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Contrast-Enhanced T1-Weighted Digital Subtraction for Increased Lesion Conspicuity and Quantifying Treatment Response in Malignant Gliomas

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Abstract

Contrast enhancement is the gold standard for quantifying malignant glioma tumor burden and therapeutic response assessment. However, assessment of contrast-enhancing tumor burden can be challenging in the presence of T1 shortening from postsurgical blood products or treatments or use of therapies that alter vascular permeability. Contrast-enhanced T1-weighted digital subtraction is a quick and simple technique for increasing lesion conspicuity even in the presence of blood products and inflammation and during antiangiogenic therapies that do not increase scan time or require specialized MRI pulse sequences. This results in improved stratification of survival in patients with malignant glioma.

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References

  1. Russell SM, Elliott R, Forshaw D, Golfinos JG, Nelson PK, Kelly PJ. Glioma vascularity correlates with reduced patient survival and increased malignancy. Surg Neurol. 2009;72(3):242–6.

    Article  PubMed  Google Scholar 

  2. Leon SP, Folkerth RD, Black PM. Microvessel density is a prognostic indicator for patients with astroglial brain tumors. Cancer. 1996;77:362–72.

    Article  CAS  PubMed  Google Scholar 

  3. Wesseling P, van der Laak JA, Link M, Teepen HL, Ruiter DJ. Quantitative analysis of microvascular changes in diffuse astrocytic neoplasms with increasing grade of malignancy. Hum Pathol. 1998;29(4):352–8.

    Article  CAS  PubMed  Google Scholar 

  4. Long DM. Capillary ultrastructure and the blood-brain barrier in human malignant brain tumors. J Neurosurg. 1970;32(2):127–44.

    Article  CAS  PubMed  Google Scholar 

  5. Jain RK, di Tomaso E, Duda DG, Loeffler JS, Sorensen AG, Batchelor TT. Angiogenesis in brain tumours. Nat Rev Neurosci. 2007;8(8):610–22.

    Article  CAS  PubMed  Google Scholar 

  6. Plate KH, Mennel HD. Vascular morphology and angiogenesis in glial tumors. Exp Toxicol Pathol. 1995;47:89–94.

    Article  CAS  PubMed  Google Scholar 

  7. Rampling R, Cruickshank G, Lewis A, Fitzsimmon S, Workman P. Direct measurement of pO2 distribution and bioreductive enzymes in human malignant brain tumors. Int J Radiat Oncol Biol Phys. 1994;29:427–31.

    Article  CAS  PubMed  Google Scholar 

  8. Loges S, Mazzone M, Hohensinner P, Carmeliet P. Silencing or fueling metastasis with VEGF inhibitors: antiangiogenesis revisited. Cancer Cell. 2009;15(6):167–70.

    Article  CAS  PubMed  Google Scholar 

  9. Folkman J. Role of angiogenesis in tumor growth and metastasis. Semin Oncol. 2002;29(6 Suppl 16):15–8.

    Article  CAS  PubMed  Google Scholar 

  10. Yuan F, Salehi HA, Boucher Y, Vasthare US, Tuma RF, Jain RK. Vascular permeability and microcirculation of gliomas and mammary carcinomas transplanted in rat and mouse cranial windows. Cancer Res. 1994;54(17):4564–8.

    CAS  PubMed  Google Scholar 

  11. Hobbs SK, Monsky WL, Yuan F, Roberts WG, Griffith L, Torchilin VP, et al. Regulation of transport pathways in tumor vessels: role of tumor type and microenvironment. Proc Nat Acad Sci. 1998;95(8):4607–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Monsky WL, Fukumura D, Gohongi T, Ancukiewcz M, Weich HA, Torchilin VP, et al. Augmentation of transvascular transport of macromolecules and nanoparticles in tumors using vascular endothelial growth factor. Cancer Res. 1999;59(16):4129–35.

    CAS  PubMed  Google Scholar 

  13. Jain RK, Tong RT, Munn LL. Effect of vascular normalization by antiangiogenic therapy on interstitial hypertension, peritumor edema, and lymphatic metastasis: insights from a mathematical model. Cancer Res. 2007;67(6):2729–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Brismar J, Stromblad LG, Salford LG. Impact of CT in the neurosurgical management of intracranial tumors. Neuroradiology. 1978;16:506–9.

    Article  CAS  PubMed  Google Scholar 

  15. Salcman M. Glioblastoma multiforme. Am J Med Sci. 1980;279(2):84–94.

    Article  CAS  PubMed  Google Scholar 

  16. Baker HL Jr, Houser OW, Campbell JK. National Cancer Institute study: evaluation of computed tomography in the diagnosis of intracranial neoplasms. I. Overall results. Radiology. 1980;136(1):91–6.

    Article  PubMed  Google Scholar 

  17. Mansfield P. Multi-planar image formation using NMR spin echoes. J Phys C Solid State Phys. 1977;10(3):L55.

    Article  CAS  Google Scholar 

  18. Carr DH, Brown J, Bydder GM, Weinmann HJ, Speck U, Thomas DJ, et al. Intravenous chelated gadolinium as a contrast agent in NMR imaging of cerebral tumours. Lancet. 1984;1(8375):484–6.

    Article  CAS  PubMed  Google Scholar 

  19. Felix R, Schorner W, Laniado M, Niendorf HP, Claussen C, Fiegler W, et al. Brain tumors: MR imaging with gadolinium-DTPA. Radiology. 1985;156(3):681–8.

    Article  CAS  PubMed  Google Scholar 

  20. Graif M, Bydder GM, Steiner RE, Niendorf P, Thomas DG, Young IR. Contrast-enhanced MR imaging of malignant brain tumors. AJNR Am J Neuroradiol. 1985;6(6):855–62.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Earnest F, Kelly PJ, Scheithauer BW, Kall BA, Cascino TL, Ehman RL, et al. Cerebral astrocytomas: histopathologic correlation of MR and CT contrast enhancement with stereotactic biopsy. Radiology. 1988;166(3):823–7.

    Article  PubMed  Google Scholar 

  22. Claussen C, Laniado M, Kazner E, Schorner W, Felix R. Application of contrast agents in CT and MRI (NMR): their potential in imaging of brain tumors. Neuroradiology. 1985;27(2):164–71.

    Article  CAS  PubMed  Google Scholar 

  23. Haughton VM, Rimm AA, Czervionke LF, Breger RK, Fisher ME, Papke RA, et al. Sensitivity of Gd-DTPA-enhanced MR imaging of benign extraaxial tumors. Radiology. 1988;166(3):829–33.

    Article  CAS  PubMed  Google Scholar 

  24. Dean BL, Drayer BP, Bird CR, Flom RA, Hodak JA, Coons SW, et al. Gliomas: classification with MR imaging. Radiology. 1990;174(2):411–5.

    Article  CAS  PubMed  Google Scholar 

  25. Kelly PJ, Daumas-Duport C, Scheithauer BW, Kall BA, Kispert DB. Stereotactic histologic correlations of computed tomography- and magnetic resonance imaging-defined abnormalities in patients with glial neoplasms. Mayo Clinic Proc. 1987;62(6):450–9.

    Article  CAS  Google Scholar 

  26. Kelly PJ, Daumas-Duport C, Kispert DB, Kall BA, Scheithauer BW, Illig JJ. Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms. J Neurosurg. 1987;66(6):865–74.

    Article  CAS  PubMed  Google Scholar 

  27. Butler AR, Horii SC, Kricheff II, Shannon MB, Budzilovich GN. Computed tomography in astrocytomas. A statistical analysis of the parameters of malignancy and the positive contrast-enhanced CT scan. Radiology. 1978;129(2):433–9.

    Article  CAS  PubMed  Google Scholar 

  28. Lewander R, Bergstrom M, Boethius J, Collins VP, Edner G, Greitz T, et al. Stereotactic computer tomography for biopsy of gliomas. Acta Radiol Diagn (Stockh). 1978;19(6):867–88.

    Article  CAS  Google Scholar 

  29. Amundsen P, Dugstad G, Syvertsen AH. The reliability of computer tomography for the diagnosis and differential diagnosis of meningiomas, gliomas, and brain metastases. Acta Neurochir. 1978;41(1–3):177–90.

    Article  CAS  PubMed  Google Scholar 

  30. Lilja A, Bergstrom K, Spannare B, Olsson Y. Reliability of computed tomography in assessing histopathological features of malignant supratentorial gliomas. J Comput Assist Tomogr. 1981;5(5):625–36.

    CAS  PubMed  Google Scholar 

  31. Burger PC. Pathologic anatomy and CT correlations in the glioblastoma multiforme. Appl Neurophysiol. 1983;46(1–4):180–7.

    CAS  PubMed  Google Scholar 

  32. Burger PC, Dubois PJ, Schold SC Jr, Smith KR Jr, Odom GL, Crafts DC, et al. Computerized tomographic and pathologic studies of the untreated, quiescent, and recurrent glioblastoma multiforme. J Neurosurg. 1983;58(2):159–69.

    Article  CAS  PubMed  Google Scholar 

  33. Burger PC, Heinz ER, Shibata T, Kleihues P. Topographic anatomy and CT correlations in the untreated glioblastoma multiforme. J Neurosurg. 1988;68(5):698–704.

    Article  CAS  PubMed  Google Scholar 

  34. Barajas RF Jr, Hodgson JG, Chang JS, Vandenberg SR, Yeh RF, Parsa AT, et al. Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging. Radiology. 2010;254(2):564–76.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Barajas RF Jr, Phillips JJ, Parvataneni R, Molinaro A, Essock-Burns E, Bourne G, et al. Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR imaging. Neuro-Oncology. 2012;14(7):942–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kubben PL, Wesseling P, Lammens M, Schijns OE, Ter Laak-Poort MP, van Overbeeke JJ, et al. Correlation between contrast enhancement on intraoperative magnetic resonance imaging and histopathology in glioblastoma. Surg Neurol Int. 2012;3:158.

    Article  PubMed  PubMed Central  Google Scholar 

  37. des Plantes Z. Subtraktion. Stuttgart: Thieme Verlag; 1961.

    Google Scholar 

  38. Harrington DP, Boxt LM, Murray PD. Digital subtraction angiography: overview of technical principles. AJR Am J Roentgenol. 1982;139(4):781–6.

    Article  CAS  PubMed  Google Scholar 

  39. Gauvrit JY, Leclerc X, Oppenheim C, Munier T, Trystram D, Rachdi H, et al. Three-dimensional dynamic MR digital subtraction angiography using sensitivity encoding for the evaluation of intracranial arteriovenous malformations: a preliminary study. AJNR Am J Neuroradiol. 2005;26(6):1525–31.

    PubMed  PubMed Central  Google Scholar 

  40. Suto Y, Caner BE, Tamagawa Y, Matsuda T, Kimura I, Kimura H, et al. Subtracted synthetic images in Gd-DTPA enhanced MR. J Comput Assist Tomogr. 1989;13(5):925–8.

    Article  CAS  PubMed  Google Scholar 

  41. Hanna SL, Langston JW, Gronemeyer SA, Fletcher BD. Subtraction technique for contrast-enhanced MR images of musculoskeletal tumors. Magn Reson Imaging. 1990;8(3):213–5.

    Article  CAS  PubMed  Google Scholar 

  42. Gilles R, Guinebretiere JM, Shapeero LG, Lesnik A, Contesso G, Sarrazin D, et al. Assessment of breast cancer recurrence with contrast-enhanced subtraction MR imaging: preliminary results in 26 patients. Radiology. 1993;188(2):473–8.

    Article  CAS  PubMed  Google Scholar 

  43. Lloyd GA, Barker PG, Phelps PD. Subtraction gadolinium enhanced magnetic resonance for head and neck imaging. Br J Radiol. 1993;66(781):12–6.

    Article  CAS  PubMed  Google Scholar 

  44. Murray JG, Stack JP, Ennis JT, Behan M. Digital subtraction in contrast-enhanced MR imaging of the postoperative lumbar spine. AJR Am J Roentgenol. 1994;162(4):893–8.

    Article  CAS  PubMed  Google Scholar 

  45. Lee VS, Flyer MA, Weinreb JC, Krinsky GA, Rofsky NM. Image subtraction in gadolinium-enhanced MR imaging. AJR Am J Roentgenol. 1996;167(6):1427–32.

    Article  CAS  PubMed  Google Scholar 

  46. Tatli S, Acar M, Tuncali K, Sadow CA, Morrison PR, Silverman SG. MRI assessment of percutaneous ablation of liver tumors: value of subtraction images. JMagn ResonImaging. 2013;37(2):407–13.

    Google Scholar 

  47. An C, Park MS, Kim D, Kim YE, Chung WS, Rhee H, et al. Added value of subtraction imaging in detecting arterial enhancement in small (<3 cm) hepatic nodules on dynamic contrast-enhanced MRI in patients at high risk of hepatocellular carcinoma. Eur Radiol. 2013;23(4):924–30.

    Article  PubMed  Google Scholar 

  48. Ogura A, Hayakawa K, Yoshida S, Maeda F, Saeki F, Syukutani A. Use of dynamic phase subtraction (DPS) map in dynamic contrast-enhanced MRI of the breast. J Comput Assist Tomogr. 2011;35(6):749–52.

    Article  PubMed  Google Scholar 

  49. Yu JS, Rofsky NM. Dynamic subtraction MR imaging of the liver: advantages and pitfalls. AJR Am J Roentgenol. 2003;180(5):1351–7.

    Article  PubMed  Google Scholar 

  50. Secil M, Obuz F, Altay C, Gencel O, Igci E, Sagol O, et al. The role of dynamic subtraction MRI in detection of hepatocellular carcinoma. Diagn Interv Radiol. 2008;14(4):200–4.

    PubMed  Google Scholar 

  51. Kransdorf MJ, Murphey MD, Lee JHE, D.A. R, Imaging AMCoB. ACR-SSR Practice Guideline for the Performance and Interpretation of Magnetic Resonance Imaging (MRI) of Bone and Soft Tissue Tumors. Revised 2010 – Resolution #18.

    Google Scholar 

  52. Hanna SL, Langston JW, Gronemeyer SA. Value of subtraction images in the detection of hemorrhagic brain lesions on contrast-enhanced MR images. AJNR Am J Neuroradiol. 1991;12(4):681–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Melhem ER, Mehta NR. Dynamic T1-weighted spin-echo MR imaging: the role of digital subtraction in the demonstration of enhancing brain lesions. JMagnResonImaging. 1999;9(4):503–8.

    CAS  Google Scholar 

  54. Sardanelli F, Losacco C, Iozzelli A, Renzetti P, Rosso E, Parodi RC, et al. Evaluation of Gd-enhancement in brain MR of multiple sclerosis: image subtraction with and without magnetization transfer. Eur Radiol. 2002;12(8):2077–82.

    Article  CAS  PubMed  Google Scholar 

  55. Moraal B, Wattjes MP, Geurts JJ, Knol DL, van Schijndel RA, Pouwels PJ, et al. Improved detection of active multiple sclerosis lesions: 3D subtraction imaging. Radiology. 2010;255(1):154–63.

    Article  PubMed  Google Scholar 

  56. Martens RM, Bechten A, Ingala S, van Schijndel RA, Machado VB, de Jong MC, et al. The value of subtraction MRI in detection of amyloid-related imaging abnormalities with oedema or effusion in Alzheimer's patients: An interobserver study. Eur Radiol. 2018;28(3):1215–26.

    Article  PubMed  Google Scholar 

  57. Shakur SF, Brunozzi D, Hussein AE, Linninger A, Hsu CY, Charbel FT, et al. Validation of cerebral arteriovenous malformation hemodynamics assessed by DSA using quantitative magnetic resonance angiography: preliminary study. J Neurointerv Surg. 2018;10(2):156–61.

    Article  PubMed  Google Scholar 

  58. Mori H, Aoki S, Okubo T, Hayashi N, Masumoto T, Yoshikawa T, et al. Two-dimensional thick-slice MR digital subtraction angiography in the assessment of small to medium-size intracranial arteriovenous malformations. Neuroradiology. 2003;45(1):27–33.

    Article  CAS  PubMed  Google Scholar 

  59. Tsuchiya K, Katase S, Yoshino A, Hachiya J. MR digital subtraction angiography of cerebral arteriovenous malformations. AJNR Am J Neuroradiol. 2000;21(4):707–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Chan JH, Tsui EY, Chan CY, Lai KF, Chau LF, Fong D, et al. Digital subtraction in gadolinium-enhanced MR imaging of the brain: a method to reduce contrast dosage. Eur Radiol. 2002;12(9):2317–21.

    Article  CAS  PubMed  Google Scholar 

  61. Gaul HP, Wallace CJ, Crawley AP. Reverse enhancement of hemorrhagic brain lesions on postcontrast MR: detection with digital image subtraction. AJNR Am J Neuroradiol. 1996;17(9):1675–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Hajnal JV, Saeed N, Oatridge A, Williams EJ, Young IR, Bydder GM. Detection of subtle brain changes using subvoxel registration and subtraction of serial MR images. J Comput Assist Tomogr. 1995;19(5):677–91.

    Article  CAS  PubMed  Google Scholar 

  63. Curati WL, Williams EJ, Oatridge A, Hajnal JV, Saeed N, Bydder GM. Use of subvoxel registration and subtraction to improve demonstration of contrast enhancement in MRI of the brain. Neuroradiology. 1996;38(8):717–23.

    Article  CAS  PubMed  Google Scholar 

  64. Hajnal JV, Saeed N, Soar EJ, Oatridge A, Young IR, Bydder GM. A registration and interpolation procedure for subvoxel matching of serially acquired MR images. J Comput Assist Tomogr. 1995;19(2):289–96.

    Article  CAS  PubMed  Google Scholar 

  65. Wang Y, Johnston DL, Breen JF, Huston J 3rd, Jack CR, Julsrud PR, et al. Dynamic MR digital subtraction angiography using contrast enhancement, fast data acquisition, and complex subtraction. Magn Reson Med. 1996;36(4):551–6.

    Article  CAS  PubMed  Google Scholar 

  66. Rutherford MA, Pennock JM, Cowan FM, Saeed N, Hajnal JV, Bydder GM. Detection of subtle changes in the brains of infants and children via subvoxel registration and subtraction of serial MR images. AJNR Am J Neuroradiol. 1997;18(5):829–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Yoshikawa T, Aoki S, Hori M, Nambu A, Kumagai H, Araki T. Time-resolved two-dimensional thick-slice magnetic resonance digital subtraction angiography in assessing brain tumors. Eur Radiol. 2000;10(5):736–44.

    Article  CAS  PubMed  Google Scholar 

  68. Tsuchiya K, Katase S, Yoshino A, Hachiya J. MR digital subtraction angiography in the diagnosis of meningiomas. Eur J Radiol. 2003;46(2):130–8.

    Article  PubMed  Google Scholar 

  69. Tsuchiya K, Aoki C, Katase S, Hachiya J. MR digital subtraction angiography with three-dimensional data acquisition in the diagnosis of brain tumors: preliminary experience. Magn Reson Imaging. 2004;22(2):149–53.

    Article  PubMed  Google Scholar 

  70. Peng SH, Shen CY, Wu MC, Lin YD, Huang CH, Kang RJ, et al. Image quality improvement in three-dimensional time-of-flight magnetic resonance angiography using the subtraction method for brain and temporal bone diseases. J Chin Med Assoc. 2013;76(8):458–65.

    Article  PubMed  Google Scholar 

  71. Ellingson BM, Abrey LE, Nelson SJ, Kaufmann TJ, Garcia J, Chinot O, et al. Validation of postoperative residual contrast-enhancing tumor volume as an independent prognostic factor for overall survival in newly diagnosed glioblastoma. Neuro-Oncology. 2018;20(9):1240–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Ellingson BM, Abrey LE, Garcia J, Chinot O, Wick W, Saran F, et al. Post-chemoradiation volumetric response predicts survival in newly diagnosed glioblastoma treated with radiation, temozolomide, and bevacizumab or placebo. Neuro-Oncology. 2018;20(11):1525–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Ellingson BM, Aftab DT, Schwab GM, Hessel C, Harris RJ, Woodworth DC, et al. Volumetric response quantified using T1 subtraction predicts long-term survival benefit from cabozantinib monotherapy in recurrent glioblastoma. Neuro-Oncology. 2018;20(10):1411–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Ellingson BM, Kim HJ, Woodworth DC, Pope WB, Cloughesy JN, Harris RJ, et al. Recurrent glioblastoma treated with bevacizumab: contrast-enhanced T1-weighted subtraction maps improve tumor delineation and aid prediction of survival in a multicenter clinical trial. Radiology. 2014;271(1):200–10.

    Article  PubMed  Google Scholar 

  75. Ellingson BM, Harris RJ, Woodworth DC, Leu K, Zaw O, Mason WP, et al. Baseline pretreatment contrast enhancing tumor volume including central necrosis is a prognostic factor in recurrent glioblastoma: evidence from single and multicenter trials. Neuro-Oncology. 2017;19(1):89–98.

    Article  CAS  PubMed  Google Scholar 

  76. Ellingson BM, Nguyen HN, Lai A, Nechifor RE, Zaw O, Pope WB, et al. Contrast-enhancing tumor growth dynamics of preoperative, treatment-naive human glioblastoma. Cancer. 2016;122(11):1718–27.

    Article  CAS  PubMed  Google Scholar 

  77. Ellingson BM, Bendszus M, Boxerman J, Barboriak D, Erickson BJ, Smits M, et al. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro-Oncology. 2015;17(9):1188–98.

    PubMed  PubMed Central  Google Scholar 

  78. Bedekar DP, Jensen T, Rand SD, Malkin MG, Connelly J, Schmainda KM. Delta T1 method: an automatic post-contrast ROI selection technique for brain tumors. Proc Intl Soc Mag Reson Med. 2010;18:2166.

    Google Scholar 

  79. Bedekar DP, Schmainda KM, Rand SD, Connelly J, Malkin MG, Paulson E, et al. Delta T1 (dT1) method as a tool to evaluate tumor progression in patients with brain cancer. JClinOncol. 2011;29(Suppl):Abstract e21056.

    Article  Google Scholar 

  80. Lescher S, Jurcoane A, Veit A, Bahr O, Deichmann R, Hattingen E. Quantitative T1 and T2 mapping in recurrent glioblastomas under bevacizumab: earlier detection of tumor progression compared to conventional MRI. Neuroradiology. 2015;57(1):11–20.

    Article  PubMed  Google Scholar 

  81. Deoni SC, Rutt BK, Peters TM. Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Magn Reson Med. 2003;49(3):515–26.

    Article  PubMed  Google Scholar 

  82. Deoni SC, Peters TM, Rutt BK. High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn Reson Med. 2005;53(1):237–41.

    Article  PubMed  Google Scholar 

  83. Henderson E, McKinnon G, Lee TY, Rutt BK. A fast 3D look-locker method for volumetric T1 mapping. Magn Reson Imaging. 1999;17(8):1163–71.

    Article  CAS  PubMed  Google Scholar 

  84. Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PF, Gruetter R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. NeuroImage. 2010;49(2):1271–81.

    Article  PubMed  Google Scholar 

  85. Vos MJ, Uitdehaag BM, Barkhof F, Heimans JJ, Baayen HC, Boogerd W, et al. Interobserver variability in the radiological assessment of response to chemotherapy in glioma. Neurology. 2003;60(5):826–30.

    Article  CAS  PubMed  Google Scholar 

  86. Shah GD, Kesari S, Xu R, Batchelor TT, O’Neill AM, Hochberg FH, et al. Comparison of linear and volumetric criteria in assessing tumor response in adult high-grade gliomas. Neuro-Oncology. 2006;8(1):38–46.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Dempsey MF, Condon BR, Hadley DM. Measurement of tumor “size” in recurrent malignant glioma: 1D, 2D, or 3D? AJNR Am J Neuroradiol. 2005;26(4):770–6.

    PubMed  PubMed Central  Google Scholar 

  88. Chow KL, Gobin YP, Cloughesy T, Sayre JW, Villablanca JP, Vinuela F. Prognostic factors in recurrent glioblastoma multiforme and anaplastic astrocytoma treated with selective intra-arterial chemotherapy. AJNR Am J Neuroradiol. 2000;21(3):471–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Reeves GI, Marks JE. Prognostic significance of lesion size for glioblastoma multiforme. Radiology. 1979;132(2):469–71.

    Article  CAS  PubMed  Google Scholar 

  90. Wood JR, Green SB, Shapiro WR. The prognostic importance of tumor size in malignant gliomas: a computed tomographic scan study by the Brain Tumor Cooperative Group. J Clin Oncol. 1988;6(2):338–43.

    Article  CAS  PubMed  Google Scholar 

  91. Provenzale JM, Ison C, Delong D. Bidimensional measurements in brain tumors: assessment of interobserver variability. AJR Am J Roentgenol. 2009;193(6):W515–22.

    Article  PubMed  Google Scholar 

  92. Warren KE, Patronas N, Aikin AA, Albert PS, Balis FM. Comparison of one-, two-, and three-dimensional measurements of childhood brain tumors. J Natl Cancer Inst. 2001;93(18):1401–5.

    Article  CAS  PubMed  Google Scholar 

  93. Gallego Perez-Larraya J, Lahutte M, Petrirena G, Reyes-Botero G, Gonzalez-Aguilar A, Houillier C, et al. Response assessment in recurrent glioblastoma treated with irinotecan-bevacizumab: comparative analysis of the Macdonald, RECIST, RANO, and RECIST + F criteria. Neuro-Oncology. 2012;14(5):667–73.

    Article  CAS  PubMed  Google Scholar 

  94. Fornage BD. Measuring masses on cross-sectional images. Radiology. 1993;187(1):289.

    Article  CAS  PubMed  Google Scholar 

  95. Hopper KD, Kasales CJ, Van Slyke MA, Schwartz TA, TenHave TR, Jozefiak JA. Analysis of interobserver and intraobserver variability in CT tumor measurements. AJR Am J Roentgenol. 1996;167(4):851–4.

    Article  CAS  PubMed  Google Scholar 

  96. Lavin PT, Flowerdew G. Studies in variation associated with the measurement of solid tumors. Cancer. 1980;46(5):1286–90.

    Article  CAS  PubMed  Google Scholar 

  97. Quoix E, Wolkove N, Hanley J, Kreisman H. Problems in radiographic estimation of response to chemotherapy and radiotherapy in small cell lung cancer. Cancer. 1988;62(3):489–93.

    Article  CAS  PubMed  Google Scholar 

  98. Thiesse P, Ollivier L, Di Stefano-Louineau D, Negrier S, Savary J, Pignard K, et al. Response rate accuracy in oncology trials: reasons for interobserver variability. Groupe Francais d'Immunotherapie of the Federation Nationale des Centres de Lutte Contre le Cancer. J Clin Oncol. 1997;15(12):3507–14.

    Article  CAS  PubMed  Google Scholar 

  99. Warr D, McKinney S, Tannock I. Influence of measurement error on assessment of response to anticancer chemotherapy: proposal for new criteria of tumor response. J Clin Oncol. 1984;2(9):1040–6.

    Article  CAS  PubMed  Google Scholar 

  100. Kaus MR, Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R. Automated segmentation of MR images of brain tumors. Radiology. 2001;218(2):586–91.

    Article  CAS  PubMed  Google Scholar 

  101. Salman YM. Modified technique for volumetric brain tumor measurements. J Biomed Sci Eng. 2009;2:16–9.

    Article  Google Scholar 

  102. Mazzara GP, Velthuizen RP, Pearlman JL, Greenberg HM, Wagner H. Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int J Radiat Oncol Biol Phys. 2004;59(1):300–12.

    Article  PubMed  Google Scholar 

  103. Weltens C, Menten J, Feron M, Bellon E, Demaerel P, Maes F, et al. Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging. RadiotherOncol. 2001;60(1):49–59.

    CAS  Google Scholar 

  104. Khoo VS, Adams EJ, Saran F, Bedford JL, Perks JR, Warrington AP, et al. A comparison of clinical target volumes determined by CT and MRI for the radiotherapy planning of base of skull meningiomas. Int J Radiat Oncol Biol Phys. 2000;46(5):1309–17.

    Article  CAS  PubMed  Google Scholar 

  105. Kanaly CW, Ding D, Mehta AI, Waller AF, Crocker I, Desjardins A, et al. A novel method for volumetric MRI response assessment of enhancing brain tumors. PLoS One. 2011;6(1):e16031.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. 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):1258–60.

    Article  CAS  PubMed  Google Scholar 

  107. Chamberlain MC. MRI in patients with high-grade gliomas treated with bevacizumab and chemotherapy. Neurology. 2006;67(11):2089. author reply.

    Article  PubMed  Google Scholar 

  108. Sathornsumetee S, Cao Y, Marcello JE, Herndon JE 2nd, McLendon RE, Desjardins A, et al. Tumor angiogenic and hypoxic profiles predict radiographic response and survival in malignant astrocytoma patients treated with bevacizumab and irinotecan. J Clin Oncol. 2008;26(2):271–8.

    Article  CAS  PubMed  Google Scholar 

  109. Norden AD, Young GS, Setayesh K, Muzikansky A, Klufas R, Ross GL, et al. Bevacizumab for recurrent malignant gliomas: efficacy, toxicity, and patterns of recurrence. Neurology. 2008;70(10):779–87.

    Article  CAS  PubMed  Google Scholar 

  110. Bokstein F, Shpigel S, Blumenthal DT. Treatment with bevacizumab and irinotecan for recurrent high-grade glial tumors. Cancer. 2008;112(10):2267–73.

    Article  CAS  PubMed  Google Scholar 

  111. Ananthnarayan S, Bahng J, Roring J, Nghiemphu P, Lai A, Cloughesy T, et al. Time course of imaging changes of GBM during extended bevacizumab treatment. J Neuro-Oncol. 2008;88(3):339–47.

    Article  Google Scholar 

  112. Kang TY, Jin T, Elinzano H, Peereboom D. Irinotecan and bevacizumab in progressive primary brain tumors, an evaluation of efficacy and safety. J Neuro-Oncol. 2008;89(1):113–8.

    Article  CAS  Google Scholar 

  113. de Groot JF, Yung WK. Bevacizumab and irinotecan in the treatment of recurrent malignant gliomas. Cancer J. 2008;14(5):279–85.

    Article  PubMed  Google Scholar 

  114. Zuniga RM, Torcuator R, Jain R, Anderson J, Doyle T, Ellika S, et al. Efficacy, safety and patterns of response and recurrence in patients with recurrent high-grade gliomas treated with bevacizumab plus irinotecan. J Neuro-Oncol. 2009;91(3):329–36.

    Article  CAS  Google Scholar 

  115. Batchelor TT, Sorensen AG, di Tomaso E, Zhang WT, Duda DG, Cohen KS, et al. AZD2171, a pan-VEGF receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell. 2007;11(1):83–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Friedman HS, Prados MD, Wen PY, Mikkelsen T, Schiff D, Abrey LE, et al. Bevacizumab alone and in combination with irinotecan in recurrent glioblastoma. J Clin Oncol. 2009;27(28):4733–40.

    Article  CAS  PubMed  Google Scholar 

  117. Vredenburgh JJ, Desjardins A, Herndon JE 2nd, Dowell JM, Reardon DA, Quinn JA, et al. Phase II trial of bevacizumab and irinotecan in recurrent malignant glioma. ClinCancer Res. 2007;13(4):1253–9.

    CAS  Google Scholar 

  118. Vredenburgh JJ, Desjardins A, Herndon JE 2nd, Marcello J, Reardon DA, Quinn JA, et al. Bevacizumab plus irinotecan in recurrent glioblastoma multiforme. J Clin Oncol. 2007;25(30):4722–9.

    Article  CAS  PubMed  Google Scholar 

  119. Friedman HS, Petros WP, Friedman AH, Schaaf LJ, Kerby T, Lawyer J, et al. Irinotecan therapy in adults with recurrent or progressive malignant glioma. J Clin Oncol. 1999;17(5):1516–25.

    Article  CAS  PubMed  Google Scholar 

  120. Cloughesy TF, Filka E, Kuhn J, Nelson G, Kabbinavar F, Friedman H, et al. Two studies evaluating irinotecan treatment for recurrent malignant glioma using an every-3-week regimen. Cancer. 2003;97(9 Suppl):2381–6.

    Article  CAS  PubMed  Google Scholar 

  121. Raymond E, Fabbro M, Boige V, Rixe O, Frenay M, Vassal G, et al. Multicentre phase II study and pharmacokinetic analysis of irinotecan in chemotherapy-naive patients with glioblastoma. AnnOncol. 2003;14(4):603–14.

    CAS  Google Scholar 

  122. Prados MD, Lamborn K, Yung WK, Jaeckle K, Robins HI, Mehta M, et al. A phase 2 trial of irinotecan (CPT-11) in patients with recurrent malignant glioma: a North American Brain Tumor Consortium study. Neuro-Oncology. 2006;8(2):189–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Taal W, Oosterkamp HM, Walenkamp AM, Dubbink HJ, Beerepoot LV, Hanse MC, et al. Single-agent bevacizumab or lomustine versus a combination of bevacizumab plus lomustine in patients with recurrent glioblastoma (BELOB trial): a randomised controlled phase 2 trial. Lancet Oncol. 2014;15(9):943–53.

    Article  CAS  PubMed  Google Scholar 

  124. Hygino da Cruz LC Jr, Rodriguez I, Domingues RC, Gasparetto EL, Sorensen AG. Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol. 2011;32(11):1978–85.

    Article  PubMed  PubMed Central  Google Scholar 

  125. Gahrmann R, van den Bent M, van der Holt B, Vernhout RM, Taal W, Vos M, et al. Comparison of 2D (RANO) and volumetric methods for assessment of recurrent glioblastoma treated with bevacizumab-a report from the BELOB trial. Neuro-Oncology. 2017;19(6):853–61.

    Article  PubMed  PubMed Central  Google Scholar 

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Ellingson, B.M. (2020). Contrast-Enhanced T1-Weighted Digital Subtraction for Increased Lesion Conspicuity and Quantifying Treatment Response in Malignant Gliomas. In: Pope, W. (eds) Glioma Imaging. Springer, Cham. https://doi.org/10.1007/978-3-030-27359-0_4

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