Program Information
Big Data 2: New and Emerging Big Data Strategies in Radiation Oncology
J Deng1*, C Mayo2*, S Mutic3*, (1) Yale New Haven Hospital, New Haven, CT, (2) University of Michigan, Ann Arbor, MI, (3) Washington University in St Louis, St Louis, MO
Presentations
8:30 AM : Predicting Cancer Risks via Multi-Parametric Correlations - J Deng, Presenting Author8:50 AM : Bringing Value from Big Data Analytics into Clinical Practice - C Mayo, Presenting Author
9:10 AM : The Roadmap Ahead for Big Data in Radiation Oncology - S Mutic, Presenting Author
TU-B-605-0 (Tuesday, August 1, 2017) 8:30 AM - 9:30 AM Room: 605
In Part 2 of this Big Data Panel we present some new and novel approaches to Big Data applications in radiation oncology. The first talk will focus on the multiple carcinogenic factors and demonstrate how some multi-parameterized machine learning models can be built to predict the cancer risk for individual patient. In the second talk, practical clinical examples will be given demonstrating how application of big data analytics can be applied to clinical practice to motivate and guide efforts in using big data for the benefits of patients. The session will conclude with a discussion on the roadmap ahead highlighting the wide range of opportunities that can impact patient care and enhance potential for research and collaboration within radiation oncology and other associated fields.
Learning Objectives:
1. To demonstrate what new knowledge Big Data can provide for clinical decision support in personalized medicine
2. To introduce new and emerging strategies that optimize our current practice for Big Data application in radiation oncology
3. To become familiar with the future programs underway to enhance Big Data resources
Handouts
- 127-35415-418554-126636.pdf (C Mayo)
- 127-35416-418554-133050.pdf (S Mutic)
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