일반의과대학 특별세미나 개최 안내

안녕하세요, 아래 의과대학 특별세미나를 안내드리오니 관심있는 학생 및 연구원분들의 많은 참여 바랍니다. 

- 초 청: 이 주 상 교수

[초록]

The future of digital pathology and radiomics in immuno-oncology

The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy.

Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) and digital pathology that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological and pathology imaging data available for training radiomics and digital pahtology models. And because radiomic and pathology features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes.

In this lecture, we discuss current applications of digital pathology and radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.