Litcius/Paper detail

Economic Evaluations of Magnetic Resonance Image-Guided Radiotherapy (MRIgRT): A Systematic Review

Alessandra Castelluccia, Pierpaolo Mincarone, Maria Rosaria Tumolo, Saverio Sabina, Riccardo Colella, Antonella Bodini, Francesco Tramacere, Maurizio Portaluri, Carlo Giacomo Leo

2022International Journal of Environmental Research and Public Health11 citationsDOIOpen Access PDF

Abstract

OBJECTIVES: This review systematically summarizes the evidence on the economic impact of magnetic resonance image-guided RT (MRIgRT). METHODS: We systematically searched INAHTA, MEDLINE, and Scopus up to March 2022 to retrieve health economic studies. Relevant data were extracted on study type, model inputs, modeling methods and economic results. RESULTS: Five studies were included. Two studies performed a full economic assessment to compare the cost-effectiveness of MRIgRT with other forms of image-guided radiation therapy. One study performed a cost minimization analysis and two studies performed an activity-based costing, all comparing MRIgRT with X-ray computed tomography image-guided radiation therapy (CTIgRT). Prostate cancer was the target condition in four studies and hepatocellular carcinoma in one. Considering the studies with a full economic assessment, MR-guided stereotactic body radiation therapy was found to be cost effective with respect to CTIgRT or conventional or moderate hypofractionated RT, even with a low reduction in toxicity. Conversely, a greater reduction in toxicity is required to compete with extreme hypofractionated RT without MR guidance. CONCLUSIONS: This review highlights the great potential of MRIgRT but also the need for further evidence, especially for late toxicity, whose reduction is expected to be the real added value of this technology.

Topics & Concepts

MedicineMagnetic resonance imagingRadiation therapyEconomic evaluationScopusMedical physicsMEDLINERadiologyPathologyPolitical scienceLawAdvanced Radiotherapy TechniquesProstate Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical Imaging