QP 26 - The Right Patient, the Right Treatment: Machine Learning for Stratification and Prediction

Presentations

• 05:15pm - 05:20pm ET

  Introductions

  Speaker: Jun Deng, PhD, FASTRO - Yale University, New Haven
• 05:20pm - 05:25pm ET

  Dynamic Hematologic Trajectories and Machine Learning to Predict Survival and Radiotherapy Benefit In Unresectable Esophageal Squamous Cell Carcinoma Receiving Immunotherapy: A Multicenter, Real-World Study

  Speaker: Xiaohan Zhao, MD, PhD - The Fourth Hospital of Hebei Medical University, Shijiazhuang
• 05:25pm - 05:30pm ET

  Community-Based Health System Validation of the SHIELD-RT Machine Learning Model to Prevent Acute Care Events During Radiotherapy

  Speaker: Marianna Elia, MS, BS - University of California San Francisco, San Francisco
• 05:30pm - 05:35pm ET

  Novel Counterfactual Machine Learning Reveals Treatment-Specific Radiosensitivity in Gastric Cancer

  Speaker: Julia Balmaceda, MD - WashU Medicine, Saint Louis
• 05:35pm - 05:40pm ET

  Comparing Face Photograph-Based and Blood-Based Biological Age Biomarkers for Survival Prediction in Patients Receiving Radiation Therapy

  Speaker: Fridolin Haugg, MS - Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston
• 05:40pm - 05:45pm ET

  NIMOD: A Noninvasive Multimodality Oncology Defacer with Near-Chance Reidentification Risk and Preserved Data Utility

  Speaker: Du Wang, PhD, MS - Rutgers Cancer Institute of New Jersey, New Brunswick
• 05:45pm - 05:50pm ET

  Deep Learning-Enabled IMPT Treatment Planning Using a Current Clinical Setup: Principles and Validation Toward Online Adaptive Proton Therapy

  Speaker: Minglei Kang, PhD - University of Wisconsin, Madison
• 05:50pm - 06:10pm ET

  Questions & Answers

  Speaker: Soumyajit Roy, MBBS, MSc. - University Hospitals Seidman Cancer Center, Cleveland
• 06:10pm - 06:15pm ET

  Closing

  Speaker: Jun Deng, PhD, FASTRO - Yale University, New Haven