Biography

Jaesung Heo, M.D., Ph.D., is a Visiting Assistant Professor in the Department of Radiation Oncology at UT Southwestern Medical Center and part of the department’s Division of Medical Physics & Engineering. Prior to joining UT Southwestern, Dr. Heo worked as an Assistant Professor in the Department of Radiation Oncology at Ajou University School of Medicine in Suwon, Republic of Korea, where he was also Director of the Ajou Healthcare Artificial Intelligence Research Center. He is certified by the Korean Board of Radiation Oncology.

Dr. Heo earned his medical degree from Chung Ang University College of Medicine in Seoul, Republic of Korea. Additionally, he earned his Ph.D. in medicine from Ajou University School of Medicine. Both his residency and fellowship training in radiation oncology were completed at Ajou University School of Medicine.

Dr. Heo focuses on enhancing the effectiveness of adaptive radiotherapy using artificial intelligence (AI), particularly in image-guided radiotherapy. His research involves developing sophisticated AI models for lesion detection and treatment prediction based on MRI and CBCT images, aiming to improve the precision and outcomes of radiotherapy. Additionally, Dr. Heo is pioneering efforts in predicting the expression of biomarkers through advanced image analysis. His scholarly contributions extend to the AI-based analysis of biomarkers in cancer using whole slide images and CT images. His model has demonstrated a significant correlation between predicted mutation probabilities and genomic testing results, indicating its potential utility in clinical settings. Furthermore, Dr. Heo has developed deep learning models to predict responses to multimodal therapy in cancer patients. His research also includes survival prediction models for cancer patients, employing deep learning techniques to analyze 3D CBCT images and clinical data.

Dr. Heo’s scholarly work has resulted in the publication of numerous peer-reviewed articles and has been disseminated at several national academic conferences. He is an active member of the Korean Society of Radiation Oncology and the Korean Cancer Association as well as the National Standards Expert Committee.

Research Interest

  • Artificial intelligence (AI) in adaptive therapy: Enhancing the effectiveness of adaptive radiotherapy and image-guided radiotherapy using AI
  • Medical imaging and biomarkers: Developing AI models for lesion detection, treatment prediction, and biomarker expression analysis using MRI, CBCT, and pathology images
  • Multidisciplinary treatment tools: Creating tools to assist in predicting and selecting responses to various treatments such as radiotherapy, chemotherapy, and immunotherapy
  • Treatment predictions: Predicting treatment responses and survival outcomes in cancer patients using deep learning models

Honors & Awards

  • Merck Cancer Academic Award
    Asia Pacific Cancer Conference, Korean Cancer Association (2017)
  • Best Paper Award
    Annual Meeting of Korean Association of Radiation Oncology (2015)

Professional Associations/Affiliations

  • Korean Cancer Association (2015)
  • Korean Society of Radiation Oncology (2012)
  • National Standards Expert Committee (2021)
  • Varian HyperArc Consultant (2019)