Entry of taxonomy/keywords during proffered abstract submission was optional.
Not all abstracts will appear in search results.
Taxonomy: TH- Dataset Analysis/Biomathematics: Machine learning techniques
|MO-C930-IePD-F5-2||Prediction of Radiation Induced Lymphedema for Head & Neck Cancer Patients Using Ensemble Feature Selection and Machine Learning|
P Teo1, K Rogacki2, M Gopalakrishnan3*, B Mittal4, I Das5, M Abazeed6, M Gentile7, (1) Department of Radiation Oncology, Northwestern Memorial Hospital, Chicago, IL, (2) Department Of Radiation Oncology, Northwestern University, (3) Department of Radiation Oncology, Northwestern Memorial Hospital, Chicago, IL, (4) Department of Radiation Oncology, Northwestern Memorial Hospital, Chicago, IL, (5) Northwestern University Feinberg School of Medicine, Chicago, IL, (6) Department Of Radiation Oncology, Northwestern University, (7) Department Of Radiation Oncology, University Of Pennsylvania.
|MO-FG-BRB-6||Explainable Machine Learning for Predicting Overall Survival of Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Photon and Proton Radiotherapy|
L Duan1*, S Lee1, R Caruana2, T Kegelman1, S Feigenberg1, Y Xiao1 (1) Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA (2) Microsoft Research, Redmond, WA
|PO-GePV-M-38||Gamma Passing Rate Predictions Based On Automatic Feature Extraction of Modulation Maps and Monitor Unit Profiles: A Machine Learning Approach for Virtual Specific-Plan Verification|
P Quintero1,2*, D Benoit1, Y Cheng1, C Moore2, A Beavis2, (1) University Of Hull, UK,(2) Queens Centre for Oncology & Haematology, Cottingham, UK
|PO-GePV-M-52||The Dataset Heterogeneity Matters: A Machine Learning Study of Dataset Conformation Effects On Model Performance for Dose Deliverability Prediction|
P Quintero1,2*, D Benoit1, Y Cheng1, C Moore2, A Beavis2, (1) University Of Hull, ,UK, (2) Queens Centre for Oncology & Haematology, Cottingham, ,UK
|PO-GePV-M-72||Progression-Free Survival Analysis for Head and Neck Cancer with Multi-Modality Radiomics Using PET/CT Images|
T Alfonzetti*, R Sheu, J Junn, R Bakst, Y Yuan, Mount Sinai Medical Center, New York, NY
|PO-GePV-T-111||Improvements On Acute Gastrointestinal Toxicity Modeling by Using Deep-Learning Auto-Segmentation|
R Salazar*, J Duryea, A Leone, S Nair, R Mumme, H Baroudi, T Netherton, E Holliday, T Whitaker, K Hoffman, L Court, J Niedzielski, UT MD Anderson Cancer Center, Houston, TX
|TU-D1030-IePD-F1-1||Automated Patient Positioning Error Detection with Orthogonal Setup DRR and Treatment KV Radiograph Image Pairs|
J Charters*, R Petragallo, D Luximon, J Neylon, D Low, J Lamb, University of California, Los Angeles, Los Angeles, CA