New computational imaging platform can improve patient-specific characterization of breast cancer
Problem
Clinicians rely on imaging to direct the path of treatment for breast cancer. The current standards for vascular imaging, MRI and CT, can miss valuable information. Fortunately, there has been a wave of technical progress with digital health technologies. Digital health technologies can help doctors to make safer and more cost-efficient clinical care decisions. Recently, the FDA has defined a clearance pathway for this type of technology.1 Predictive modeling software can glean additional layers of information from MRI and CT images. This approach holds promise for enabling earlier and more accurate diagnosis. However, existing imaging tools are missing a key feature—they are not able to analyze the dynamic fluid and pressure fields at the interface between tumor and healthy tissue.
Solution
A team led by Thomas Yankeelov at The University of Texas at Austin and Gregory Karczmar at The University of Chicago are developing a new MRI acquisition and post-processing technique that allows for mapping of both flow and pressure fields of breast tumors. No other technique can provide this data using non-invasive MRI data. This FlowSim analysis software creates pressure and flow maps that can help physicians and radiologists more accurately diagnose and stage cancer. The resulting patient-specific models can help physicians and radiologists plan and monitor treatment plans. The FlowSim acquisition protocol and modeling software requires minimal changes to existing clinical workflows. This technology can greatly improve care for breast cancer patients by helping doctors identify the best treatment plans given the flow and pressure conditions around the tumor.2
References
1. Digital Health Criteria, FDA, 03/23/2018 https://www.fda.gov/medical-devices/digital-health/digital-health-criteria
2. Wu, C., et al.. Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics. IEEE Trans Med Imaging. 2020;39 (9):2760-2771. DOI: 10.1109/TMI.2020.2975375.
