Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Optimized Synthetic Correlated Diffusion Imaging

Abstract

Breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25% of all new female cancer cases. The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. However, there are different levels of severity of breast cancer requiring different treatment strategies, and hence, grading breast cancer and estimating treatment prognosis have become vital clinical tasks in breast cancer.

Recently, a new form of magnetic resonance imaging (MRI) called synthetic correlated diffusion imaging (CDIs) was introduced to address the physical hardware limitations and showed considerable promise for clinical decision support for cancers such as prostate cancer when compared to current gold-standard MRI techniques. However, the efficacy for CDIs for other forms of cancers such as breast cancer has not been as well-explored.

This thesis explores and designs novel deep learning architectures for enhancing two breast cancer clinical task performance (pathologic complete response prediction and Scarff-Bloom-Richardson grade classification) with optimized CDIs. A volumetric convolutional neural network is leveraged to learn volumetric deep radiomic features from a pre-treatment cohort, constructing a predictor based on the learned features for grade and post-treatment response prediction. The optimization of parameters for computing CDIs for breast cancer is also conducted through improving tumour delineation.

The proposed approach was evaluated using the ACRIN-6698 study and compared against current gold-standard MRI modalities. For grade prediction, using optimized CDIs achieved a leave-one-out cross-validation accuracy of 95.79%, which is over 16% above the next best gold-standard MRI modality and over 6% above using the unoptimized CDIs. Additionally, using optimized CDIs for post-treatment response prediction resulted in a leave-one-out cross-validation accuracy of 93.28%, which is over 8.5% above the next best gold-standard MRI modality and over 5.5% above using the unoptimized CDIs.

The proposed approach demonstrates how using optimized CDIs can be used to enhance the performance of breast cancer clinical tasks, indicating its potential as a valuable tool for oncologists to enhance patient treatment within the breast cancer domain and beyond.