Predict Breast Tumor Response to Chemotherapy Using a 3D Deep Learning Architecture Applied to DCE-MRI Data

Purpose: Many breast cancer patients receiving chemotherapy cannot achieve positive response unlimitedly. The main objective of this study is to predict the intra tumor breast cancer response to neoadjuvant chemotherapy (NAC). This aims to provide an early prediction to avoid unnecessary treatment sessions for no responders’ patients.

[1]  K. Polyak,et al.  Intra-tumour heterogeneity: a looking glass for cancer? , 2012, Nature Reviews Cancer.

[2]  Andrew Janowczyk,et al.  A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI , 2018, Medical Imaging.

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Maryellen L. Giger,et al.  Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning , 2017, Medical Imaging.

[5]  Stylianos Drisis,et al.  Breast Cancer Heterogeneity Analysis as Index of Response to Treatment Using MRI Images: A Review , 2017 .

[6]  Gideon Blumenthal,et al.  Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis , 2014, The Lancet.

[7]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[8]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[9]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[10]  M. Bardos,et al.  Comparaison de l'analyse discriminante linéaire et des réseaux de neurones. Application à la détection de défaillance d'entreprises , 1997 .

[11]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[12]  Mohammed Benjelloun,et al.  Analyzing breast tumor heterogeneity to predict the response to chemotherapy using 3D MR images registration , 2017, ICSDE.

[13]  Mohammed Benjelloun,et al.  A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images , 2018, International Journal of Computer Assisted Radiology and Surgery.

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).