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| BReP-SNAP-M-55 | Detecting Pathological Complete Response in Esophageal Cancer After Neoadjuvant Therapy Based On Survival-Weighted Deep Learning: A Pilot Study S Cheng1*, W Yap2, E Tu3, (1) Taiwan AI Labs, ,,(2) Chang Gung Memorial Hospital, ,,(3) Taiwan Ai Labs, |
| BReP-SNAP-M-58 | Development and Implementation of a Knowledge Base for Automated Segment Review E Pryser*, M Schmidt, F Reynoso, W Smith, Washington University in St. Louis, St. Louis, MO |
| BReP-SNAP-M-75 | Forecasting Individual Patient Response to Radiotherapy with a Dynamic Carrying Capacity Model M Zahid*1, N Mohsin1, A Mohamed2, J Caudell1, L Harrison1, C Fuller2, E Moros1, H Enderling1, (1) H. Lee Moffitt Cancer Center, Tampa, FL, (2) UT M.D. Anderson Cancer Center, Houston, TX |
| MO-F-TRACK 1-7 | Multi Time-Point Radiomics Data for Pathological Complete Response (pCR) Prediction After Neo-Adjuvant Chemoradiation for Locally Advanced Rectal Cancer M diMayorca1*, Y Zhang2, L Shi3, X Sun4, S Jabbour5, Y Zhang6, N Yue7, K Nie8, (1) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (2) University of California Irvine, Irvine, CA, (3) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (4) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (5) Rutgers Cancer Institute Of New Jersey, New Brunswick, NJ, (6) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (7) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (8) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ |
| PO-GeP-I-134 | Imaging Biomarker Analysis for Grading Malignant Gliomas Based On a Few Conventional Magnetic Resonance Imaging Sequences T Nakamoto1*, W Takahashi1, A Haga2, S Takahashi1, S Kiryu3, K Nawa1, T Ohta1, S Ozaki1, Y Nozawa1, S Tanaka1, A Mukasa4, K Nakagawa1, (1) The University of Tokyo Hospital, Tokyo, JP, (2) Tokushima University, Tokushima, JP, (3) International University of Health and Welfare Hospital, Nasushiobara, JP, (4) Kumamoto University, Kumamoto, JP |
| PO-GeP-I-142 | Integrating Gross Tumor Volume and Margin Features to Predict Treatment Response for Locally Advanced Rectal Cancer Patients M diMayorca1*, Y Zhang2, L Shi3, X Sun4, S Jabbour5, Y Zhang6, N Yue7, K Nie8, (1) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (2) University of California Irvine, Irvine, CA, (3) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (4) Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, CN, (5) Rutgers Cancer Institute Of New Jersey, New Brunswick, NJ, (6) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (7) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, (8) Rutgers Cancer Institute of New Jersey, New Brunswick, NJ |
| PO-GeP-P-41 | Evaluation of Patient Specific Quality Assurance Using Receiver Operator Characteristic Analysis M Adili*, A Alrushoud, Prince Sultan Medical Military City |
| PO-GeP-T-475 | Gross Patient Error Detection Via Cine Transmission Dosimetry N Dang1*, V Leandro Alves1, M Ahmed2, J Siebers1, (1) University of Virginia, Charlottesville, VA, (2) Vanderbilt University Medical Center, Nashville, TN |
| PO-GeP-T-676 | Quantified VMAT Plan Complexity in Relation to Measurement-Based Quality Assurance Results M Nguyen*, G Chan, Juravinski Cancer Center, Hamilton, ON, CA |
| 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 |
| TH-AB-TRACK 4-13 | Attention Guided Lymph Node Malignancy Prediction in Head and Neck Cancer L Chen1*, M Dohopolski1, Z Zhou2, K Wang1, R Wang1, D Sher1, J Wang1, (1) UT Southwestern Medical Center, Dallas, TX, (2) University Of Central Missouri, Warrensburg, MO |
| TU-CD-TRACK 2-8 | Normalizing Delta Radiomics for Early Prediction of Treatment Response During Chemoradiation Therapy of Pancreatic Cancer H Nasief*, W Hall, B Erickson, X Li, Medical College of Wisconsin, Milwaukee, WI |