Skip to main content
SpringerLink
Log in

Parametric Response Map (PRM) Analysis Improves Response Assessment in Gliomas

  • Chapter
  • First Online:
Glioma Imaging

Abstract

Current strategies for treatment and management of gliomas incorporate molecular markers to individualize patient care. Physiologic imaging identifies treatment-associated alterations in tissue properties including cellular viability, vascular function, and molecular response. Imaging biomarkers provide a noninvasive approach for early treatment response assessment and are an important tool to individualize patient management. Gliomas are heterogeneous tumors. Parametric response mapping (PRM) is a novel voxel-wise imaging analysis method which directly addresses the issue of tumor heterogeneity. PRM image analysis improves the predictive accuracy of imaging biomarkers in clinical applications including treatment response assessment. A multi-parametric imaging biomarker derived from MR perfusion and diffusion-weighted imaging (DWI) using a PRM analysis method provides early identification of high-grade glioma chemoradiation resistance. Further validation of PRM-derived imaging biomarkers is required prior to routine clinical application as an early treatment response marker.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, 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(5):459–66.

    Article  CAS  PubMed  Google Scholar 

  2. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987–96.

    Article  CAS  PubMed  Google Scholar 

  3. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131(6):803–20.

    Article  PubMed  Google Scholar 

  4. Kickingereder P, Neuberger U, Bonekamp D, Piechotta PL, Gotz M, Wick A, et al. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro-Oncology. 2018;20(6):848–57.

    Article  CAS  PubMed  Google Scholar 

  5. Kickingereder P, Burth S, Wick A, Gotz M, Eidel O, Schlemmer HP, et al. Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology. 2016;280(3):880–9.

    Article  PubMed  Google Scholar 

  6. Cao Y, Sundgren PC, Tsien CI, Chenevert TT, Junck L. Physiologic and metabolic magnetic resonance imaging in gliomas. J Clin Oncol. 2006;24(8):1228–35.

    Article  PubMed  Google Scholar 

  7. Galban CJ, Chenevert TL, Meyer CR, Tsien C, Lawrence TS, Hamstra DA, et al. The parametric response map is an imaging biomarker for early cancer treatment outcome. Nat Med. 2009;15(5):572–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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):1277–80.

    Article  CAS  PubMed  Google Scholar 

  9. Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963–72.

    Article  PubMed  Google Scholar 

  10. Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 2008;9(5):453–61.

    Article  PubMed  Google Scholar 

  11. Clarke JL, Chang S. Pseudoprogression and pseudoresponse: challenges in brain tumor imaging. Curr Neurol Neurosci Rep. 2009;9(3):241–6.

    Article  PubMed  Google Scholar 

  12. 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 

  13. Castillo M, Smith JK, Kwock L, Wilber K. Apparent diffusion coefficients in the evaluation of high-grade cerebral gliomas. AJNR Am J Neuroradiol. 2001;22(1):60–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Radbruch A, Fladt J, Kickingereder P, Wiestler B, Nowosielski M, Baumer P, et al. Pseudoprogression in patients with glioblastoma: clinical relevance despite low incidence. Neuro-Oncology. 2015;17(1):151–9.

    Article  PubMed  Google Scholar 

  15. Radbruch A, Lutz K, Wiestler B, Baumer P, Heiland S, Wick W, et al. Relevance of T2 signal changes in the assessment of progression of glioblastoma according to the response assessment in neuro-oncology criteria. Neuro-Oncology. 2012;14(2):222–9.

    Article  CAS  PubMed  Google Scholar 

  16. Pope WB, Hessel C. Response assessment in neuro-oncology criteria: implementation challenges in multicenter neuro-oncology trials. AJNR Am J Neuroradiol. 2011;32(5):794–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Law M, Young RJ, Babb JS, Peccerelli N, Chheang S, Gruber ML, et al. Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology. 2008;247(2):490–8.

    Article  PubMed  Google Scholar 

  18. Chenevert TL, Ross BD. Diffusion imaging for therapy response assessment of brain tumor. Neuroimaging Clin N Am. 2009;19(4):559–71.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chenevert TL, Stegman LD, Taylor JM, Robertson PL, Greenberg HS, Rehemtulla A, et al. Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst. 2000;92(24):2029–36.

    Article  CAS  PubMed  Google Scholar 

  20. Moffat BA, Chenevert TL, Meyer CR, McKeever PE, Hall DE, Hoff BA, et al. The functional diffusion map: an imaging biomarker for the early prediction of cancer treatment outcome. Neoplasia. 2006;8(4):259–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Moffat BA, Chenevert TL, Lawrence TS, Meyer CR, Johnson TD, Dong Q, et al. Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci U S A. 2005;102(15):5524–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chenevert TL, McKeever PE, Ross BD. Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res. 1997;3(9):1457–66.

    CAS  PubMed  Google Scholar 

  23. Hamstra DA, Chenevert TL, Moffat BA, Johnson TD, Meyer CR, Mukherji SK, et al. Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci U S A. 2005;102(46):16759–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Eidel O, Burth S, Neumann JO, Kieslich PJ, Sahm F, Jungk C, et al. Tumor infiltration in enhancing and non-enhancing parts of glioblastoma: a correlation with histopathology. PLoS One. 2017;12(1):e0169292.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Galban CJ, Lemasson B, Hoff BA, Johnson TD, Sundgren PC, Tsien C, et al. Development of a multiparametric voxel-based magnetic resonance imaging biomarker for early cancer therapeutic response assessment. Tomography. 2015;1(1):44–52.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ellingson BM, Malkin MG, Rand SD, Connelly JM, Quinsey C, LaViolette PS, et al. Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. J Magn Reson Imaging. 2010;31(3):538–48.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Szafer A, Zhong J, Anderson AW, Gore JC. Diffusion-weighted imaging in tissues: theoretical models. NMR Biomed. 1995;8(7–8):289–96.

    Article  CAS  PubMed  Google Scholar 

  28. Hamstra DA, Galban CJ, Meyer CR, Johnson TD, Sundgren PC, Tsien C, et al. Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival. J Clin Oncol. 2008;26(20):3387–94.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Cao Y, Tsien CI, Nagesh V, Junck L, Ten Haken R, Ross BD, et al. Survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT [corrected]. Int J Radiat Oncol Biol Phys. 2006;64(3):876–85.

    Article  PubMed  Google Scholar 

  30. Cao Y, Nagesh V, Hamstra D, Tsien CI, Ross BD, Chenevert TL, et al. The extent and severity of vascular leakage as evidence of tumor aggressiveness in high-grade gliomas. Cancer Res. 2006;66(17):8912–7.

    Article  CAS  PubMed  Google Scholar 

  31. Kickingereder P, Radbruch A, Burth S, Wick A, Heiland S, Schlemmer HP, et al. MR perfusion-derived hemodynamic parametric response mapping of bevacizumab efficacy in recurrent glioblastoma. Radiology. 2016;279(2):542–52.

    Article  PubMed  Google Scholar 

  32. Fatterpekar GM, Galheigo D, Narayana A, Johnson G, Knopp E. Treatment-related change versus tumor recurrence in high-grade gliomas: a diagnostic conundrum–use of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI. AJR Am J Roentgenol. 2012;198(1):19–26.

    Article  PubMed  Google Scholar 

  33. Schmainda K. DSC-MRI Measures of Relative Cerebral Blood Volume (rCBV) as a prognostic marker for progression-free and overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 multi-center trial. ISMRM; Milan, Italy 2014.

    Google Scholar 

  34. Lemasson B, Chenevert TL, Lawrence TS, Tsien C, Sundgren PC, Meyer CR, et al. Impact of perfusion map analysis on early survival prediction accuracy in glioma patients. Transl Oncol. 2013;6(6):766–74.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Chenevert TL, Malyarenko DI, Galban CJ, Gomez-Hassan DM, Sundgren PC, Tsien CI, et al. Comparison of voxel-wise and histogram analyses of glioma ADC maps for prediction of early therapeutic change. Tomography. 2019;5(1):7–14.

    Article  PubMed  PubMed Central  Google Scholar 

  36. LaViolette PS, Mickevicius NJ, Cochran EJ, Rand SD, Connelly J, Bovi JA, et al. Precise ex vivo histological validation of heightened cellularity and diffusion-restricted necrosis in regions of dark apparent diffusion coefficient in 7 cases of high-grade glioma. Neuro-Oncology. 2014;16(12):1599–606.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Galban CJ, Chenevert TL, Meyer CR, Tsien C, Lawrence TS, Hamstra DA, et al. Prospective analysis of parametric response map-derived MRI biomarkers: identification of early and distinct glioma response patterns not predicted by standard radiographic assessment. Clin Cancer Res. 2011;17(14):4751–60.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Ellingson BM, Cloughesy TF, Lai A, Nghiemphu PL, Liau LM, Pope WB. Quantitative probabilistic functional diffusion mapping in newly diagnosed glioblastoma treated with radiochemotherapy. Neuro-Oncology. 2013;15(3):382–90.

    Article  CAS  PubMed  Google Scholar 

  39. Pope WB, Kim HJ, Huo J, Alger J, Brown MS, Gjertson D, et al. Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology. 2009;252(1):182–9.

    Article  PubMed  Google Scholar 

  40. Pope WB, Lai A, Mehta R, Kim HJ, Qiao J, Young JR, et al. Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am J Neuroradiol. 2011;32(5):882–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wen Q, Jalilian L, Lupo JM, Molinaro AM, Chang SM, Clarke J, et al. Comparison of ADC metrics and their association with outcome for patients with newly diagnosed glioblastoma being treated with radiation therapy, temozolomide, erlotinib and bevacizumab. J Neuro-Oncol. 2015;121(2):331–9.

    Article  CAS  Google Scholar 

  42. Pope WB, Qiao XJ, Kim HJ, Lai A, Nghiemphu P, Xue X, et al. Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study. J Neuro-Oncol. 2012;108(3):491–8.

    Article  CAS  Google Scholar 

  43. Paulson ES, Schmainda KM. Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. Radiology. 2008;249(2):601–13.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lee KC, Bradley DA, Hussain M, Meyer CR, Chenevert TL, Jacobson JA, et al. A feasibility study evaluating the functional diffusion map as a predictive imaging biomarker for detection of treatment response in a patient with metastatic prostate cancer to the bone. Neoplasia. 2007;9(12):1003–11.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Lee KC, Moffat BA, Schott AF, Layman R, Ellingworth S, Juliar R, et al. Prospective early response imaging biomarker for neoadjuvant breast cancer chemotherapy. Clin Cancer Res. 2007;13(2 Pt 1):443–50.

    Article  CAS  PubMed  Google Scholar 

  46. Srinivasan A, Chenevert TL, Dwamena BA, Eisbruch A, Watcharotone K, Myles JD, et al. Utility of pretreatment mean apparent diffusion coefficient and apparent diffusion coefficient histograms in prediction of outcome to chemoradiation in head and neck squamous cell carcinoma. J Comput Assist Tomogr. 2012;36(1):131–7.

    Article  PubMed  Google Scholar 

  47. Bonekamp S, Jolepalem P, Lazo M, Gulsun MA, Kiraly AP, Kamel IR. Hepatocellular carcinoma: response to TACE assessed with semiautomated volumetric and functional analysis of diffusion-weighted and contrast-enhanced MR imaging data. Radiology. 2011;260(3):752–61.

    Article  PubMed  Google Scholar 

  48. Sarkar S, Johnson TD, Ma B, Chenevert TL, Bland PH, Park H, et al. Evaluation of an automatic registration-based algorithm for direct measurement of volume change in tumors. Int J Radiat Oncol Biol Phys. 2012;83(3):1038–46.

    Article  PubMed  Google Scholar 

  49. Ellingson BM, Cloughesy TF, Lai A, Nghiemphu PL, Pope WB. Nonlinear registration of diffusion-weighted images improves clinical sensitivity of functional diffusion maps in recurrent glioblastoma treated with bevacizumab. Magn Reson Med. 2012;67(1):237–45.

    Article  CAS  PubMed  Google Scholar 

  50. Norris DG. Implications of bulk motion for diffusion-weighted imaging experiments: effects, mechanisms, and solutions. J Magn Reson Imaging. 2001;13(4):486–95.

    Article  CAS  PubMed  Google Scholar 

  51. Ellingson BM, Cloughesy TF, Zaw T, Lai A, Nghiemphu PL, Harris R, et al. Functional diffusion maps (fDMs) evaluated before and after radiochemotherapy predict progression-free and overall survival in newly diagnosed glioblastoma. Neuro-Oncology. 2012;14(3):333–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 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 

  53. Hoff BA. Parametric response mapping of FLAIR MRI provides an early indication of progression risk in glioblastoma. Radiology: Imaging Cancer 2019;1.

    Google Scholar 

  54. Taal W, Brandsma D, de Bruin HG, Bromberg JE, Swaak-Kragten AT, Smitt PA, et al. Incidence of early pseudo-progression in a cohort of malignant glioma patients treated with chemoirradiation with temozolomide. Cancer. 2008;113(2):405–10.

    Article  CAS  PubMed  Google Scholar 

  55. Tsien C, Galban CJ, Chenevert TL, Johnson TD, Hamstra DA, Sundgren PC, et al. Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. J Clin Oncol. 2010;28(13):2293–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Reimer C, Deike K, Graf M, Reimer P, Wiestler B, Floca RO, et al. Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps. PLoS One. 2017;12(4):e0174620.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Ceschin R, Kurland BF, Abberbock SR, Ellingson BM, Okada H, Jakacki RI, et al. Parametric response mapping of apparent diffusion coefficient as an imaging biomarker to distinguish pseudoprogression from true tumor progression in peptide-based vaccine therapy for pediatric diffuse intrinsic pontine glioma. AJNR Am J Neuroradiol. 2015;36(11):2170–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ellingson BM, Malkin MG, Rand SD, LaViolette PS, Connelly JM, Mueller WM, et al. Volumetric analysis of functional diffusion maps is a predictive imaging biomarker for cytotoxic and anti-angiogenic treatments in malignant gliomas. J Neuro-Oncol. 2011;102(1):95–103.

    Article  Google Scholar 

  59. Kickingereder P, Gotz M, Muschelli J, Wick A, Neuberger U, Shinohara RT, et al. Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res. 2016;22(23):5765–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ellingson BM, Kim E, Woodworth DC, Marques H, Boxerman JL, Safriel Y, et al. Diffusion MRI quality control and functional diffusion map results in ACRIN 6677/RTOG 0625: a multicenter, randomized, phase II trial of bevacizumab and chemotherapy in recurrent glioblastoma. Int J Oncol. 2015;46(5):1883–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Mulkern RV, Forbes P, Dewey K, Osganian S, Clark M, Wong S, et al. Establishment and results of a magnetic resonance quality assurance program for the pediatric brain tumor consortium. Acad Radiol. 2008;15(9):1099–110.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Malyarenko D, Galban CJ, Londy FJ, Meyer CR, Johnson TD, Rehemtulla A, et al. Multi-system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice-water phantom. J Magn Reson Imaging. 2013;37(5):1238–46.

    Article  PubMed  Google Scholar 

  63. 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 

  64. Partridge SC, Zhang Z, Newitt DC, Gibbs JE, Chenevert TL, Rosen MA, et al. Diffusion-weighted MRI findings predict pathologic response in neoadjuvant treatment of breast cancer: the ACRIN 6698 multicenter trial. Radiology. 2018;289(3):618–27.

    Article  PubMed  Google Scholar 

  65. Ceschin R, Kocak M, Vajapeyam S, Pollack IF, Onar-Thomas A, Dunkel IJ, et al. Quantifying radiation therapy response using apparent diffusion coefficient (ADC) parametric mapping of pediatric diffuse intrinsic pontine glioma: a report from the pediatric brain tumor consortium. J Neuro-Oncol. 2019;143:79.

    Article  CAS  Google Scholar 

  66. Tsien CI, Brown D, Normolle D, Schipper M, Piert M, Junck L, et al. Concurrent temozolomide and dose-escalated intensity-modulated radiation therapy in newly diagnosed glioblastoma. Clin Cancer Res. 2012;18(1):273–9.

    Article  CAS  PubMed  Google Scholar 

  67. Keith L, Ross BD, Galban CJ, Luker GD, Galban S, Zhao B, et al. Semiautomated workflow for clinically streamlined glioma parametric response mapping. Tomography. 2016;2(4):267–75.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Kickingereder P, Bonekamp D, Nowosielski M, Kratz A, Sill M, Burth S, et al. Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology. 2016;281(3):907–18.

    Article  PubMed  Google Scholar 

  69. Lee IH, Piert M, Gomez-Hassan D, Junck L, Rogers L, Hayman J, et al. Association of 11C-methionine PET uptake with site of failure after concurrent temozolomide and radiation for primary glioblastoma multiforme. Int J Radiat Oncol Biol Phys. 2009;73(2):479–85.

    Article  CAS  PubMed  Google Scholar 

  70. Tsien CI, Cao Y, Lawrence TS. Functional and metabolic magnetic resonance imaging and positron emission tomography for tumor volume definition in high-grade gliomas. Semin Radiat Oncol. 2009;19(3):155–62.

    Article  PubMed  Google Scholar 

  71. Ellingson BM, Chen W, Harris RJ, Pope WB, Lai A, Nghiemphu PL, et al. PET parametric response mapping for clinical monitoring and treatment response evaluation in brain tumors. PET Clin. 2013;8(2):201–17.

    Article  PubMed  Google Scholar 

  72. Harris RJ, Cloughesy TF, Pope WB, Nghiemphu PL, Lai A, Zaw T, et al. 18F-FDOPA and 18F-FLT positron emission tomography parametric response maps predict response in recurrent malignant gliomas treated with bevacizumab. Neuro-Oncology. 2012;14(8):1079–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Chen W, Delaloye S, Silverman DH, Geist C, Czernin J, Sayre J, et al. Predicting treatment response of malignant gliomas to bevacizumab and irinotecan by imaging proliferation with [18F] fluorothymidine positron emission tomography: a pilot study. J Clin Oncol. 2007;25(30):4714–21.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Lauren Keith, Ph.D, for her significant contributions to the manuscript including the generation of Fig. 7.5 and 7.6.

Funding by NIH R35CA197701, U01CA166104 and P01CA085878.

Author information

Authors and Affiliations

  1. Department of Radiation Oncology, University of California Davis, Sacramento, CA, USA

    Michael Cardenas

  2. Department of Radiology, University of Michigan Medical School, Ann Arbor, MI, USA

    Craig J. Galban, Thomas L. Chenevert & Brian D. Ross

  3. Department of Neuroradiology, Washington University School of Medicine, St. Louis, MO, USA

    Michelle Miller-Thomas

  4. University of Michigan School of Medicine, Departments of Radiology and Biological Chemistry, Center for Molecular Imaging, Ann Arbor, MI, USA

    Brian D. Ross

  5. Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA

    Christina Tsien

Authors
  1. Michael Cardenas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Craig J. Galban
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas L. Chenevert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michelle Miller-Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brian D. Ross
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christina Tsien
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Brian D. Ross or Christina Tsien .

Editor information

Editors and Affiliations

  1. David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Whitney B. Pope

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cardenas, M., Galban, C.J., Chenevert, T.L., Miller-Thomas, M., Ross, B.D., Tsien, C. (2020). Parametric Response Map (PRM) Analysis Improves Response Assessment in Gliomas. In: Pope, W. (eds) Glioma Imaging. Springer, Cham. https://doi.org/10.1007/978-3-030-27359-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-27359-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27358-3

  • Online ISBN: 978-3-030-27359-0

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation