  Purpose: To determine whether deep learning-based algorithms applied to
breast MR images can aid in the prediction of occult invasive disease following
the di- agnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.
Material and Methods: In this institutional review board-approved study, we
analyzed dynamic contrast-enhanced fat-saturated T1-weighted MRI sequences of
131 patients at our institution with a core needle biopsy-confirmed diagnosis
of DCIS. The patients had no preoperative therapy before breast MRI and no
prior history of breast cancer. We explored two different deep learning
approaches to predict whether there was a hidden (occult) invasive component in
the analyzed tumors that was ultimately detected at surgical excision. In the
first approach, we adopted the transfer learning strategy, in which a network
pre-trained on a large dataset of natural images is fine-tuned with our DCIS
images. Specifically, we used the GoogleNet model pre-trained on the ImageNet
dataset. In the second approach, we used a pre-trained network to extract deep
features, and a support vector machine (SVM) that utilizes these features to
predict the upstaging of the DCIS. We used 10-fold cross validation and the
area under the ROC curve (AUC) to estimate the performance of the predictive
models. Results: The best classification performance was obtained using the
deep features approach with GoogleNet model pre-trained on ImageNet as the
feature extractor and a polynomial kernel SVM used as the classifier (AUC =
0.70, 95% CI: 0.58- 0.79). For the transfer learning based approach, the
highest AUC obtained was 0.53 (95% CI: 0.41-0.62). Conclusion: Convolutional
neural networks could potentially be used to identify occult invasive disease
in patients diagnosed with DCIS at the initial core needle biopsy.
