报告摘要：

Through technological advances and clinical research over the past few decades, precision radiation therapy practice with optimal biological outcome substantially increases the treatment complexity in both space and time such as the dose painting technique and orchestrated adaptive strategy. Simultaneously, the cancer patients are continuously increasing and that resulted in a global shortage of radiation oncologists and medical physicists. Therefore, the automation and artificial intelligence applications are urgently needed. This session will cover recent advances and applications of AI in radiotherapy treatment planning, image guidance, quality assurance and image-based tumor grading and prognostic prediction. The well-developed AI assisted clinical procedures can be implemented in a streamlined and automated manner with improved treatment accuracy, efficiency and comprehensiveness.

报告人简介：

Dr. Bin Han is a Clinical Associate Professor at the Department of Radiation Oncology, Stanford University.  He is an alumnus who finished his undergraduate and master's study at the Engineering Physics Department, Tsinghua University in 2003 and 2006.  He received his Ph.D. training in Rensselaer Polytechnic Institute and Massachusetts General Hospital from 2007 to 2011. Dr. Han finished the CAMPEP-credited Therapeutic Medical Physics at Stanford in 2013 and became the American Board of Radiology certified Medical Physicist. He joined the faculty at the Department of Radiation Oncology, Stanford University in 2013 after the residency and was promoted to the associate level in 2019.  Dr. Han is responsible for providing high quality clinical medical physics services, developing innovative radiation therapy treatment devices, and new treatment protocols to improve patient care. He leads several research projects including the development of an advanced EPID-based dosimetric solution, an ultrasound system for image guided prostate cancer treatment, depth sensing and 3D-printing techniques for total body irradiation, and predicting treatment effectiveness and cancer recurrence with deep learning. He is also mentoring graduate students, postdocs, and residents at Stanford for their research guidance and clinical education.

“论坛”主请人联系方式：

高河伟  hwgao@tsinghua.edu.cn