Breast cancer molecular subtype classification using deep features: Preliminary results

Published

Conference Paper

© 2018 SPIE. 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.

Full Text

Duke Authors

Cited Authors

  • Zhu, Z; Albadawy, E; Saha, A; Zhang, J; Harowicz, MR; Mazurowski, MA

Published Date

  • January 1, 2018

Published In

Volume / Issue

  • 10575 /

International Standard Serial Number (ISSN)

  • 1605-7422

International Standard Book Number 13 (ISBN-13)

  • 9781510616394

Digital Object Identifier (DOI)

  • 10.1117/12.2295471

Citation Source

  • Scopus