Deep Learning for identifying radiogenomic associations in breast cancer
RATIONALE AND OBJECTIVES To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast enhanced magnetic resonance imaging (DCE MRI) MATERIALS AND METHODS In this institutional review board approved single center study, we analyzed DCE MR images of 270 patients at our institution. Lesions of interest were identified by radiologists. The task was to automatically determine whether the tumor is of the Luminal A subtype or of another subtype based on the MR image patches representing the tumor. Three different deep learning approaches were used to classify the tumor according to their molecular subtypes: learning from scratch where only tumor patches were used for training, transfer learning where networks pre trained on natural images were fine tuned using tumor patches, and off the shelf deep features where the features extracted by neural networks trained on natural images were used for classification with a support vector machine. Network architectures utilized in our experiments were GoogleNet, VGG, and CIFAR. We used 10 fold crossvalidation method for validation and area under the receiver operating characteristic (AUC) as the measure of performance. RESULTS The best AUC performance for distinguishing molecular subtypes was 0.65 (95% CI:[0.57,0.71] and was achieved by the off the shelf deep features approach. The highest AUC performance for training from scratch was 0.58 (95% CI:[0.51,0.64] and the best AUC performance for transfer learning was 0.60 (95% CI:[0.52,0.65] respectively. For the off the shelf approach, the features extracted from the fully connected layer performed the best. CONCLUSION Deep learning may play a role in discovering radiogenomic associations in breast cancer.
Comput. Biol. Medicine