The Future of Brain Cancer Imaging
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.
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.
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
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).
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.
- Wedeen VJ, Wang RP, Schmahmann JD, et al. Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage. 2008;41(4):1267-1277.
- Setsompop K, Cohen-Adad J, Gagoski BA, et al. Improving diffusion MRI using simultaneous multi-slice echo planar imaging. Neuroimage. 2012;63(1):569-580.
- Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34(4):537-541.
- Lee MH, Smyser CD, Shimony JS. Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol. 2013;34(10):1866-1872.
- Beckmann CF, Smith SM. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging. 2004;23(2):137-152.
- Dhermain F. Radiotherapy of high-grade gliomas: current standards and new concepts, innovations in imaging and radiotherapy, and new therapeutic approaches. Chin J Cancer. 2014;33(1):16-24.
- Gondi V, Tome WA, Mehta MP. Why avoid the hippocampus? A comprehensive review. Radiother Oncol. 2010;97(3):370-376.
- Gondi V, Pugh SL, Tome WA, et al. Preservation of Memory With Conformal Avoidance of the Hippocampal Neural Stem-Cell Compartment During Whole-Brain Radiotherapy for Brain Metastases (RTOG 0933): A Phase II Multi-Institutional Trial. J Clin Oncol. Pub online Oct 27 2014.
- Brandsma D, van den Bent MJ. Pseudoprogression and pseudoresponse in the treatment of gliomas. Curr Opin Neurol. 2009;22(6):633-638.
- Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963-1972.
- Hu LS, Baxter LC, Smith KA, et al. Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements. AJNR Am J Neuroradiol. 2009;30(3):552-558.
- Tsien C, Galban CJ, Chenevert TL, et al. Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. J Clin Oncol. 2010;28(13):2293-2299.
- Hu LS, Eschbacher JM, Heiserman JE, et al. Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro Oncol. 2012;14(7):919-930.
- Oborski MJ, Laymon CM, Lieberman FS, Mountz JM. Distinguishing pseudoprogression from progression in high-grade gliomas: a brief review of current clinical practice and demonstration of the potential value of 18F-FDG PET. Clin Nucl Med. 2013;38(5):381-384.
- Rabinov JD, Lee PL, Barker FG, et al. In vivo 3-T MR spectroscopy in the distinction of recurrent glioma versus radiation effects: initial experience. Radiology. 2002;225(3):871-879.
- Hamstra DA, Chenevert TL, Moffat BA, et al. Evaluation of the functional diffusion map as an early biomarker of timeto- progression and overall survival in high-grade glioma. Proc Natl Acad Sci U S A. 2005;102(46):16759-16764.
- Ellingson BM, Cloughesy TF, Lai A, Nghiemphu PL, Pope WB. Cell invasion, motility, and proliferation level estimate (CIMPLE) maps derived from serial diffusion MR images in recurrent glioblastoma treated with bevacizumab. J Neurooncol. 2011;105(1):91-101.
- Ellingson BM, LaViolette PS, Rand SD, et al. Spatially quantifying microscopic tumor invasion and proliferation using a voxel-wise solution to a glioma growth model and serial diffusion MRI. Magn Reson Med. 2011;65(4):1131-1143.
- Ellingson BM, Malkin MG, Rand SD, et al. Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. J Magn Reson Imaging. 2010;31(3):538-548.
- LaViolette PS, Mickevicius NJ, Cochran EJ, et al. Precise ex-vivo histological validation of heightened cellularity and diffusion restricted necrosis in regions of dark ADC in seven cases of high-grade glioma. Neuro Oncology. 2014;In Press.
- Raab P, Hattingen E, Franz K, Zanella FE, Lanfermann H. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology. 2010;254(3):876-881.
- Zeng QS, Li CF, Zhang K, Liu H, Kang XS, Zhen JH. Multivoxel 3D proton MR spectroscopy in the distinction of recurrent glioma from radiation injury. J Neurooncol. 2007;84(1):63-69.
- Andronesi OC, Rapalino O, Gerstner E, et al. Detection of oncogenic IDH1 mutations using magnetic resonance spectroscopy of 2-hydroxyglutarate. J Clin Invest. 2013;123(9):3659-3663.
- Andronesi OC, Kim GS, Gerstner E, et al. Detection of 2-hydroxyglutarate in IDH-mutated glioma patients by in vivo spectral-editing and 2D correlation magnetic resonance spectroscopy. Sci Transl Med. 2012;4(116):116ra114.
- Mickevicius NJ, Cochran EJ, Rand SD, Connelly J, Schmainda KM, LaViolette PS. Imaging based, histology trained maps (IBHTMs) of brain tumor cellularity predict tumor presence in pathologically confirmed regions sampled ex-vivo. Proc. International Society for Magnetic Resonance in Medicine Cancer Imaging Workshop; Nov 6-9,2014; Austin, TX.
- Norden AD, Young GS, Setayesh K, et al. Bevacizumab for recurrent malignant gliomas: efficacy, toxicity, and patterns of recurrence. Neurology. 2008;70(10):779- 787.
- Pope WB, Lai A, Nghiemphu P, Mischel P, Cloughesy TF. MRI in patients with high-grade gliomas treated with bevacizumab and chemotherapy. Neurology. 2006;66(8):1258-1260.
- Macdonald DR, Cascino TL, Schold SC, Jr., Cairncross JG. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol. 1990;8(7):1277-1280.
- Hicklin DJ, Ellis LM. Role of the vascular endothelial growth factor pathway in tumor growth and angiogenesis. J Clin Oncol. 2005;23(5):1011-1027.
- Verhoeff JJ, van Tellingen O, Claes A, et al. Concerns about anti-angiogenic treatment in patients with glioblastoma multiforme. BMC Cancer. 2009;9:444.
- Piao Y, Liang J, Holmes L, Henry V, Sulman E, de Groot JF. Acquired resistance to anti-VEGF therapy in glioblastoma is associated with a mesenchymal transition. Clin Cancer Res. 2013;19(16):4392-4403.
- Keunen O, Johansson M, Oudin A, et al. Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma. Proc Natl Acad Sci U S A. 2011;108(9):3749-3754.
- Ellingson BM, Kim HJ, Woodworth DC, et al. Recurrent glioblastoma treated with bevacizumab: contrastenhanced T1-weighted subtraction maps improve tumor delineation and aid prediction of survival in a multicenter clinical trial. Radiology. 2014;271(1):200-210.
- Schmainda KM, Prah M, Connelly J, et al. Dynamicsusceptibility contrast agent MRI measures of relative cerebral blood volume predict response to bevacizumab in recurrent high-grade glioma. Neuro Oncol. 2014;16(6):880-888.
- LaViolette PS, Cohen AD, Prah MA, et al. Vascular change measured with independent component analysis of dynamic susceptibility contrast MRI predicts bevacizumab response in high-grade glioma. Neuro Oncol. 2013;15(4):442-450.
- Emblem KE, Mouridsen K, Bjornerud A, et al. Vessel architectural imaging identifies cancer patient responders to anti-angiogenic therapy. Nat Med. 2013;19(9):1178-1183.
- Ratai EM, Zhang Z, Snyder BS, et al. Magnetic resonance spectroscopy as an early indicator of response to anti-angiogenic therapy in patients with recurrent glioblastoma: RTOG 0625/ACRIN 6677. Neuro Oncol. 2013;15(7):936-944.