Synopsis

This course will present a high level overview of the “other” MRI methods (dT1, DSC, BOLD, ASL, MRS) that have been used for the assessment of cancer, with a focus on their utility in brain tumors. The specific emphasis will be on quantification, which is becoming increasingly necessary to detect and track changes over time with the goal of optimal response assessment.

Highlights

• Delineation of enhancing tumor burden has long relied on manual determination of contrast-agent enhancing lesions on post-contrast T1-weighted MRI. However, the degree of enhancement can be confounded by treatment effects that depend on the tumor type and treatment protocol. In addition, inter-reader variability, which relies on manual delineation of enhancing tumor, remains high. As a solution, a deltaT1 (dT1) method (1) has been developed, that relies on image intensity calibration or “standardization”(2), so that enhancing tumor burden is less dependent on treatment-related confounds and can be semi-automated thereby eliminating inter-reader variability.
• The utility of dynamic susceptibility contrast perfusion MRI (DSC-pMRI) for the evaluation of brain tumors has been demonstrated with a multitude of studies. However, reaching consensus regarding methodology and quantitation has remained a long-standing challenge that is blamed for differences in the thresholds recommended to distinguish tumor and tissue types, for example. However, consensus is being reached based on both preclinical and clinical studies showing that leakage-corrected relative cerebral blood volume (rCBV) derived from DSC-pMRI is most reliable. A study addressing multi-site concordance of DSC-MRI analysis for brain tumors, presented at this meeting (3), suggests that when a preload of contrast agent is used before collecting DSC-MRI data there is excellent agreement across sites and platforms in their ability to distinguish low-grade from high-grade brain tumor. Taking these findings into consideration, the National Brain Tumor Society (NBTS) dynamic susceptibility contrast (DSC) MRI working group is in the process of finalizing the details of a recommended consensus DSC-MRI protocol. These recommendations will be briefly outlined.
• The combination of MRI parameters, such as dT1 and rCBV can be used to generate a new imaging biomarker, fractional tumor burden (FTB) (4), which identifies the portion of enhancing lesion that is true tumor. This metric has demonstrated promise to predict outcomes in the setting of both chemo-radiation therapy (5) and bevacizumab treatment (6).
• Quantification of longitudinal changes in DSC-pMRI and diffusion parameters over time hold promise as sensitive indicators of treatment response (7-10) but are challenged by the need for improved methods of registration, and consistency across acquisition platforms.
• BOLD (blood oxygenation level dependent) methods have been used in attempt to characterize the complexity of tumor vascular physiology (11). More commonly BOLD MRI methods are used for presurgical mapping, yet have not seen widespread clinical translation due to the concerns regarding vascular uncoupling (12).
• Arterial spin labeling methods (ASL) has been explored as a replacement for contrast agent perfusion methods for the evaluation of tumor blood flow, with promising results (13). However, concern remains regarding loss of the radio frequency label given the highly heterogeneous vasculature of tumors and therefore transit times.
• Magnetic resonance spectroscopy (MRS) continues to be the primary MR method that provides information about tumor metabolism. MRS has experienced a recent resurgence due to its ability to provide a measure of IDH1 mutation (14), an important brain tumor molecular marker. However, its widespread use continues to be limited by a lack of confidence in a reliable and automatic method that both new and experienced users can depend on for the processing of MRS data.

Conclusion

Acknowledgements

References

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5. Prah MA, et al. ISMRM 2017; Honolulu, Hawaii. p 707.

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12. Para AE, et al. J Magn Reson Imaging 2017.

13. Cebeci H, et al. Eur J Radiol 2014;83(10):1914-1919.

14. Hu J, et al. Top Magn Reson Imaging 2017;26(1):27-32.