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

2022/11/10 by Chi-en Amy Tai, Tai, Chi-en Amy, Hayden Gunraj +8
Medicine · #Advanced Neuroimaging Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #MRI in cancer diagnosis #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2211.05308

openalex publication_date 2022/11/10 · openalex created_date 2022/11/16 · openalex updated_date 2026/07/28

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. As such, there has been immense research and progress on improving screening and clinical support for breast cancer. In this paper, we investigate enhancing clinical support for breast cancer with deep learning models using a newly introduced magnetic resonance imaging (MRI) modality called synthetic correlated diffusion imaging (CDI^s). More specifically, we leverage a volumetric convolutional neural network to learn volumetric deep radiomic features from a pre-treatment cohort and construct a predictor based on the learnt features for grade and post-treatment response prediction. As the first study to learn CDI^s-centric radiomic sequences within a deep learning perspective for clinical decision support, we evaluated the proposed approach using the ACRIN-6698 study against those learnt using gold-standard imaging modalities. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction and thus may be a useful tool to aid oncologists in improving recommendation of treatment of patients. Subsequently, the approach to leverage volumetric deep radiomic features for breast cancer can be further extended to other applications of CDI^s in the cancer domain to further improve clinical support.

Related

• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging
• Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction
• A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data
• Deep Learning Applied to Diffusion-weighted Imaging for Differentiating Malignant from Benign Breast Tumors without Lesion Segmentation