Multi‐parametric MRI (mpMRI) for treatment response assessment of radiation therapy

Abstract

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft‐tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision‐making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

Delineation of recurrent glioblastoma by whole brain spectroscopic magnetic resonance imaging

“…We also analyzed the potential value of rCBV and ADC in rGBM re-RT planning as they are often used to assist clinicians to identify rGBM and differentiate it from radiation necrosis [ 37 ]. Attempts have also been made to use these volumes to guide rGBM dose escalation in the upfront GBM setting [ 38 ].…”

A neural ordinary differential equation model for visualizing deep neural network behaviors in multi‐parametric MRI‐based glioma segmentation

“…2,4,6,7 It is important to acquire accurate delineation of glioma boundaries with potential sub-region segmentations in personalized glioma treatment regimens for improved survival time. 8 Multi-parametric MRI protocols that involve multiple anatomical and functional sequences can provide comprehensive and possible complementary information on spatial disease distribution 7,[9][10][11][12] ; therefore, multiparametric MRI has been extensively studied for glioma segmentation.…”

Microstructural parameters from DW‐MRI for tumour characterization and local recurrence prediction in particle therapy of skull‐base chordoma

“…12,21,[23][24][25][30][31] Specifically, promising computational approaches able to derive multiple and advanced tissue properties from routinelyacquired MRI images by employing dictionary of signals from multiple MRI sequences (i.e., fingerprints methods) or Monte Carlo simulations are currently being investigated and developed. [29][30][31][32][33] These studies are pushed toward an improved tumor microstructure description, which is crucial for treatment optimization and personalization, especially when dealing with SBC treated with CPT.…”

“…The role of ADC as potential prognostic biomarker able to differentiate and characterize different physio‐pathological processes has been already demonstrated in the literature, but the possibility of extracting more complex information (e.g., about cellularity) encapsulated in the ADC is one of the main challenges for non‐invasive characterization 12,21,23–25,30–31 . Specifically, promising computational approaches able to derive multiple and advanced tissue properties from routinely‐acquired MRI images by employing dictionary of signals from multiple MRI sequences (i.e., fingerprints methods) or Monte Carlo simulations are currently being investigated and developed 29–33 . These studies are pushed toward an improved tumor microstructure description, which is crucial for treatment optimization and personalization, especially when dealing with SBC treated with CPT.…”