DISCLAIMER:
Entry of taxonomy/keywords during proffered abstract submission was optional.
Not all abstracts will appear in search results.
Taxonomy: IM- Radiation dose and risk: Models
| PO-GeP-M-416 | Using Raman Spectroscopy and Machine Learning to Predict and Monitor Cellular Radiation Responses X Deng*, K Milligan, R Ali-Adeeb, P Shreeves, S Van Nest, J Andrews, A Brolo, J Lum, A Jirasek, University of British Columbia, Kelowna, BC, CA, University of Victoria, Victoria, BC, CA, Deeley Research Centre, BC Cancer, Victoria, BC, CA, Weill Cornell Medicine, New York, NY, USA |
| PO-GeP-T-800 | Towards An Image-Informed Mathematical Model of Response to Fractionated Radiation Therapy D Hormuth,II1,5*, A Jarrett1,5, T Yankeelov1-5, (1) Oden Institute for Computational Engineering and Sciences, Departments of (2) Biomedical Engineering, (3) Diagnostic Medicine, and (4) Oncology, (5) Livestrong Cancer Institutes . The University of Texas at Austin, Austin, TX USA |
| PO-GeP-T-831 | Using Machine Learning Techniques to Determine Dose Thresholds Predictive of Grade >= 2 Acute Rectal Toxicity in Prostate Cancer Patients Treated with Radiation Therapy J Li1, S Vora2, S Schild2, W Wong2, M Fatyga2,W Liu2, J Hu1*, (1) Arizona State University, (2) Mayo Clinic Arizona |
| WE-CD-TRACK 2-0 | Advances of Radiomics and Genomics in Cancer Management M Giger1*, J Deasy2*, I Tai3*, F Yin4*, (1) University of Chicago, Chicago, IL, (2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) BCCancer Agency At Vancouver, Vancouver, BC, CA, (4) Duke University Medical Center, Chapel Hill, NC |