Rationale and Objectives
The assessment of the therapeutic response of high-grade gliomas treated with concomitant chemoradiotherapy (CCRT) using temozolomide is difficult because of the frequent occurrence of early imaging changes that are indistinguishable from tumor progression, termed pseudoprogression. The purpose of this study was to determine whether diffusion-weighted imaging could be used to differentiate true progression and pseudoprogression.
Materials and Methods
Magnetic resonance images and diffusion-weighted images obtained within 2 months of CCRT completion in patients with high-grade gliomas were retrospectively reviewed. A total of 22 patients with increases in measurable enhancing regions were identified and classified into true progression and pseudoprogression groups on the basis of contrast-enhanced magnetic resonance images obtained 12 weeks after CCRT. Qualitative and quantitative analysis of diffusion-weighted images and apparent diffusion coefficient maps, respectively, was performed to discriminate true progression and pseudoprogression. Statistical analyses were performed using Fisher’s exact test, unpaired t tests, and receiver-operating characteristic analysis.
Results
The true progression group showed a higher incidence of homogeneous or multifocal high signal intensity on diffusion-weighted images (seven of 10 patients [70%]), whereas rim high or no high signal intensity (10 of 12 [83%]) was observed in the pseudoprogression group ( P = .027). True progression was defined by newly appearing or enlarged enhancing lesions with mean apparent diffusion coefficient values of 1200 × 10 −6 mm 2 /s inside the radiation field after CCRT; the sensitivity, specificity, and accuracy were 80% (eight of 10), 83.3% (10 of 12), and 81.2% (18 of 22), respectively.
Conclusions
The assessment of diffusion-weighted images for patients with increases of measurable enhancing regions 2 months after CCRT completion is useful for differentiating true progression from pseudoprogression.
Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor in adults. The current standard care for newly diagnosed GBM includes postoperative concomitant chemoradiotherapy (CCRT) with temozolomide (TMZ) followed by maintenance TMZ, which significantly prolongs overall survival compared to radiation alone . Assessment of the response to treatment and disease progression, which includes variations in the contrast-enhancing tumor volume on radiologic imaging, neurologic function, and steroid dosage, is based on the criteria defined by Macdonald et al in 1990. However, this traditional method of measuring the enhancing tumor may not assess the true treatment response, particularly with pseudoprogression of high-grade gliomas after CCRT.
Pseudoprogression has been recognized and widely accepted to occur in the treatment of high-grade gliomas, as transient increases of the enhancing area usually <3 months after CCRT . This phenomenon was originally reported by Hoffman et al in 1979 and more fully described by de Wit et al in 2004 as the early tumor bed enhancement of high-grade gliomas treated with radiation therapy with or without carmustine. More recently, this was better defined in the era of the currently used TMZ regimen . Recent reports suggest that up to 30% of patients treated with this regimen may develop lesion changes that mimic early tumor progression on standard contrast-enhanced magnetic resonance (MR) imaging . This treatment-related change has implications for patient management and may result in premature discontinuation of effective adjuvant therapy. The absence of reliable and accepted imaging methods and biochemical markers to discriminate pseudoprogression from true progression further challenges its diagnosis.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Materials and methods
Patients
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Promoter Methylation Status of O6–Methylguanine–Deoxyribonucleic Acid–Methyltransferase
Get Radiology Tree app to read full this article<
MR Image Acquisition
Get Radiology Tree app to read full this article<
MR Image Analysis
Get Radiology Tree app to read full this article<
Qualitative analysis of diffusion-weighted images
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Quantitative analysis of ADC maps
Get Radiology Tree app to read full this article<
Statistical Analysis
Get Radiology Tree app to read full this article<
Results
Clinical and Histopathologic Analysis
Get Radiology Tree app to read full this article<
Table 1
Patient Characteristics
Characteristic Total True Progression Pseudoprogression_P_ ( n = 22) ( n = 10) ( n = 12) Age (y), mean ± standard deviation 48.5 ± 15.8 53 ± 12.8 44.8 ± 17.7 .329 ∗ Diagnosis .221 † Glioblastoma multiforme, grade IV 19 10 9 Anaplastic astrocytoma, grade III 3 0 3 Karnofsky performance status .455 † <70 1 1 0 ≥70 21 9 12 Surgery .412 † Biopsy 5 3 2 Subtotal resection 10 3 7 Gross total resection 7 4 3 Radiation dose (Gy), mean 59.54 59.1 59.9 .071 †
Numerical identifiers refer to individual comparisons.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
MR Image Analysis
Qualitative analysis of diffusion-weighted images
Get Radiology Tree app to read full this article<
Table 2
Signal Intensity Patterns of True Progression and Pseudoprogression on Diffusion-Weighted Imaging
Group Signal Intensity Pattern Homogenous High Multifocal High Rim High No High True progression ( n = 10) 50% (5/10) 20% (2/10) 0% (0/10) 30% (3/10) Pseudoprogression ( n = 12) 0% (0/10) 16.7% (2/12) 41.7% (5/12) 41.7% (5/12)
Get Radiology Tree app to read full this article<
Quantitative analysis of ADC maps
Get Radiology Tree app to read full this article<
Table 3
ADC Values of True Progression and Pseudoprogression
Variable True Progression Pseudoprogression_P_ ∗ ( n = 10) ( n = 12) Volume (mm 3 ) 884 ± 501 2822 ± 2021 .009 Mean ADC (10 −6 mm 2 /s) 1215 ± 385 1349 ± 169 .289 Kurtosis 3.73 ± 2.78 4.11 ± 1.70 .701 Skewness 0.48 ± 0.75 0.71 ± 0.56 .412
ADC, apparent diffusion coefficient.
Data are expressed as mean ± standard deviation.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Discussion
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Conclusions
Get Radiology Tree app to read full this article<
Acknowledgment
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
References
1. Stupp R., Mason W.P., van den Bent M.J., et. al.: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005; 352: pp. 987-996.
2. Stupp R., Hegi M.E., Mason W.P., et. al.: Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 2009; 10: pp. 459-466.
3. Mirimanoff R.O., Gorlia T., Mason W., et. al.: Radiotherapy and temozolomide for newly diagnosed glioblastoma: recursive partitioning analysis of the EORTC 26981/22981-NCIC CE3 phase III randomized trial. J Clin Oncol 2006; 24: pp. 2563-2569.
4. Macdonald D.R., Cascino T.L., Schold S.C., et. al.: Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990; 8: pp. 1277-1280.
5. Brandes A.A., Franceschi E., Tosoni A., et. al.: MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients. J Clin Oncol 2008; 26: pp. 2192-2197.
6. Brandsma D., Stalpers L., Taal W., et. al.: Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol 2008; 9: pp. 453-461.
7. Chamberlain M.C., Glantz M.J., Chalmers L., et. al.: Early necrosis following concurrent Temodar and radiotherapy in patients with glioblastoma. J Neurooncol 2007; 82: pp. 81-83.
8. de Wit M.C., de Bruin H.G., Eijkenboom W., et. al.: Immediate post-radiotherapy changes in malignant glioma can mimic tumor progression. Neurology 2004; 63: pp. 535-537.
9. Taal W., Brandsma D., de Bruin H.G., et. al.: Incidence of early pseudo-progression in a cohort of malignant glioma patients treated with chemoirradiation with temozolomide. Cancer 2008; 113: pp. 405-410.
10. Hoffman W.F., Levin V.A., Wilson C.B.: Evaluation of malignant glioma patients during the postirradiation period. J Neurosurgery 1979; 50: pp. 624-628.
11. Chamberlain M.C.: Pseudoprogression in glioblastoma. J Clin Oncol 2008; 26: pp. 4359.
12. Wen P.Y., Macdonald D.R., Reardon D.A., et. al.: Updated response assessment criteria for high-grade gliomas: Response Assessment in Neuro-Oncology Working Group. J Clin Oncol 2010; 28: pp. 1963-1972.
13. Thon N., Eigenbrod S., Grasbon-Frodl E.M., et. al.: Predominant influence of MGMT methylation in non-resectable glioblastoma after radiotherapy plus temozolomide. J Neurol Neurosurg Psychiatry 2011; 82: pp. 441-446.
14. Cancer Genome Atlas Research Network: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008; 455: pp. 1061-1068.
15. Bjornerud A.: The ICE software package: direct co-registration of anatomical and functional datasets using DICOM image geometry information. Proc Hum Brain Mapping 2003; 19: pp. 1018.
16. Pope W.B., Young J.R., Ellingson B.M.: Advances in MRI assessment of gliomas and response to anti-VEGF therapy. Curr Neurol Neurosci Rep 2011; 11: pp. 336-344.
17. Dhermain F.G., Hau P., Lanfermann H., et. al.: Advanced MRI and PET imaging for assessment of treatment response in patients with gliomas. Lancet Neurol 2010; 9: pp. 906-920.
18. Padhani A.R., Liu G., Koh D.M., et. al.: Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009; 11: pp. 102-125.
19. Lam W.W., Poon W.S., Metreweli C.: Diffusion MR imaging in glioma: does it have any role in the pre-operation determination of grading of glioma?. Clin Radiol 2002; 57: pp. 219-225.
20. Sugahara T., Korogi Y., Kochi M., et. al.: Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 1999; 9: pp. 53-60.
21. Guo A.C., Cummings T.J., Dash R.C., et. al.: Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology 2002; 224: pp. 177-183.
22. Hayashida Y., Hirai T., Morishita S., et. al.: Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. AJNR Am J Neuroradiol 2006; 27: pp. 1419-1425.
23. Herneth A.M., Guccione S., Bednarski M.: Apparent diffusion coefficient: a quantitative parameter for in vivo tumor characterization. Eur J Radiol 2003; 45: pp. 208-213.
24. Asao C., Korogi Y., Kitajima M., et. al.: Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol 2005; 26: pp. 1455-1460.
25. Hein P.A., Eskey C.J., Dunn J.F., et. al.: Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiol 2004; 25: pp. 201-209.
26. Matsusue E., Fink J.R., Rockhill J.K., et. al.: Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology 2010; 52: pp. 297-306.
27. Zeng Q.S., Li C.F., Liu H., et. al.: Distinction between recurrent glioma and radiation injury using magnetic resonance spectroscopy in combination with diffusion-weighted imaging. Int J Radiat Oncol Biol Phys 2007; 68: pp. 151-158.
28. Kong D.S., Kim S.T., Kim E.H., et. al.: Diagnostic dilemma of pseudoprogression in the treatment of newly diagnosed glioblastomas: the role of assessing relative cerebral blood flow volume and oxygen-6-methylguanine-DNA methyltransferase promoter methylation status. AJNR Am J Neuroradiol 2011; 32: pp. 382-387.
29. Gahramanov S., Raslan A.M., Muldoon L.L., et. al.: Potential for differentiation of pseudoprogression from true tumor progression with dynamic susceptibility-weighted contrast-enhanced magnetic resonance imaging using ferumoxytol vs. gadoteridol: a pilot study. Int J Radiat Oncol Biol Phys 2011; 79: pp. 514-523.
30. Tsien C., Galban C.J., Chenevert T.L., et. al.: Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. J Clin Oncol 2010; 28: pp. 2293-2299.