Towards quantitative MRI Driving online adaptive MRgRT for lung tumors

Introduction: Successful delivery of lung cancer radiotherapy is hindered by respiratory motion, low soft-tissue contrast and anatomical variabilities, often compromising precision. Magnetic Resonance Image-guided Radiotherapy (MRgRT) has emerged as a promising approach, particularly with hybrid MR-Linac systems that offer superior soft-tissue visualization and enable online adaptive radiotherapy (online MRgART). Purpose: This review synthesizes current evidence for online MRgART in lung cancer and examines emerging roles for quantitative MRI (qMRI). A PubMed search covering the period from January 2020 to September 2025 identified 19 studies, 3 of which focused specifically on quantitative imaging. Main Findings: Online MRgART consistently demonstrated workflow feasibility, frequent online adaptation, improved target coverage while respecting Organs-At-Risk (OARs) constraints and encouraging Local Control (LC) with low high-grade toxicity. qMRI on MR-Linacs, most commonly Diffusion-Weighted Imaging (DWI) and cine-MRI-derived ventilation/perfusion mapping, showed feasibility and early signals for treatment adaptation, toxicity prediction and response assessment. Principal Conclusions: qMRI studies integrated in online MRgART for lungs are, at present, extremely limited; nevertheless, establishing a clear snapshot of the current state-of-the-art is essential, as this topic is expected to become highly prevalent and of particular interest in the near future. To our knowledge, this is the first review centered on online MRgART for lung tumors, with a dedicated subsection summarizing the nascent evidence on qMRI. Looking ahead, integrating AI-driven motion compensation, auto-segmentation and adaptive replanning with qMRI-enabled biomarkers could standardize workflows and accelerate truly personalized online MRgART. Prospective multi-center studies are needed to validate biomarkers and demonstrate clinical benefit.