MRI In Breast Cancer: From Bench to Bedside & Beyond
Member-Initiated Symposium

ORGANIZERS: Elizabeth Morris

Wednesday, 15 May 2019

Session Number: MIS-14

Overview
New MRI techniques provide insight into the underlying oncogenic processes of breast cancer. The ability of breast MRI to provide diagnostic, prognostic and predictive information is thus continuously improving. We discuss new frontiers in breast cancer imaging, progressing from bench to bedside and beyond, focusing on the development and integration of advanced techniques to enable precision medicine. While the symposium focuses on breast cancer, the discussed concepts are applicable to all fields of oncological imaging. Preclinical MRI is essential to understand the effect of oncogenesis on the MRI tissue signature and identify novel imaging biomarkers that can be successfully translated from bench to bedside. This adds onto available MRI features and clinical information already allowing sub-classification of cancer into indolent and more aggressive types. Advanced DWI further expands the possibilities to assess breast cancer and its microenvironment. PET/MR may play a pivotal role in the understanding of tumor biology and identification of targets for treatment. However, the available information may be too complex for the human mind to comprehend.

Artificial Intelligence enables capturing the relevant data, and the emerging field of radiomics/genomics integrates multiparametric imaging phenotypes with the heterogenous mutational landscape of breast cancer, identifying spatially resolved predictive and prognostic imaging biomarkers in order to improve patient outcomes.

Target Audience
Radiologists/scientists interested in experimental techniques, practical application of oncological (PET/)MRI and AI in breast cancer.

Educational Objectives
As a result of attending this course, participants should be able to:
- Explain metabolic MRS/I of breast cancer and its challenges, translation and future clinical applications;
- Identify the available predictive and prognostic information in breast MRI and hybrid PET/MRI;
- Recognize the clinical applications of diffusion-weighted imaging in breast cancer and the potential of advanced modeling approaches under investigation; and
- Describe the basic principles, current methods, challenges and limitations of machine learning, deep learning and AI and its applications in breast cancer.