Sponsored Content | Webinar | Advanced Visualization| August 29, 2017

WEBINAR: Multi-Modality 3-D Quantitative Imaging in Cancer Care: Clinical Value and Future Perspectives

This webinar is supported by an educational grant from Philips Healthcare

Liver cancer advanced imaging. Webinar will cover new advances for more precise targeting of Liver Cancer.

The webinar "Multi-Modality 3D Quantitative Imaging in Cancer Care: Clinical Value and Future Perspectives" focuses on the role of image analysis and artificial intelligence for image-guided, minimally invasive cancer therapies. It introduces the audience to the mechanisms and principles of image analysis, outlines the growing role of machine learning for the therapeutic algorithm and the decision making processes in interventional oncology.

The webinar took place Oct. 3, 2017. Watch the recorded archive version of this webinar by registering.

Register to watch the archive version of this webinar

 

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.

 

Learning Objectives
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.

 

Intended Audience:
This activity is intended for oncologists, radiologist, nurse practitioners, biomedical engineers, and other clinicians interested in the management of liver cancer.

 

Presenter

Julius Chapiro, M.D., Ph.D.Julius Chapiro, M.D., Ph.D.
Research Scientist and Resident Physician
Yale School of Medicine

Dr. Chapiro is a research scientist at the Department of Radiology and Biomedical Imaging, Yale University School of Medicine. After graduating from the University of Leipzig and upon completion of his research thesis at the Justus-Liebig University in Giessen, he served as a postdoctoral fellow in interventional oncology at The Johns Hopkins Hospital and then as radiology resident at the Department of Radiology, Charité University Hospital in Berlin.

Dr. Chapiro works on the development of novel imaging biomarkers, tumor response criteria, staging systems and molecular imaging techniques for image-guided liver cancer therapies. His translational research portfolio includes the development of novel embolic agents as well as the application of artificial intelligence and machine learning solutions for the assessment of liver cancer. His basic research interest mainly focuses on tumor metabolism and immuno-oncology. With over 50 peer-reviewed publications, book chapters and news articles, Dr. Chapiro advocates the element of personalized medicine in interventional oncology, which is the most rapidly growing field in interventional radiology practice. Creating innovative and clinically practicable solutions and translating them from concept to practice has been his central mission for the past five years.  He is the co-director of the Yale Radiology Research laboratory.

 

Register to watch the archive version of this webinar

Related Content

This data represents wave 2 of a QuickPoLL survey conducted in partnership with an imagePRO panel created by The MarkeTech Group (TMTG), regarding the effects of COVID-19 on their business

Getty Images

Feature | Coronavirus (COVID-19) | July 01, 2020 | By Melinda Taschetta-Millane
Imaging Artificial Intelligence (AI) provider Qure.ai announced its first US FDA 510(k) clearance for its head CT scan product qER. The US Food and Drug Administration's decision covers four critical abnormalities identified by Qure.ai's emergency room product.
News | Artificial Intelligence | June 30, 2020
June 30, 2020 — Imaging Artificial Intelligence (AI) provider Qure.ai announced its first US FDA 510(k) clearance for
In new QuickPoLL survey on imaging during the pandemic, responses were tallied from around 170 radiology administrators and business managers, who are part of an imagePRO panel created by The MarkeTech Group (TMTG), regarding the effects of COVID-19 on their business. TMTG is a research firm specializing in the medical device, healthcare and pharmaceutical industries.
Feature | Coronavirus (COVID-19) | June 30, 2020 | By Melinda Taschetta-Millane
Thoracic findings in a 15-year-old girl with Multisystem Inflammatory Syndrome in Children (MIS-C). (a) Chest radiograph on admission shows mild perihilar bronchial wall cuffing. (b) Chest radiograph on the third day of admission demonstrates extensive airspace opacification with a mid and lower zone predominance. (c, d) Contrast-enhanced axial CT chest of the thorax at day 3 shows areas of ground-glass opacification (GGO) and dense airspace consolidation with air bronchograms. (c) This conformed to a mosai

Thoracic findings in a 15-year-old girl with Multisystem Inflammatory Syndrome in Children (MIS-C). (a) Chest radiograph on admission shows mild perihilar bronchial wall cuffing. (b) Chest radiograph on the third day of admission demonstrates extensive airspace opacification with a mid and lower zone predominance. (c, d) Contrast-enhanced axial CT chest of the thorax at day 3 shows areas of ground-glass opacification (GGO) and dense airspace consolidation with air bronchograms. (c) This conformed to a mosaic pattern with a bronchocentric distribution to the GGO (white arrow, d) involving both central and peripheral lung parenchyma with pleural effusions (black small arrow, d). image courtesy of Radiological Society of North America

News | Coronavirus (COVID-19) | June 26, 2020
June 26, 2020 — In recent weeks, a multisystem hyperinflammatory condition has emerged in children in association wit
The American College of Radiology (ACR) Center for Research and Innovation (CRI) is pleased to announce the development of the COVID-19 Imaging Research Registry (CIRR), an effort by the ACR CRI and the ACR Data Science Institute in collaboration with the ACR and the Society of Thoracic Radiology (STR). Sharyn Katz, M.D., director of research for thoracic radiology at the University of Pennsylvania, chairs the effort’s multiple-disciplinary steering committee, which includes representation from across the i

Getty Images

News | Coronavirus (COVID-19) | June 25, 2020
June 25, 2020 — The American College of Radiology (ACR) Center for R
he FDA launched the first “FDA Insight” podcast, featuring FDA Commissioner Stephen Hahn, M.D., and FDA Deputy Commissioner for Medical and Scientific Affairs Anand Shah, M.D., discussing FDA's COVID-19 efforts, including the drug development process for a COVID-19 treatment.

Getty Images

News | Coronavirus (COVID-19) | June 25, 2020
June 25, 2020 — The FDA launched the first “...
Researchers from five infectious disease hospitals across four districts in Guangzhou, China found that the less pulmonary consolidation on chest CT, the greater the possibility of negative initial reverse transcription–polymerase chain reaction (RT-PCR) results for 21 patients (nine men, 12 women; age range, 26–90 years)

Comparison of CT features between groups with negative and positive initial RT-PCR results.
aThe difference was statistically significant in comparison of the two groups (p < 0.05).

News | Coronavirus (COVID-19) | June 18, 2020
June 18, 2020 — 
The thickness of the cartilage covering the end of each bone is colour-coded, with red areas denoting thinner cartilage and green-blue areas denoting thicker cartilage. The technique helps locate where arthritis is affecting the joint over time.

The thickness of the cartilage covering the end of each bone is colour-coded, with red areas denoting thinner cartilage and green-blue areas denoting thicker cartilage. The technique helps locate where arthritis is affecting the joint over time. Image courtesy of the University of Cambridge

News | Magnetic Resonance Imaging (MRI) | June 11, 2020
June 11, 2020 — An algorithm that analyzes...
Siemens Partnership will make better health easier throughout Pennsylvania and in all communities that Geisinger serves

Getty Images

News | Radiology Business | June 08, 2020
June 8, 2020 — Siemens Healthineers and Geisinger have estab