In this study, we proposed a multi-space-enabled deep learning modeling method for predicting Oncotype DX recurrence risk categories from digital mammogram images on breast cancer patients. Our study included 189 estrogen receptor-positive (ER+) and node-negative invasive breast cancer patients, who all have Oncotype DX recurrence risk score available. Breast tumors were segmented manually by an expert radiologist. We built a 3- channel convolutional neural network (CNN) model that accepts three-space tumor data: the spatial intensity information and the phase and amplitude components in the frequency domain. We compared this multi-space model to a baseline model that is based on sorely the intensity information. Classification accuracy is based on 5- fold cross-validation and average area-under the receiver operating characteristics curve (AUC). Our results showed that the 3-channel multi-space CNN model achieved a statistically significant improvement than the baseline model.
The essential sequences in breast magnetic resonance imaging (MRI) are the dynamic contrast-enhanced (DCE) images, which are widely used in clinical settings. Diffusion-weighted imaging (DWI) MRI also plays an important role in many diagnostic applications and in developing novel imaging bio-makers. Compared to DCE MRI, technical advantages of DWI include a shorter acquisition time, no need for administration of any contrast agent, and availability on most commercial scanners. Segmenting the whole-breast region is an essential pre-processing step in many quantitative and radiomics breast MRI studies. However, it is a challenging task for computerized methods due to the low contrast of intensity along breast chest wall boundaries. While several studies have reported computational methods for automated whole-breast segmentation in DCE MRI, the segmentation in DWI MRI is still underdeveloped. In this paper, we propose to use deep learning and transfer learning methods to segment the whole-breast in DWI MRI, by leveraging pretraining on a DCE MRI dataset. Experiments are reported in multiple breast MRI datasets including an external evaluation dataset and encouraging results are demonstrated.
Breast cancer risk prediction refers to the task of predicting whether a healthy patient is likely to develop breast cancer in the future. Breast density and parenchymal texture features are well-known imaging-based breast cancer risk markers that can be qualitatively/visually assessed by radiologists or even quantitatively measured by computerized software. Recently, deep learning has emerged as a promising strategy to solve tasks in a variety of classification and prediction scenarios, including breast imaging. Building on this premise, we propose a deep learning-based modeling method for breast cancer risk prediction in a case-control setting purely using prior normal screening mammogram images. In addition, considering the fact that clinical statistics shows that the upper outer quadrant is the most common site of origin for breast cancer, we designed a simple experiment on 226 patients (a total of 1,632 images) to explore the concept of localized breast cancer risk prediction. We built two deep learning models with the same settings but fed one with the top halves of the mammogram images (corresponding to the outer portion of a breast) and the other with the bottom halves (corresponding to the inner portion of a breast). Our preliminary results showed that the top halves have a higher prediction performance (AUC=0.89) than the bottom halves (AUC=0.69) in predicting the case/control outcome. This indicates a relation between localized imaging features extracted from a sub-region of the full mammogram images and the underlying risk of developing breast cancer in this specific sub-region.