• The Future of Brain Cancer Imaging

    Author: Peter S. LaViolette

    Imaging is utilized at all stages of brain cancer treatment. Future developments are aimed at improving the techniques used for 1) surgical planning, 2) radiation treatment, 3) pseudo-progression differentiation, 4) detection of brain tumor invasion, and 5) measuring response to antiangiogenic therapy.

    This article highlights recent studies applying new techniques during each stage. The ”future” here refers to current technology that may be useful in your practice rather than bench technology that in the long term may be applied to brain cancer. This article describes how different imaging can be used to maximize diagnostic specificity in response to the tumor’s stage in therapy.

    Pre-surgical Planning
    Currently, advanced imaging such as diffusion tensor imaging (DTI) is used to map the brain’s white matter (WM) tracts. DTI has limited sensitivity in regions with divergent structure such as crossing WM fibers. Diffusion spectrum imaging (DSI)1 has the ability to measure crossing fibers and shows promise, as the scan time needed for acquisition is falling.2

    The function of eloquent cortex can be mapped using functional MRI (fMRI). This technology requires the presentation of stimulating “tasks,” which can add equipment expense and limit specificity to the function tested. Resting-state fMRI (rs-fmri) has recently received considerable attention due to its ability to measure brain network connectivity.3 The two key advantages of this technique are, first, that it requires no additional equipment for presenting tasks, as the subject simply lies there awake, and second, all the brain’s networks can be measured with one acquisition.4 There is, however, no consensus on how best to process the data, where seed-based approaches3 and data-driven approaches such as independent component analysis (ICA)5 may provide different network boundaries.

    Radiation Planning
    Currently, the combination of CT and MRI are used for radiation planning. MRI plays a large role, where the T1 enhancement and T2/FLAIR hyperintensity margins are used for defining radiation plans. Invisible tumor beyond these margins can, however, be missed. Inclusion of multi-parametric MRI such as diffusion, perfusion, and spectroscopy will likely begin to address this and may lead to improved tumor control in the future.6 Sparing structures such as the hippocampus also shows some promise in improving patient outcome.7, 8

    Pseudo-progression Window
    Following surgery, patients with high-grade gliomas are treated with the combination of radiation and temozolomide. These concomitant therapies result in increased capillary permeability, which can lead to a false diagnosis of tumor progression due to MRI contrast agents leaking through damaged vasculature.9 Contrast-enhanced imaging obtained within three months following radiation is most susceptible.10 Currently, assessing blood volume using perfusion MR imaging metrics such as relative cerebral blood volume (rCBV) appears to be the best method for distinguishing pseudo- progression from true progression.11-13 Positron emission tomography (PET) also can distinguish pseudo-progression.14 Studies applying MR spectroscopy15 and other imaging techniques are also ongoing.

    Figure 1: Machine-learning is used to determine the relationship between co-registered imaging and histology. This information can then be applied to generate maps of cellularity or nuclei count. The region highlighted shows no indication of infiltrative tumor presence on typical clinical imaging, yet is confirmed to contain tumor.

    Detecting Tumor Progression
    One of the most active fields in brain cancer imaging involves detecting invisible tumor cells infiltrating into normal brain matter. Currently, progressive disease is defined as increased contrast enhancement or an increase in the size of the T2/FLAIR hyperintensity. Ongoing studies are using techniques such as diffusion weighted imaging (DWI) and image post-processing to detect tumor cellularity.16-20 Other diffusion imaging techniques such as kurtosis21 and perfusion corrected DWI have also boosted sensitivity. Spectroscopy also has the ability to distinguish regions of active tumor near contrast enhancement,15, 22 and more recently, has been shown to be sensitive to tumor mutation.23, 24 Ongoing studies are using co-registered histology and imaging to produce machine-learning based maps of tumor cellularity.25 These maps have been shown to detect infiltrative tumor in regions that appear normal on conventional imaging (Figure 1).

    Anti-VEGF Response
    Treating recurrent glioblastomas with anti-angiogenic therapies such as bevacizumab is becoming standard of care. It has become increasingly clear, however, that standard pre- and post-contrast T1 imaging is no longer adequate.10, 26-28 Bevacizumab decreases vessel permeability,29 which diminishes MRI contrast agent extravasation.27 While tumor volume recedes, it may not reflect a tumor’s true biological response.30 It has even been suggested that anti-VEGF therapy can cause tumors to become more infiltrative.31, 32 Imaging techniques meant to overcome this are in development. Interestingly, image processing techniques that quantitatively subtract the pre-contrast map from the T1+C appear to predict patient response.33 Additionally, rCBV34 and other more advanced perfusion imaging have also predicted survival following anti-VEGF therapy.35, 36 Spectroscopy also shows some promise in predicting patient response.37 Novel technologies and new spins on old technology are improving diagnostic capabilities at each stage of brain tumor treatment. Many of the techniques highlighted here are close, if not already in use clinically. Knowledge of the treatment stage and implementation and interpretation of imaging are critical for making the most of each new sequence.


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