive regularization was utilized including dropout, L2, feature map dropout, and transition layers. The class imbalance was addressed by doubling the input of underrepresented classes and utilizing a class sensitive cost function. Parameters were tuned based on a 20% validation group. A class balanced holdout set of 40 patients was utilized as the testing set. Software code was written in Python using the TensorFlow module on a Linux workstation with one NVidia Titan X GPU. Seventy-four luminal A, 106 luminal B, 13 HER2+, and 23 basal breast tumors were evaluated. Testing set accuracy was measured at 70%. The class normalized macro area under receiver operating curve (ROC) was measured at 0.853. Non-normalized micro-aggregated AUC was measured at 0.871, representing improved discriminatory power for the highly represented Luminal A and Luminal B subtypes. Aggregate sensitivity and specificity was measured at 0.603 and 0.958. MRI analysis of breast cancers utilizing a novel CNN can predict the molecular subtype of breast cancers. Larger data sets will likely improve our model.
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References
Siegel R, Ma J, Zou Z, Jemal A: Cancer statistics, 2014. CA Cancer J Clin 64:9–29, 2014
Perou CM, Sørlie T, Eisen MB et al.: Molecular portraits of human breast tumours. Nature 406(6797):747–752, 2000
Morris EA: Diagnostic breast MR imaging: current status and future directions. Magn Reson Imaging Clin N Am 18:57–74, 2010
Liberman L, Morris EA, Dershaw DD et al.: MR imaging of the ipsilateral breast in women with percutaneously proven breast cancer. AJR Am J Roentgenol 180(4):901–910, 2003
Schelfout K, Van Goethem M, Kersschot E et al.: Contrast-enhanced MR imaging of breast lesions and effect on treatment. Eur J Surg Oncol. 30(5):501–507, 2004
Sørlie T, Perou CM, Tibshirani R et al.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98(19):10869–10874, 2001
Wiechmann L, Sampson M, Stempel M et al.: Presenting features of breast cancer differ by molecular subtype. Ann Surg Oncol 16(10):2705–2710, 2009
Morrow M, Waters J, Morris E: MRI for breast cancer screening, diagnosis, and treatment. Lancet 378:1804–1811, 2011
Goldhirsch A, Wood WC, Coates AS et al.: Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 22(8):1736–1747, 2011
Metzger-Filho O, Sun Z, Viale G et al.: Patterns of recurrence and outcome according to breast cancer subtypes in lymph node-negative disease: results from international Breast Cancer Study Group Trials VIII and IX. J Clin Oncol 31(25):3083–3090, 2013
Carey LA, Dees EC, Sawyer L, Gatti L, Moore DT, Collichio F et al.: The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes. Clin Cancer Res 13(8):2329–2334, 2007
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB et al.: Radiomics: the process and the challenges. Magn Reson Imaging 30(9):1234–1248, 2012
Kuo MD, Jamshidi N: Behind the numbers: Decoding molecular phenotypes with radiogenomics—guiding principles and technical considerations. Radiology 270(2):320–325, 2014
Holli-Helenius K, Salminen A, Rinta-Kiikka I et al.: MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study. BMC Med Imaging 17(1):69, 2017
Chen W, Giger ML, Lan L, Bick U: Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys 31:1076–1108, 2004
Guo W, Li H, Zhu Y et al.: Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging (Bellingham) 2:041007, 2015
Fan M, Li H, Wang S et al.: Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer. PLoS One 12(2):e0171683, 2017
Bhooshan N, Giger ML, Jansen SA et al.: Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology. 254(3):680–690, 2010
Bhooshan N, Giger M, Edwards D et al.: Computerized three-class classification of MRI-based prognostic markers for breast cancer. Phys Med Biol 56(18):5995–6008, 2011
Agner SC, Rosen MA, Englander S et al.: Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study. Radiology 272:91–99, 2014
Mazurowski MA, Zhang J, Grimm LJ et al.: Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 273(2):365–372, 2014
Grimm LJ, Zhang J, Mazurowski MA: Computational approach to radiogenomics of breast cancer: luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging 42(4):902–907, 2015
Yamamoto S, Han W, Kim Y et al.: Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis. Radiology 275(2):384–392, 2015
Ashraf AB, Daye D, Gavenonis S et al.: Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology 272(2):374–384, 2014
Yamaguchi K, Abe H, Newstread G et al.: Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer. Breast Cancer 22(5):496–502, 2015
Blaschke E, Abe H: MRI phenotype of breast cancer: kinetic assessment for molecular subtypes. J Magn Reson Imaging 42(4):920–924, 2015
LeChun Y, Bengio T, Hinton G: Deep learning. Nature 521:436–444, 2015
Ha R, Jin B, Mango V et al.: Breast cancer molecular subtype as a predictor of the utility of preoperative MRI. AJR Am J Roentgenol 204(6):1354–1360, 2015
Carey LA, Perou CM, Livasy CA et al.: Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 295:2492–2502, 2006
Nguyen PL, Taghian AG, Katz MS et al.: Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and HER-2 is associated with local and distant recurrence after breast-conserving therapy. J Clin Oncol 26(14):2373–2828, 2008
LeCun Y, Bottou L, Bengio Y et al.: Gradient-based learning applied to document recognition. Proceed IEEE 86(11):2278–2324, 1998
He K, Zhang X, Ren S, et al: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp 770–778
Nair, V, Hinton GE: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), 2010, pp 807–814
Ioffe S, Szegedy C: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, 2015
Srivastava N, Hinton G, Krizhevsky A et al.: Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958, 2014
Kingma DP, Jimmy BA: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014
Nesterov Y: Gradient methods for minimizing composite objective function. 2007
Dozat T: Incorporating nesterov momentum into adam. 2016
Glorot X, Bengio Y: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010, pp 249–256
Zhu Z, Albadawy E, Saha A, et al: Breast cancer molecular subtype classification using deep features: preliminary results. In: Proceedings Volume 10575, Medical imaging 2018: computer-aided diagnosis; 105752X. 2018
Sun C, Shrivastaval A, Singh S, et al: Revisiting unreasonable effectiveness of data in deep learning Era. arXiv preprint arXIV:1707.02968. 2017
Guiu S, Michiels S, André F et al.: Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement. Ann Oncol 23(12):2997–3006, 2012
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Ha, R., Mutasa, S., Karcich, J. et al. Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm. J Digit Imaging 32, 276–282 (2019). https://doi.org/10.1007/s10278-019-00179-2
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DOI: https://doi.org/10.1007/s10278-019-00179-2