Application of radiomics and artificial intelligence in lung cancer immunotherapy: a guide and hurdles from clinical trials

Abstract

Immunotherapy has shown promising results with improved progression-free survival and overall survival in lung cancer. However, novel immunotherapy could generate atypical response patterns, which is a big challenge for traditional imaging criteria. Radiomics, combined with artificial intelligence (AI), represents new quantitative methodologies that could serve as an additional imaging biomarker to predict immunotherapy benefits and assess responses to assist oncologists in decision-making in lung cancer treatment. This paper aims to review the latest advancement of AI-based radiomics applied to lung cancer patients receiving immunotherapy, focusing on the fundamentals of these approaches and commonly used techniques. We also address the hurdles in the AI and radiomic analysis pipeline to guide clinicians in approaching this new concept.