Multi-Modality 3D Quantitative Imaging in Cancer Care: Clinical Value and Future Perspectives
Originally Aired: October 3, 2017
CME/CE is not available for this on-demand webcast. This webinar is supported by an educational grant
from Philips Healthcare
Statement of Purpose
Liver cancer is the second most common cause of cancer-related death worldwide and most cases are diagnosed at intermediate to advanced stages of the disease, making most patients no longer amenable to surgical therapies. Minimally invasive, loco-regional image guided therapies, such as chemoembolization, have become the mainstay therapy for such patients. These image-guided interventions also gave birth to the new field of interventional oncology, a subspecialty of interventional radiology which is increasingly considered as the new and fourth pillar of cancer care (next to medical, surgical and radiation oncology). The explosive growth of such therapies requires new and more efficient intra- and post-procedural imaging solutions. This webinar will focus on the role of image analysis and artificial intelligence for image-guided, minimally invasive cancer therapies and introduce the audience to the mechanisms of action, principles of image analysis and the growing role of machine learning for the therapeutic algorithm and decision making in interventional oncology.
Upon completion of this activity, particpants will be able to:
- Summarize the principles and applications of image-guided minimally invasive tumor therapy.
- Describe the role of cancer imaging and multi-modality tumor tracking for local therapies of liver cancer.
- Review novel software-assisted 3-D quantitative tools to evaluate surrogate endpoints of therapeutic efficacy.
- Explain the growing role of machine learning in the automation and standardization of image analysis.