Keywords: Quantitative Imaging, Machine Learning/Artificial IntelligenceA significant challenge in the management of metastatic brain tumors following radiation therapy is distinguishing radiation necrosis from tumor recurrence. Differential diagnosis is difficult on routine MRI and patients are subject to invasive procedures to confirm the absence of disease. We explored the feasibility of deformation features from the normal parenchyma to identify disease recurrence versus radiation effects. Our results suggest that measurements of the subtle tissue deformations in the normal-appearing brain regions may elucidate differences in the tissue microarchitecture of radionecrosis and tumor recurrence and may serve as surrogate markers to non-invasively characterize treatment response in brain metastases.
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