Special Session

Presidential Symposium II - EL06 - Artificial Intelligence Will Drive Who We Treat for Cure With Metastatic Disease and How We Treat Them

10:15 AM - 11:45 AM
Location: Room W183

Session Type: Special Session
0.00 AMA PRA Category 1 Credits™

Radiation therapy is a potentially powerful tool to aid in the cure of metastatic cancer. The challenges of determining which patients will benefit and how best to treat them represents an increased complexity of both data and predictive models beyond the current state of the art radiation therapy paradigms for localized disease control. In this session, we will explore the use of artificial intelligence (AI) to aid in resolving the complex information down to clinically actionable activities that seek to cure metastatic disease. We will debate the ability of AI to identify the patients that will benefit from radiation; and how AI can help select the appropriate care plans for a given patient to include localized and systemic therapies; and also, how AI can help design the ideal radiation therapy treatment, including which metastatic sites to target and when. The debates will be focused on the data and infrastructure necessary to utilize AI, the current understanding of patient outcomes, the required prognostic information about the patients, and the potential for the success of AI to support the charter towards curing metastatic disease.


Raymond Mak, MD

Dana-Farber Cancer Institute

Brigham and Women's Hospital/Dana-Farber: Associate professor: Employee

AstraZeneca: Advisory Board: Relationship ended 12/31/2018;
NewRT: Honoraria: Travel Expenses: Relationship ended 03/31/2018

Dr. Mak is an Associate Professor of Radiation Oncology at Harvard Medical School, and he has clinical expertise in the treatment of thoracic cancers such as lung cancer and mesothelioma, and in advanced radiation techniques including stereotactic body radiation therapy and magnetic resonance imaging (MRI)-guided radiation therapy. Dr. Mak has several leadership roles in the Department of Radiation Oncology at Dand-Farber/Brigham and Women's Cancer Center including serving as Radiation Oncology Disease Site Leader for the Thoracic Oncology Program, Director of Stereotactic Body Radiation Therapy, Director of Patient Safety and Quality, and Director of Clinical Innovation.

In these roles, Dr. Mak has led an effort to design and implement a MRI-guided radiation therapy program at BWH/DFCI, including a MRI-guided linear accelerator and a MRI simulator, which are innovative technologies that will revolutionize the way radiation therapy is delivered in New England.

His translational research has improved the understanding of how genetic factors in lung tumors influence the way that patients respond to radiation and his pioneering work in applying artificial intelligence techniques to medical imaging of lung cancer is improving outcome prediction and the of quality and efficiency of radiation therapy planning—work that has received funding from prestigious foundations such as the Radiological Society of North America and the National Institutes of Health.

Dr. Mak’s efforts in the clinic and research activities share a common theme of developing innovative, cutting edge tools to improve radiation therapy for cancer patients by combining translational research work on genetic and imaging biomarkers paired with leadership in technology development and implementation in the clinic. Dr. Mak’s desire to ensure that his patients have the best possible outcomes informs his unique approach to treatment and research.


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Todd McNutt, PhD

Johns Hopkins University

Johns Hopkins University : Associate Professor : Employee

Canon: Research Grants;
Radiation Oncology Institute : Research Grants

Accuray-Tomotherapy: Patent/License Fees/Copyright; Oncospace Inc.: Stock; Sun Nuclear: Patent/License Fees/Copyright

Oncospace Inc. : Chairman of the Board : Chairman of the board


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Julian Hong, MD, MS


Duke University Medical Center

Julian Hong, MD, MS is an Assistant Professor in the Department of Radiation Oncology and the Bakar Computational Health Sciences Institute at UCSF. His research focuses on developing and deploying actionable analytics in oncology, including machine learning and artificial intelligence for prediction and decision making, data extraction and natural language processing, and imaging analytics.


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Presidential Symposium II - EL06 - Artificial Intelligence Will Drive Who We Treat for Cure With Metastatic Disease and How We Treat Them

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