Quantitative Imaging in Lung Cancer

Abstract

Medical images are used to guide clinicians throughout the course of lung cancer: screening, differential diagnosis of lung nodules, TNM staging, biopsy guiding, radiation treatment planning, and response assessment. Over the past decade, the management of lung cancer patients has radically improved, due to numerous breakthroughs in our understanding of lung cancer molecular characteristics, targeted and immunotherapies, and computer hardware and software. Among innovative technologies, quantitative imaging biomarkers (QIBs) have become promising tools to support clinical decision making. This indicates a paradigm shift, redefining medical images as a quantitative asset for data-driven precision medicine, rather than a qualitative method for estimating disease status. Of note, QIBs, or radiomics signatures, must not only achieve high accuracy, but should also be robust across different imaging acquisition settings, to be clinically applicable and advance patient management. Quantitative imaging (QI) in lung cancer is one of the most active research areas in medical imaging. In this chapter, the current state-of-the-art QI technologies in lung cancer are reviewed, focusing on diagnosis, prognosis, and response assessment. Then, key challenges in QIB development and validation in lung cancer are addressed. Lastly, the importance of establishing quality control tools to ensure reproducible and generalizable QIBs is discussed.