Abstract
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.
Method and material: Three-dimensional Dynamic Contrast Enhanced of Magnetic Resonance Images (DCE-MRI) were collected for 42 patients with local breast cancer. This retrospective study is based on a data provided by our collaborating radiology institute in Brussels. According to the pathological complete response (pCR) ground truth, 14 of these patients responded positively to chemotherapy, and 28 were not responsive positively. In this work, a convolutional neural network (CNN) model were used to classify responsive and non-responsive patients. To make this classification, two CNN branches architecture was used. This architecture takes as inputs three views of two aligned DCE-MRI cropped volumes acquired before and after the first chemotherapy. The data was split into 20% for validation and 80% for training. Cross-validation was used to evaluate the proposed CNN model. To assess the model’s performance, the area under the receiver operating characteristic curve (AUC) and accuracy were used.
Results: The proposed CNN architecture was able to predict the breast tumor response to chemotherapy with an accuracy of 91.03%. The Area Under the Curve (AUC) was 0.92.
Discussion: Although the number of subjects remains limited, relevant results were obtained by using data augmentation and three-dimensional tumor DCE-MRI.
Conclusion: Deep CNNs models can be exploited to solve breast cancer follow-up related problems. Therefore, the obtained model can be used in future clinical data other than breast images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Cortazar, P., et al.: Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384(9938), 164–172 (2014)
El Adoui, M., Drisis, S., Benjelloun, M.: Analyzing breast tumor heterogeneity to predict the response to chemotherapy using 3D MR images registration. In: Proceedings of the 2017 International Conference on Smart Digital Environment, pp. 56–63. ACM, July 2017
Marusyk, A., Almendro, V., Polyak, K.: Intra-tumor heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12(5), 323 (2012)
El Adoui, M., Drisis, S., Benjelloun, M.: A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images. Int. J. Comput. Assist. Radiol. Surg. 13, 1233–1243 (2018)
El Adoui, M., Drisis, S., Larhmam, M.A., Lemort, M., Benjelloun, M.: Breast cancer heterogeneity analysis as index of response to treatment using MRI images: a review. Imaging Med. 9(4), 109–119 (2017)
Huynh, B.Q., Antropova, N., Giger, M.L.: Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning. In: Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134, p. 101340U. International Society for Optics and Photonics, March 2017
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Bardos, M., Zhu, W.H.: Comparaison de l’analyse discriminante linéaire et des réseaux de neurones. Application à la détection de défaillance d’entreprises. Revue de statistique appliquée 45(4), 65–92 (1997)
Ravichandran, K., Braman, N., Janowczyk, A., Madabhushi, A.: A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI. In: Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575, p. 105750C. International Society for Optics and Photonics, February 2018
Jenkinson, M., Smith, S.: A global optimization method for robust affine registration of brain images. Med. Image Anal. 5(2), 143–156 (2001)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2015). arXiv preprint arXiv:1511.06434
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010. Physica-Verlag HD, Heidelberg (2010)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
Acknowledgments
Our thanks are addressed to Dr. Marc Lemort, the head of the Radiology Department at Jules Bordet Institute in Brussels, for offering us the dataset used to evaluate our proposed method.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
El Adoui, M., Drisis, S., Benjelloun, M. (2019). Predict Breast Tumor Response to Chemotherapy Using a 3D Deep Learning Architecture Applied to DCE-MRI Data. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-17935-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17934-2
Online ISBN: 978-3-030-17935-9
eBook Packages: Computer ScienceComputer Science (R0)