Deep learning for identifying radiogenomic associations in breast cancer.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • June 2019

Published In

Volume / Issue

  • 109 /

Start / End Page

  • 85 - 90

PubMed ID

  • 31048129

Pubmed Central ID

  • 31048129

Electronic International Standard Serial Number (EISSN)

  • 1879-0534

Digital Object Identifier (DOI)

  • 10.1016/j.compbiomed.2019.04.018

Language

  • eng

Conference Location

  • United States