Download PDF
Semin Neurol 2018; 38(01): 024-031
DOI: 10.1055/s-0038-1627468
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Imaging Criteria in Neuro-oncology

Martha Nowosielski
1   Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
2   Department of Neurology and Neurooncology, University Medical Center, German Cancer Research Center (DKFZ), Heidelberg, Germany
,
Patrick Y. Wen
2   Department of Neurology and Neurooncology, University Medical Center, German Cancer Research Center (DKFZ), Heidelberg, Germany
3   Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
4   Division of Cancer Neurology, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts
5   Department of Neurology, Harvard Medical School, Boston, Massachusetts
› Author Affiliations
Further Information

Publication History

Publication Date:
16 March 2018 (online)

    Permissions and Reprints

Abstract

The identification of more effective therapies for brain tumors has been limited in part by the lack of reliable criteria for determining response and progression. Since its introduction in 1990, the MacDonald criteria have been used in neuro-oncology clinical trials to determine response, but they fail to address issues such as pseudoprogression, pseudoresponse, and nonenhancing tumor progression that have arisen with more recent therapies. The Response Assessment in Neuro-Oncology (RANO) working group, a multidisciplinary international group consisting of neuro-oncologists, medical oncologists, neuroradiologists, neurosurgeons, radiation oncologists, and neuropsychologists, was formed to improve response assessment and clinical trial endpoints in neuro-oncology. Although it was initially focused on response assessment for gliomas, the scope of the RANO group has been broadened to include brain metastases, leptomeningeal metastases, spine tumors, pediatric brain tumors, and meningiomas. In addition, subgroups have focused on response assessment during immunotherapy and use of positron emission tomography, as well as determination of neurologic function, clinical outcomes assessment, and seizures. The RANO criteria are currently a collective work in progress, and refinements will be needed in the future based on data from clinical trials and improved imaging techniques.

Keywords

response assessment - RANO - brain tumors - glioma
 

  • References

  • 1 Wöhrer A, Waldhör T, Heinzl H. , et al. The Austrian Brain Tumour Registry: a cooperative way to establish a population-based brain tumour registry. J Neurooncol 2009; 95 (03) 401-411
    CrossrefPubMedGoogle Scholar
  • 2 Ostrom QT, Gittleman H, Xu J. , et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2009-2013. Neuro-oncol 2016; 18 (Suppl. 05) v1-v75
    PubMedGoogle Scholar
  • 3 Davis FG, Dolecek TA, McCarthy BJ, Villano JL. Toward determining the lifetime occurrence of metastatic brain tumors estimated from 2007 United States cancer incidence data. Neuro-oncol 2012; 14 (09) 1171-1177
    PubMedGoogle Scholar
  • 4 Arvold ND, Lee EQ, Mehta MP. , et al. Updates in the management of brain metastases. Neuro-oncol 2016; 18 (08) 1043-1065
    PubMedGoogle Scholar
  • 5 Stupp R, Mason WP, van den Bent MJ. , et al; European Organisation for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups; National Cancer Institute of Canada Clinical Trials Group. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005; 352 (10) 987-996
    CrossrefPubMedGoogle Scholar
  • 6 Ballman KV, Buckner JC, Brown PD. , et al. The relationship between six-month progression-free survival and 12-month overall survival end points for phase II trials in patients with glioblastoma multiforme. Neuro-oncol 2007; 9 (01) 29-38
    PubMedGoogle Scholar
  • 7 Lamborn KR, Yung WK, Chang SM. , et al; North American Brain Tumor Consortium. Progression-free survival: an important end point in evaluating therapy for recurrent high-grade gliomas. Neuro-oncol 2008; 10 (02) 162-170
    PubMedGoogle Scholar
  • 8 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
    CrossrefPubMedGoogle Scholar
  • 9 Wen PY, Chang SM, Van den Bent MJ, Vogelbaum MA, Macdonald DR, Lee EQ. Response Assessment in Neuro-Oncology Clinical Trials. J Clin Oncol 2017; 35 (21) 2439-2449
    CrossrefPubMedGoogle Scholar
  • 10 Macdonald DR, Cascino TL, Schold Jr SC, Cairncross JG. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990; 8 (07) 1277-1280
    CrossrefPubMedGoogle Scholar
  • 11 van den Bent MJ, Vogelbaum MA, Wen PY, Macdonald DR, Chang SM. End point assessment in gliomas: novel treatments limit usefulness of classical Macdonald's Criteria. J Clin Oncol 2009; 27 (18) 2905-2908
    CrossrefPubMedGoogle Scholar
  • 12 Sorensen AG, Batchelor TT, Wen PY, Zhang WT, Jain RK. Response criteria for glioma. Nat Clin Pract Oncol 2008; 5 (11) 634-644
    CrossrefPubMedGoogle Scholar
  • 13 Chang S, Wen P, Vogelbaum M. , et al. Response Assessment in Neuro-Oncology (RANO): more than imaging criteria for malignant glioma. Neuro-Oncology Practice 2015; 2: 205-209
    PubMedGoogle Scholar
  • 14 Eisele SC, Wen PY, Lee EQ. Assessment of brain tumor response: RANO and its offspring. Curr Treat Options Oncol 2016; 17 (07) 35
    CrossrefPubMedGoogle Scholar
  • 15 Ulmer S, Braga TA, Barker II FG, Lev MH, Gonzalez RG, Henson JW. Clinical and radiographic features of peritumoral infarction following resection of glioblastoma. Neurology 2006; 67 (09) 1668-1670
    CrossrefPubMedGoogle Scholar
  • 16 Rheims S, Ricard D, van den Bent M. , et al. Peri-ictal pseudoprogression in patients with brain tumor. Neuro-oncol 2011; 13 (07) 775-782
    PubMedGoogle Scholar
  • 17 Cairncross JG, Pexman JH, Rathbone MP, DelMaestro RF. Postoperative contrast enhancement in patients with brain tumor. Ann Neurol 1985; 17 (06) 570-572
    CrossrefPubMedGoogle Scholar
  • 18 Sheline GE, Wara WM, Smith V. Therapeutic irradiation and brain injury. Int J Radiat Oncol Biol Phys 1980; 6 (09) 1215-1228
    CrossrefPubMedGoogle Scholar
  • 19 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 (05) 453-461
    CrossrefPubMedGoogle Scholar
  • 20 Watling CJ, Lee DH, Macdonald DR, Cairncross JG. Corticosteroid-induced magnetic resonance imaging changes in patients with recurrent malignant glioma. J Clin Oncol 1994; 12 (09) 1886-1889
    CrossrefPubMedGoogle Scholar
  • 21 Batchelor TT, Sorensen AG, di Tomaso E. , et al. AZD2171, a pan-VEGF receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell 2007; 11 (01) 83-95
    CrossrefPubMedGoogle Scholar
  • 22 van den Bent MJ, Brandes AA, Rampling R. , et al. Randomized phase II trial of erlotinib versus temozolomide or carmustine in recurrent glioblastoma: EORTC brain tumor group study 26034. J Clin Oncol 2009; 27 (08) 1268-1274
    CrossrefPubMedGoogle Scholar
  • 23 Gerstner ER, McNamara MB, Norden AD, Lafrankie D, Wen PY. Effect of adding temozolomide to radiation therapy on the incidence of pseudo-progression. J Neurooncol 2009; 94 (01) 97-101
    CrossrefPubMedGoogle Scholar
  • 24 Taal W, Brandsma D, de Bruin HG. , et al. Incidence of early pseudo-progression in a cohort of malignant glioma patients treated with chemoirradiation with temozolomide. Cancer 2008; 113 (02) 405-410
    CrossrefPubMedGoogle Scholar
  • 25 Radbruch A, Fladt J, Kickingereder P. , et al. Pseudoprogression in patients with glioblastoma: clinical relevance despite low incidence. Neuro-oncol 2015; 17 (01) 151-159
    PubMedGoogle Scholar
  • 26 Brandes AA, 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 (13) 2192-2197
    CrossrefPubMedGoogle Scholar
  • 27 Okada H, Weller M, Huang R. , et al. Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. Lancet Oncol 2015; 16 (15) e534-e542
    CrossrefPubMedGoogle Scholar
  • 28 van West SE, de Bruin HG, van de Langerijt B, Swaak-Kragten AT, van den Bent MJ, Taal W. Incidence of pseudoprogression in low-grade gliomas treated with radiotherapy. Neuro-oncol 2017; 19 (05) 719-725
    PubMedGoogle Scholar
  • 29 Chinot OL, Wick W, Mason W. , et al. Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. N Engl J Med 2014; 370 (08) 709-722
    CrossrefPubMedGoogle Scholar
  • 30 Gilbert MR, Sulman EP, Mehta MP. Bevacizumab for newly diagnosed glioblastoma. N Engl J Med 2014; 370 (21) 2048-2049
    CrossrefPubMedGoogle Scholar
  • 31 Taal W, Oosterkamp HM, Walenkamp AM. , 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 (09) 943-953
    CrossrefPubMedGoogle Scholar
  • 32 Wick W, Gorlia T, Bendszus M. , et al. Lomustine and Bevacizumab in Progressive Glioblastoma. N Engl J Med 2017; 377 (20) 1954-1963
    CrossrefPubMedGoogle Scholar
  • 33 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
    CrossrefPubMedGoogle Scholar
  • 34 Nowosielski M, Ellingson BM, Chinot OL. , et al. Radiologic progression of glioblastoma under therapy – an exploratory analysis of AVAglio. Neuro Oncol 2017; DOI: 10.1093/neuonc/nox162.
    CrossrefPubMedGoogle Scholar
  • 35 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 (08) 1258-1260
    CrossrefPubMedGoogle Scholar
  • 36 Wick A, Dörner N, Schäfer N. , et al. Bevacizumab does not increase the risk of remote relapse in malignant glioma. Ann Neurol 2011; 69 (03) 586-592
    CrossrefPubMedGoogle Scholar
  • 37 Wick W, Stupp R, Beule AC. , et al; European Organisation for Research and Treatment of Cancer and the National Cancer Institute of Canada Clinical Trials Group. A novel tool to analyze MRI recurrence patterns in glioblastoma. Neuro-oncol 2008; 10 (06) 1019-1024
    PubMedGoogle Scholar
  • 38 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
    CrossrefPubMedGoogle Scholar
  • 39 Chamberlain M, Junck L, Brandsma D. , et al. Leptomeningeal metastases: a RANO proposal for response criteria. Neuro-oncol 2017; 19 (04) 484-492
    PubMedGoogle Scholar
  • 40 Lin NU, Lee EQ, Aoyama H. , et al; Response Assessment in Neuro-Oncology (RANO) group. Response assessment criteria for brain metastases: proposal from the RANO group. Lancet Oncol 2015; 16 (06) e270-e278
    CrossrefPubMedGoogle Scholar
  • 41 van den Bent MJ, Wefel JS, Schiff D. , et al. Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas. Lancet Oncol 2011; 12 (06) 583-593
    CrossrefPubMedGoogle Scholar
  • 42 Warren KE, Vezina G, Poussaint TY. , et al. Response assessment in medulloblastoma and leptomeningeal seeding tumors: recommendations from the Response Assessment in Pediatric Neuro-Oncology Committee. Neuro-oncol 2018; 20 (01) 12-23
    PubMedGoogle Scholar
  • 43 Thibault I, Chang EL, Sheehan J. , et al. Response assessment after stereotactic body radiotherapy for spinal metastasis: a report from the SPIne response assessment in Neuro-Oncology (SPINO) group. Lancet Oncol 2015; 16 (16) e595-e603
    CrossrefPubMedGoogle Scholar
  • 44 Kaley T, Barani I, Chamberlain M. , et al. Historical benchmarks for medical therapy trials in surgery- and radiation-refractory meningioma: a RANO review. Neuro-oncol 2014; 16 (06) 829-840
    PubMedGoogle Scholar
  • 45 Blakeley JO, Coons SJ, Corboy JR, Kline Leidy N, Mendoza TR, Wefel JS. Clinical outcome assessment in malignant glioma trials: measuring signs, symptoms, and functional limitations. Neuro-oncol 2016; 18 (Suppl. 02) ii13-ii20
    PubMedGoogle Scholar
  • 46 Peyre M, Cartalat-Carel S, Meyronet D. , et al. Prolonged response without prolonged chemotherapy: a lesson from PCV chemotherapy in low-grade gliomas. Neuro-oncol 2010; 12 (10) 1078-1082
    PubMedGoogle Scholar
  • 47 Roelcke U, Wyss MT, Nowosielski M. , et al. Amino acid positron emission tomography to monitor chemotherapy response and predict seizure control and progression-free survival in WHO grade II gliomas. Neuro-oncol 2016; 18 (05) 744-751
    PubMedGoogle Scholar
  • 48 Buckner JC, Chakravarti A, Curran Jr WJ. Radiation plus chemotherapy in low-grade glioma. N Engl J Med 2016; 375 (05) 490-491
    PubMedGoogle Scholar
  • 49 Weller M, van den Bent M, Tonn JC. , et al; European Association for Neuro-Oncology (EANO) Task Force on Gliomas. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol 2017; 18 (06) e315-e329
    CrossrefPubMedGoogle Scholar
  • 50 Nayak L, DeAngelis LM, Brandes AA. , et al. The Neurologic Assessment in Neuro-Oncology (NANO) scale: a tool to assess neurologic function for integration into the Response Assessment in Neuro-Oncology (RANO) criteria. Neuro-oncol 2017; 19 (05) 625-635
    PubMedGoogle Scholar
  • 51 Koekkoek JA, Kerkhof M, Dirven L, Heimans JJ, Reijneveld JC, Taphoorn MJ. Seizure outcome after radiotherapy and chemotherapy in low-grade glioma patients: a systematic review. Neuro-oncol 2015; 17 (07) 924-934
    PubMedGoogle Scholar
  • 52 Avila EK, Chamberlain M, Schiff D. , et al. Seizure control as a new metric in assessing efficacy of tumor treatment in low-grade glioma trials. Neuro-oncol 2017; 19 (01) 12-21
    PubMedGoogle Scholar
  • 53 Huang RY, Neagu MR, Reardon DA, Wen PY. Pitfalls in the neuroimaging of glioblastoma in the era of antiangiogenic and immuno/targeted therapy - detecting illusive disease, defining response. Front Neurol 2015; 6: 33
    PubMedGoogle Scholar
  • 54 Shah GD, Kesari S, Xu R. , et al. Comparison of linear and volumetric criteria in assessing tumor response in adult high-grade gliomas. Neuro-oncol 2006; 8 (01) 38-46
    PubMedGoogle Scholar
  • 55 Galanis E, Buckner JC, Maurer MJ. , et al. Validation of neuroradiologic response assessment in gliomas: measurement by RECIST, two-dimensional, computer-assisted tumor area, and computer-assisted tumor volume methods. Neuro-oncol 2006; 8 (02) 156-165
    PubMedGoogle Scholar
  • 56 Bauknecht HC, Romano VC, Rogalla P. , et al. Intra- and interobserver variability of linear and volumetric measurements of brain metastases using contrast-enhanced magnetic resonance imaging. Invest Radiol 2010; 45 (01) 49-56
    CrossrefPubMedGoogle Scholar
  • 57 Shiroishi MS, Boxerman JL, Pope WB. Physiologic MRI for assessment of response to therapy and prognosis in glioblastoma. Neuro-oncol 2016; 18 (04) 467-478
    PubMedGoogle Scholar
  • 58 Pope WB, Kim HJ, Huo J. , et al. Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009; 252 (01) 182-189
    CrossrefPubMedGoogle Scholar
  • 59 Bulik M, Kazda T, Slampa P, Jancalek R. The diagnostic ability of follow-up imaging biomarkers after treatment of glioblastoma in the temozolomide era: implications from proton MR spectroscopy and apparent diffusion coefficient mapping. BioMed Res Int 2015; 2015: 641023
    PubMedGoogle Scholar
  • 60 Zhang H, Ma L, Wang Q, Zheng X, Wu C, Xu BN. Role of magnetic resonance spectroscopy for the differentiation of recurrent glioma from radiation necrosis: a systematic review and meta-analysis. Eur J Radiol 2014; 83 (12) 2181-2189
    CrossrefPubMedGoogle Scholar
  • 61 Boxerman JL, Ellingson BM, Jeyapalan S. , et al. Longitudinal DSC-MRI for distinguishing tumor recurrence from pseudoprogression in patients with a high-grade glioma. Am J Clin Oncol 2017; 40 (03) 228-234
    CrossrefPubMedGoogle Scholar
  • 62 Price SJ, Green HA, Dean AF, Joseph J, Hutchinson PJ, Gillard JH. Correlation of MR relative cerebral blood volume measurements with cellular density and proliferation in high-grade gliomas: an image-guided biopsy study. AJNR Am J Neuroradiol 2011; 32 (03) 501-506
    CrossrefPubMedGoogle Scholar
  • 63 Usinskiene J, Ulyte A, Bjørnerud A. , et al. Optimal differentiation of high- and low-grade glioma and metastasis: a meta-analysis of perfusion, diffusion, and spectroscopy metrics. Neuroradiology 2016; 58 (04) 339-350
    CrossrefPubMedGoogle Scholar
  • 64 Hyare H, Thust S, Rees J. Advanced MRI techniques in the monitoring of treatment of gliomas. Curr Treat Options Neurol 2017; 19 (03) 11
    CrossrefPubMedGoogle Scholar
  • 65 Albert NL, Weller M, Suchorska B. , et al. Response Assessment in Neuro-Oncology working group and European Association for Neuro-Oncology recommendations for the clinical use of PET imaging in gliomas. Neuro-oncol 2016; 18 (09) 1199-1208
    PubMedGoogle Scholar
  • 66 Langen KJ, Galldiks N, Hattingen E, Shah NJ. Advances in neuro-oncology imaging. Nat Rev Neurol 2017; 13 (05) 279-289
    CrossrefPubMedGoogle Scholar
  • 67 Kebir S, Rauschenbach L, Galldiks N. , et al. Dynamic O-(2-[18F]fluoroethyl)-L-tyrosine PET imaging for the detection of checkpoint inhibitor-related pseudoprogression in melanoma brain metastases. Neuro-oncol 2016; 18 (10) 1462-1464
    PubMedGoogle Scholar
  • 68 Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278 (02) 563-577
    CrossrefPubMedGoogle Scholar
  • 69 Kickingereder P, Götz M, Muschelli J. , 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) 5765-5771
    CrossrefPubMedGoogle Scholar
  • 70 Kickingereder P, Burth S, Wick A. , 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 (03) 880-889
    CrossrefPubMedGoogle Scholar
  • 71 Wen PY, Cloughesy TF, Ellingson BM. , et al. Report of the Jumpstarting Brain Tumor Drug Development Coalition and FDA clinical trials neuroimaging endpoint workshop (January 30, 2014, Bethesda MD). Neuro-oncol 2014; 16 (Suppl. 07) vii36-vii47
    PubMedGoogle Scholar
  • 72 Ellingson BM, Bendszus M, Boxerman J. , et al; Jumpstarting Brain Tumor Drug Development Coalition Imaging Standardization Steering Committee. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro-oncol 2015; 17 (09) 1188-1198
    PubMedGoogle Scholar
  • 73 Wolchok JD, Hoos A, O'Day S. , et al. Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin Cancer Res 2009; 15 (23) 7412-7420
    CrossrefPubMedGoogle Scholar
  • 74 Nowosielski M, Recheis W, Goebel G. , et al. ADC histograms predict response to anti-angiogenic therapy in patients with recurrent high-grade glioma. Neuroradiology 2011; 53 (04) 291-302
    CrossrefPubMedGoogle Scholar
  • 75 Hutterer M, Nowosielski M, Putzer D. , et al. O-(2-18F-fluoroethyl)-L-tyrosine PET predicts failure of antiangiogenic treatment in patients with recurrent high-grade glioma. J Nucl Med 2011; 52 (06) 856-864
    CrossrefPubMedGoogle Scholar