Neuro-Oncology Neuro-Oncology 16(12), 1563– 1564, 2014 doi:10.1093/neuonc/nou309 Advance Access date 17 October 2014
Is a picture really worth a 1000 words? Elizabeth R. Gerstner Department of Neurology and Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA ([email protected]) See the article by LaViolette et al, on pages 1599 –1606.
responding through tumor starvation would significantly influence subsequent treatment decisions. Moreover, the holy grail of imaging is to have a direct measure of tumor cell burden rather than surrogate markers of blood brain barrier breakdown or vascularity (i.e. contrast enhancement). Histological studies that corroborate what we think we are measuring with MRI are critical to accomplishing this goal. In this issue of Neuro-oncology, LaViolette et al correlate ex vivo tissue cell density with areas of restricted diffusion as measured by diffusion weighted imaging (specifically ADC values) within regions of abnormal FLAIR hyperintensity. They show that in 6 of 7 patients with high grade glioma low ADC values can represent hypercellularity or necrosis. However, the necrosis was only seen in patients treated with bevacizumab suggesting that ADC values can represent different etiologies in patients depending on their treatment. In addition, the ADC values in the setting of necrosis were lower than in the setting of hypercellular tumor so the quantitative value may shed light on the underlying biological process. Despite the small numbers in this paper and the complexity of working on both macro and micro scales, these results take us a step closer to understanding changes seen on MRI and re-enforce the feasibility of histological correlation with MRI. In addition to histological correlation, an important step to reaching the holy grail of accurately measuring tumor burden is standardizing imaging for high grade gliomas. LaViolette et al used a specific ADC threshold to define low ADC but this absolute threshold may not be generalizable. Different MRI vendors, different field strengths, and different acquisition protocols influence quantitative imaging parameters. Through the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Alzheimer’s disease researchers were able to create a standardized acquisition protocol that allows comparison of data across multiple sites.8 The Jumpstarting Brain Tumor Coalition convened a meeting with the FDA earlier this year to begin a similar process for the brain tumor community so ideally we can pool data to better interpret changes on MRI scans – making a picture really worth a 1000 words.
References 1.
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.
# The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: [email protected].
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Downloaded from http://neuro-oncology.oxfordjournals.org/ at Ritsumeikan University on June 18, 2015
One of the biggest challenges in glioma management is accurately tracking tumor response to treatment. While direct tissue assessment might be ideal, repeat surgery is not always feasible and subject to sampling error. Consequently, MRI (or less ideally CT) is the surrogate tool for tracking changes in tumor in response to therapy. However, there are clear limitations to MRI which, in reality, is just a picture and we do not always know what biological process or processes that picture symbolizes. The most recent iteration of response criteria for high grade gliomas, the RANO criteria, highlight one specific imaging challenge facing Neuro-oncologists: changes in FLAIR hyperintensity associated with treatment particularly in the setting of anti-angiogenic therapy.1 RANO includes a “significant change” in FLAIR hyperintensity as a possible definition of tumor progression. Anti-angiogenic agents block tumor blood vessel formation so are hypothesized to promote invasive, infiltrative tumor growth and co-option of native brain blood vessels with an intact BBB – this progressive growth may be reflected by changes in FLAIR hyperintensity.2,3 Recent studies comparing the older Macdonald criteria, which do not factor in FLAIR changes, to RANO criteria have shown mixed results about the clinical impact of incorporating FLAIR changes.4 Disease progression may be called earlier but there is unclear impact on overall survival with some studies showing that patients with FLAIR changes that predate changes on contrast sequences have no difference in overall survival and others suggesting these patients do better.5 – 7 Adding to the complexity is concern that there may be several different kinds of “FLAIR relapse” with different prognostic significance.7 These mixed results reflect our lack of understanding about what FLAIR hyperintensity truly represents pathologically and how to best measure changes in FLAIR signal. FLAIR hyperintensity may be a final common pathway for multiple processes such as vasogenic edema, seizure, cellular tumor, or gliosis from past chemoradiation, stroke, or surgical injury. Changes in corticosteroid dose also affect FLAIR hyperintensity. Given the heterogeneous nature of high grade gliomas, any or some combination of all of these processes may be occurring in an individual patient. Consequently, it is critical that we develop a better understanding of how to interpret FLAIR changes in glioma patients or develop a better tool to help interpret FLAIR changes. Being able to noninvasively identify the biological impact that drugs are having on tumor is critical to optimizing drug therapy and selecting appropriate salvage therapies for individual patients. For example, knowing if a patient treated with an anti-angiogenic agent is relapsing via infiltrative tumor or
Editorial
2.
Rubenstein JL, Kim J, Ozawa T, et al. Anti-VEGF antibody treatment of glioblastoma prolongs survival but results in increased vascular cooption. Neoplasia. 2000;2(4):306 –314.
3.
Kunkel P, Ulbricht U, Bohlen P, et al. Inhibition of glioma angiogenesis and growth in vivo by systemic treatment with a monoclonal antibody against vascular endothelial growth factor receptor-2. Cancer research. 2001;61(18):6624–6628.
4.
5.
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. Radbruch A, Lutz K, Wiestler B, et al. Relevance of T2 signal changes in the assessment of progression of glioblastoma according to the
Response Assessment in Neurooncology criteria. Neuro-oncology. 2012;14(2):222–229. 6.
Schaub C, Greschus S, Seifert M, et al. FLAIR-only progression in bevacizumab-treated relapsing glioblastoma does not predict short survival. Oncology. 2013;85(3):191– 195.
7.
Nowosielski M, Wiestler B, Goebel G, et al. Progression types after antiangiogenic therapy are related to outcome in recurrent glioblastoma. Neurology. 2014;82(19):1684– 1692.
8.
Jack CR Jr., Bernstein MA, Fox NC, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685–691.
Downloaded from http://neuro-oncology.oxfordjournals.org/ at Ritsumeikan University on June 18, 2015
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Is a picture really worth a 1000 words? Elizabeth R. Gerstner Department of Neurology and Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA ([email protected]) See the article by LaViolette et al, on pages 1599 –1606.
responding through tumor starvation would significantly influence subsequent treatment decisions. Moreover, the holy grail of imaging is to have a direct measure of tumor cell burden rather than surrogate markers of blood brain barrier breakdown or vascularity (i.e. contrast enhancement). Histological studies that corroborate what we think we are measuring with MRI are critical to accomplishing this goal. In this issue of Neuro-oncology, LaViolette et al correlate ex vivo tissue cell density with areas of restricted diffusion as measured by diffusion weighted imaging (specifically ADC values) within regions of abnormal FLAIR hyperintensity. They show that in 6 of 7 patients with high grade glioma low ADC values can represent hypercellularity or necrosis. However, the necrosis was only seen in patients treated with bevacizumab suggesting that ADC values can represent different etiologies in patients depending on their treatment. In addition, the ADC values in the setting of necrosis were lower than in the setting of hypercellular tumor so the quantitative value may shed light on the underlying biological process. Despite the small numbers in this paper and the complexity of working on both macro and micro scales, these results take us a step closer to understanding changes seen on MRI and re-enforce the feasibility of histological correlation with MRI. In addition to histological correlation, an important step to reaching the holy grail of accurately measuring tumor burden is standardizing imaging for high grade gliomas. LaViolette et al used a specific ADC threshold to define low ADC but this absolute threshold may not be generalizable. Different MRI vendors, different field strengths, and different acquisition protocols influence quantitative imaging parameters. Through the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Alzheimer’s disease researchers were able to create a standardized acquisition protocol that allows comparison of data across multiple sites.8 The Jumpstarting Brain Tumor Coalition convened a meeting with the FDA earlier this year to begin a similar process for the brain tumor community so ideally we can pool data to better interpret changes on MRI scans – making a picture really worth a 1000 words.
References 1.
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.
# The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: [email protected].
1563
Downloaded from http://neuro-oncology.oxfordjournals.org/ at Ritsumeikan University on June 18, 2015
One of the biggest challenges in glioma management is accurately tracking tumor response to treatment. While direct tissue assessment might be ideal, repeat surgery is not always feasible and subject to sampling error. Consequently, MRI (or less ideally CT) is the surrogate tool for tracking changes in tumor in response to therapy. However, there are clear limitations to MRI which, in reality, is just a picture and we do not always know what biological process or processes that picture symbolizes. The most recent iteration of response criteria for high grade gliomas, the RANO criteria, highlight one specific imaging challenge facing Neuro-oncologists: changes in FLAIR hyperintensity associated with treatment particularly in the setting of anti-angiogenic therapy.1 RANO includes a “significant change” in FLAIR hyperintensity as a possible definition of tumor progression. Anti-angiogenic agents block tumor blood vessel formation so are hypothesized to promote invasive, infiltrative tumor growth and co-option of native brain blood vessels with an intact BBB – this progressive growth may be reflected by changes in FLAIR hyperintensity.2,3 Recent studies comparing the older Macdonald criteria, which do not factor in FLAIR changes, to RANO criteria have shown mixed results about the clinical impact of incorporating FLAIR changes.4 Disease progression may be called earlier but there is unclear impact on overall survival with some studies showing that patients with FLAIR changes that predate changes on contrast sequences have no difference in overall survival and others suggesting these patients do better.5 – 7 Adding to the complexity is concern that there may be several different kinds of “FLAIR relapse” with different prognostic significance.7 These mixed results reflect our lack of understanding about what FLAIR hyperintensity truly represents pathologically and how to best measure changes in FLAIR signal. FLAIR hyperintensity may be a final common pathway for multiple processes such as vasogenic edema, seizure, cellular tumor, or gliosis from past chemoradiation, stroke, or surgical injury. Changes in corticosteroid dose also affect FLAIR hyperintensity. Given the heterogeneous nature of high grade gliomas, any or some combination of all of these processes may be occurring in an individual patient. Consequently, it is critical that we develop a better understanding of how to interpret FLAIR changes in glioma patients or develop a better tool to help interpret FLAIR changes. Being able to noninvasively identify the biological impact that drugs are having on tumor is critical to optimizing drug therapy and selecting appropriate salvage therapies for individual patients. For example, knowing if a patient treated with an anti-angiogenic agent is relapsing via infiltrative tumor or
Editorial
2.
Rubenstein JL, Kim J, Ozawa T, et al. Anti-VEGF antibody treatment of glioblastoma prolongs survival but results in increased vascular cooption. Neoplasia. 2000;2(4):306 –314.
3.
Kunkel P, Ulbricht U, Bohlen P, et al. Inhibition of glioma angiogenesis and growth in vivo by systemic treatment with a monoclonal antibody against vascular endothelial growth factor receptor-2. Cancer research. 2001;61(18):6624–6628.
4.
5.
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. Radbruch A, Lutz K, Wiestler B, et al. Relevance of T2 signal changes in the assessment of progression of glioblastoma according to the
Response Assessment in Neurooncology criteria. Neuro-oncology. 2012;14(2):222–229. 6.
Schaub C, Greschus S, Seifert M, et al. FLAIR-only progression in bevacizumab-treated relapsing glioblastoma does not predict short survival. Oncology. 2013;85(3):191– 195.
7.
Nowosielski M, Wiestler B, Goebel G, et al. Progression types after antiangiogenic therapy are related to outcome in recurrent glioblastoma. Neurology. 2014;82(19):1684– 1692.
8.
Jack CR Jr., Bernstein MA, Fox NC, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685–691.
Downloaded from http://neuro-oncology.oxfordjournals.org/ at Ritsumeikan University on June 18, 2015
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