연구진 소개

Medical physics is an interdisciplinary field that applies the principles and techniques of physics to medicine for the diagnosis, treatment, and prevention of disease. it plays a critical role in medical imaging, radiation therapy, and nuclear medicine by improving image quality, evaluating and optimizing radiation dose, and enhancing treatment accuracy. The field aims to improve patient safety while increasing the precision and efficiency of healthcare. Furthermore, by integrating emerging technologies such as artificial intelligence and data science, medical physics contributes to the development of next generation medical technologies.

Our research focuses on understanding the mechanisms of brain tumor development and improving radiotherapy through translational cancer research. We investigate how adult neural stem cells acquire oncogenic mutations, migrate from their niches, and transform into malignant brain tumors, as well as how the tumor microenvironment drives tumor progression and therapeutic resistance. We also study the molecular and genetic mechanisms underlying radiation resistance to develop strategies that enhance the efficacy of radiotherapy. In parallel, we investigate radiation-induced toxicity in the normal brain, including cognitive impairment and other long-term complications, using genome-wide screening, organoid models, brain-on-chip platforms, animal models, and patient-derived tissues. By integrating laboratory research with clinical studies, we aim to identify novel therapeutic targets and biomarkers that improve treatment outcomes while minimizing adverse effects. Our work is conducted in close collaboration with multidisciplinary brain tumor specialists and is supported by clinical cohorts, tissue biobanking, and investigator-initiated clinical trials to accelerate the translation of scientific discoveries into patient care.

Professor Bum-Sup Jang is a Clinical Associate Professor in the Department of Radiation Oncology at Seoul National University Hospital. With a unique interdisciplinary background that combines computer engineering and medicine, he leads precision medicine research that integrates advanced technologies into the field of radiation therapy. In medical artificial intelligence and big data research, he utilizes machine learning, deep learning, and large language models (LLMs) to develop clinically applicable decision-support tools, including systems for differentiating pseudoprogression from true progression in glioblastoma, predicting treatment response in rectal cancer, and natural language-based automated target volume generation systems for radiation therapy. In breast and prostate cancer radiation therapy optimization, he leads multi-institutional studies through the Korean Radiation Oncology Group (KROG), conducting evidence-based clinical research that encompasses Bayesian network-based risk-benefit assessment models for treatment decision-making, radiomics-based lymphedema prediction, and analysis of complications following breast reconstruction surgery. In radiogenomics and transcriptome analysis, he leverages large-scale datasets such as TCGA and the UK Biobank to identify radiosensitivity gene signatures and investigates the relationship between single-cell RNA sequencing and immune checkpoint inhibitor responsiveness. In the field of microbiome and radiation therapy, he has pioneered research both domestically and internationally, elucidating how gut microbial composition influences treatment response and toxicity in rectal and prostate cancer radiotherapy, while also developing probiotic-based strategies to mitigate radiation-induced side effects. Through this multi-omics-based convergence research, he establishes personalized radiation therapy strategies tailored to each patient's biological characteristics, contributing to the realization of precision