Breast cancer molecular subtype classification using deep features: preliminary results

Zhe Zhu; Ehab Albadawy; Ashirbani Saha; Jun Zhang; Michael R. Harowicz; Maciej A. Mazurowski

doi:10.1117/12.2295471

27 February 2018 Breast cancer molecular subtype classification using deep features: preliminary results

Zhe Zhu, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz, Maciej A. Mazurowski

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105752X (2018) https://doi.org/10.1117/12.2295471
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Radiogenomics is a field of investigation that attempts to examine the relationship between imaging characteris- tics of cancerous lesions and their genomic composition. This could offer a noninvasive alternative to establishing genomic characteristics of tumors and aid cancer treatment planning. While deep learning has shown its supe- riority in many detection and classification tasks, breast cancer radiogenomic data suffers from a very limited number of training examples, which renders the training of the neural network for this problem directly and with no pretraining a very difficult task. In this study, we investigated an alternative deep learning approach referred to as deep features or off-the-shelf network approach to classify breast cancer molecular subtypes using breast dynamic contrast enhanced MRIs. We used the feature maps of different convolution layers and fully connected layers as features and trained support vector machines using these features for prediction. For the feature maps that have multiple layers, max-pooling was performed along each channel. We focused on distinguishing the Luminal A subtype from other subtypes. To evaluate the models, 10 fold cross-validation was performed and the final AUC was obtained by averaging the performance of all the folds. The highest average AUC obtained was 0.64 (0.95 CI: 0.57-0.71), using the feature maps of the last fully connected layer. This indicates the promise of using this approach to predict the breast cancer molecular subtypes. Since the best performance appears in the last fully connected layer, it also implies that breast cancer molecular subtypes may relate to high level image features

Citation Download Citation

Zhe Zhu, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz, and Maciej A. Mazurowski "Breast cancer molecular subtype classification using deep features: preliminary results", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105752X (27 February 2018); https://doi.org/10.1117/12.2295471

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