Improved brain tumor diagnostics and follow-up with novel magnetic resonance imaging methods: A single center study protocol - Jesse Lohela; Kaisa Lehtiö; Kalle Inget; Sakari S. Karhula; Susanna Piironen; Angélica Suutari; Antti Knuutinen; Miro Jänkälä; Eveliina Lammentausta; Michaela K. Bode; Juha Nikkinen; Niina Salokorpi; et al. (PLoS ONE, 2025)
This protocol outlines a prospective study aimed at enhancing the diagnosis and monitoring of brain tumors through advanced non-invasive imaging techniques. While magnetic resonance imaging (MRI) is a cornerstone of brain tumor diagnostics, it often lacks the specificity required for definitive diagnosis, which typically relies on invasive tissue sampling. To address this, the study will evaluate advanced MRI techniques—such as perfusion, diffusion, blood-oxygen-level-dependent imaging, magnetic resonance spectroscopy, and amide proton transfer-weighted imaging— that offer valuable physiological and molecular insights, beyond conventional anatomical imaging. Despite their potential, clinical adoption of these methods remains limited. MRI also plays a central role in treatment response assessment and follow-up, yet conventional anatomical sequences may not detect early physiological changes or differentiate true progression from pseudoprogression. Advanced imaging methods have shown promise in addressing these limitations, and predictive models for recurrence risk could further personalize treatment strategies. In this study, imaging will be performed using a standardized 3T MRI scanner at multiple time points: preoperatively, before radiotherapy, during treatment, and throughout follow-up. This protocol aims to establish a multiparametric imaging framework capable of capturing dynamic physiological and molecular changes in brain tumors. The primary goal is to determine whether combining advanced sequences improves diagnostic accuracy compared to conventional MRI, using histopathology as the reference. Secondary objectives include predicting treatment response, distinguishing true progression from pseudoprogression, and modeling spatial recurrence risk based on quantitative imaging biomarkers. We hypothesize that a multiparametric imaging approach will, enable earlier detection of tumor progression and support more precise, individualized treatment decisions.