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Journal of Neuro-Oncology

, Volume 134, Issue 2, pp 423–431 | Cite as

Elderly patients with newly diagnosed glioblastoma: can preoperative imaging descriptors improve the predictive power of a survival model?

  • Mina Park
  • Seung-Koo Lee
  • Jong Hee Chang
  • Seok-Gu Kang
  • Eui Hyun Kim
  • Se Hoon Kim
  • Mi Kyung Song
  • Bo Gyoung Ma
  • Sung Soo AhnEmail author
Clinical Study

Abstract

The purpose of this study was to identify independent prognostic factors among preoperative imaging features in elderly glioblastoma patients and to evaluate whether these imaging features, in addition to clinical features, could enhance the predictive power of survival models. This retrospective study included 108 patients ≥65 years of age with newly diagnosed glioblastoma. Preoperative clinical features (age and KPS), postoperative clinical features (extent of surgery and postoperative treatment), and preoperative MRI features were assessed. Univariate and multivariate cox proportional hazards regression analyses for overall survival were performed. The integrated area under the receiver operating characteristic curve (iAUC) was calculated to evaluate the added value of imaging features in the survival model. External validation was independently performed with 40 additional patients ≥65 years of age with newly diagnosed glioblastoma. Eloquent area involvement, multifocality, and ependymal involvement on preoperative MRI as well as clinical features including age, preoperative KPS, extent of resection, and postoperative treatment were significantly associated with overall survival on univariate Cox regression. On multivariate analysis, extent of resection and ependymal involvement were independently associated with overall survival and preoperative KPS showed borderline significance. The model with both preoperative clinical and imaging features showed improved prediction of overall survival compared to the model with preoperative clinical features (iAUC, 0.670 vs. 0.600, difference 0.066, 95% CI 0.021–0.121). Analysis of the validation set yielded similar results (iAUC, 0.790 vs. 0.670, difference 0.123, 95% CI 0.021–0.260), externally validating this observation. Preoperative imaging features, including eloquent area involvement, multifocality, and ependymal involvement, in addition to clinical features, can improve the predictive power for overall survival in elderly glioblastoma patients.

Keywords

Glioblastoma Magnetic resonance imaging Survival analysis Aged Prognosis 

Notes

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2014R1A1A1002716).

Compliance with ethical standards

Conflict of interest

None of the authors has a financial or personal relationship that could inappropriately influence the contents of this paper.

Supplementary material

11060_2017_2544_MOESM1_ESM.docx (21 kb)
Supplementary material 1 (DOCX 21 KB)
11060_2017_2544_MOESM2_ESM.tif (478 kb)
Supplementary material 2 (TIF 478 KB)
11060_2017_2544_MOESM3_ESM.tif (463 kb)
Supplementary material 3 (TIF 463 KB)

References

  1. 1.
    Ostrom QT, Gittleman H, Liao P, Rouse C, Chen Y, Dowling J, Wolinsky Y, Kruchko C, Barnholtz-Sloan J (2014) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007–2011. Neuro Oncol 16(Suppl 4):iv1–i63. doi: 10.1093/neuonc/nou223 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Curran WJ Jr, Scott CB, Horton J, Nelson JS, Weinstein AS, Fischbach AJ, Chang CH, Rotman M, Asbell SO, Krisch RE et al (1993) Recursive partitioning analysis of prognostic factors in three radiation therapy oncology group malignant glioma trials. J Natl Cancer Inst 85:704–710CrossRefPubMedGoogle Scholar
  3. 3.
    Lamborn KR, Chang SM, Prados MD (2004) Prognostic factors for survival of patients with glioblastoma: recursive partitioning analysis. Neuro Oncol 6:227–235. doi: 10.1215/S1152851703000620 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Chaichana KL, Pendleton C, Chambless L, Camara-Quintana J, Nathan JK, Hassam-Malani L, Li G, Harsh GRt, Thompson RC, Lim M, Quinones-Hinojosa A (2013) Multi-institutional validation of a preoperative scoring system which predicts survival for patients with glioblastoma. J Clin Neurosci 20:1422–1426. doi: 10.1016/j.jocn.2013.02.007 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Saraswathy S, Crawford FW, Lamborn KR, Pirzkall A, Chang S, Cha S, Nelson SJ (2009) Evaluation of MR markers that predict survival in patients with newly diagnosed GBM prior to adjuvant therapy. J Neurooncol 91:69–81. doi: 10.1007/s11060-008-9685-3 CrossRefPubMedGoogle Scholar
  6. 6.
    Burger PC, Green SB (1987) Patient age, histologic features, and length of survival in patients with glioblastoma multiforme. Cancer 59:1617–1625CrossRefPubMedGoogle Scholar
  7. 7.
    Coffey RJ, Lunsford LD (1987) Factors determining survival of patients with malignant gliomas diagnosed by stereotactic biopsy. Appl Neurophysiol 50:183–187PubMedGoogle Scholar
  8. 8.
    Jeremic B, Grujicic D, Antunovic V, Djuric L, Stojanovic M, Shibamoto Y (1994) Influence of extent of surgery and tumor location on treatment outcome of patients with glioblastoma multiforme treated with combined modality approach. J Neurooncol 21:177–185CrossRefPubMedGoogle Scholar
  9. 9.
    Wrensch M, Minn Y, Chew T, Bondy M, Berger MS (2002) Epidemiology of primary brain tumors: current concepts and review of the literature. Neuro Oncol 4:278–299PubMedPubMedCentralGoogle Scholar
  10. 10.
    Scott JG, Bauchet L, Fraum TJ, Nayak L, Cooper AR, Chao ST, Suh JH, Vogelbaum MA, Peereboom DM, Zouaoui S, Mathieu-Daude H, Fabbro-Peray P, Rigau V, Taillandier L, Abrey LE, DeAngelis LM, Shih JH, Iwamoto FM (2012) Recursive partitioning analysis of prognostic factors for glioblastoma patients aged 70 years or older. Cancer 118:5595–5600. doi: 10.1002/cncr.27570 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Oh SW, Jee TK, Kong DS, Nam DH, Lee JI, Seol HJ (2014) Outcome of conventional treatment and prognostic factor in elderly glioblastoma patients. Acta Neurochir (Wien) 156:641–651. doi: 10.1007/s00701-014-2020-1 CrossRefGoogle Scholar
  12. 12.
    Nguyen LT, Touch S, Nehme-Schuster H, Antoni D, Eav S, Clavier JB, Bauer N, Vigneron C, Schott R, Kehrli P, Noel G (2013) Outcomes in newly diagnosed elderly glioblastoma patients after concomitant temozolomide administration and hypofractionated radiotherapy. Cancers (Basel) 5:1177–1198. doi: 10.3390/cancers5031177 CrossRefGoogle Scholar
  13. 13.
    Hammoud MA, Sawaya R, Shi W, Thall PF, Leeds NE (1996) Prognostic significance of preoperative MRI scans in glioblastoma multiforme. J Neurooncol 27:65–73CrossRefPubMedGoogle Scholar
  14. 14.
    Zinn PO, Sathyan P, Mahajan B, Bruyere J, Hegi M, Majumder S, Colen RR (2012) A novel volume-age-KPS (VAK) glioblastoma classification identifies a prognostic cognate microRNA-gene signature. PLoS ONE 7:e41522. doi: 10.1371/journal.pone.0041522 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD Jr, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Mongkolwat P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ (2013) MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 267:560–569. doi: 10.1148/radiol.13120118 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Park JK, Hodges T, Arko L, Shen M, Dello Iacono D, McNabb A, Olsen Bailey N, Kreisl TN, Iwamoto FM, Sul J, Auh S, Park GE, Fine HA, Black PM (2010) Scale to predict survival after surgery for recurrent glioblastoma multiforme. J Clin Oncol 28:3838–3843. doi: 10.1200/JCO.2010.30.0582 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Lacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, Lang FF, McCutcheon IE, Hassenbusch SJ, Holland E, Hess K, Michael C, Miller D, Sawaya R (2001) A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 95:190–198. doi: 10.3171/jns.2001.95.2.0190 CrossRefPubMedGoogle Scholar
  18. 18.
    Pope WB, Sayre J, Perlina A, Villablanca JP, Mischel PS, Cloughesy TF (2005) MR imaging correlates of survival in patients with high-grade gliomas. AJNR Am J Neuroradiol 26:2466–2474PubMedGoogle Scholar
  19. 19.
    Mazurowski MA, Desjardins A, Malof JM (2013) Imaging descriptors improve the predictive power of survival models for glioblastoma patients. Neuro Oncol 15:1389–1394. doi: 10.1093/neuonc/nos335 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Wiki for the VASARI feature set The National Cancer Institute Web site. https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project
  21. 21.
    Wangaryattawanich P, Hatami M, Wang J, Thomas G, Flanders A, Kirby J, Wintermark M, Huang ES, Bakhtiari AS, Luedi MM, Hashmi SS, Rubin DL, Chen JY, Hwang SN, Freymann J, Holder CA, Zinn PO, Colen RR (2015) Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. Neuro Oncol 17:1525–1537. doi: 10.1093/neuonc/nov117 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefPubMedGoogle Scholar
  23. 23.
    Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723CrossRefGoogle Scholar
  24. 24.
    Iwamoto FM, Cooper AR, Reiner AS, Nayak L, Abrey LE (2009) Glioblastoma in the elderly: the Memorial Sloan-Kettering Cancer Center experience (1997–2007). Cancer 115:3758–3766. doi: 10.1002/cncr.24413 CrossRefPubMedGoogle Scholar
  25. 25.
    Nobusawa S, Watanabe T, Kleihues P, Ohgaki H (2009) IDH1 mutations as molecular signature and predictive factor of secondary glioblastomas. Clin Cancer Res 15:6002–6007. doi: 10.1158/1078-0432.CCR-09-0715 CrossRefPubMedGoogle Scholar
  26. 26.
    Perry JR, Laperriere N, O’Callaghan CJ, Brandes AA, Menten J, Phillips C, Fay M, Nishikawa R, Cairncross JG, Roa W, Osoba D, Rossiter JP, Sahgal A, Hirte H, Laigle-Donadey F, Franceschi E, Chinot O, Golfinopoulos V, Fariselli L, Wick A, Feuvret L, Back M, Tills M, Winch C, Baumert BG, Wick W, Ding K, Mason WP, Trial I (2017) Short-course radiation plus temozolomide in elderly patients with glioblastoma. N Engl J Med 376:1027–1037. doi: 10.1056/NEJMoa1611977 CrossRefPubMedGoogle Scholar
  27. 27.
    Wick W, Platten M, Meisner C, Felsberg J, Tabatabai G, Simon M, Nikkhah G, Papsdorf K, Steinbach JP, Sabel M, Combs SE, Vesper J, Braun C, Meixensberger J, Ketter R, Mayer-Steinacker R, Reifenberger G, Weller M, Society NOASGoN-oWGoGC (2012) Temozolomide chemotherapy alone versus radiotherapy alone for malignant astrocytoma in the elderly: the NOA-08 randomised, phase 3 trial. Lancet Oncol 13:707–715. doi: 10.1016/S1470-2045(12)70164-X CrossRefPubMedGoogle Scholar
  28. 28.
    Malmstrom A, Gronberg BH, Marosi C, Stupp R, Frappaz D, Schultz H, Abacioglu U, Tavelin B, Lhermitte B, Hegi ME, Rosell J, Henriksson R, Nordic Clinical Brain Tumour Study G (2012) Temozolomide versus standard 6-week radiotherapy versus hypofractionated radiotherapy in patients older than 60 years with glioblastoma: the Nordic randomised, phase 3 trial. Lancet Oncol 13:916–926. doi: 10.1016/S1470-2045(12)70265-6 CrossRefPubMedGoogle Scholar
  29. 29.
    Tanaka S, Meyer FB, Buckner JC, Uhm JH, Yan ES, Parney IF (2013) Presentation, management, and outcome of newly diagnosed glioblastoma in elderly patients. J Neurosurg 118:786–798. doi: 10.3171/2012.10.JNS112268 CrossRefPubMedGoogle Scholar
  30. 30.
    Yin AA, Cai S, Dong Y, Zhang LH, Liu BL, Cheng JX, Zhang X (2014) A meta-analysis of temozolomide versus radiotherapy in elderly glioblastoma patients. J Neurooncol 116:315–324. doi: 10.1007/s11060-013-1294-0 CrossRefPubMedGoogle Scholar
  31. 31.
    Sanai N, Alvarez-Buylla A, Berger MS (2005) Neural stem cells and the origin of gliomas. N Engl J Med 353:811–822. doi: 10.1056/NEJMra043666 CrossRefPubMedGoogle Scholar
  32. 32.
    Lim DA, Cha S, Mayo MC, Chen MH, Keles E, VandenBerg S, Berger MS (2007) Relationship of glioblastoma multiforme to neural stem cell regions predicts invasive and multifocal tumor phenotype. Neuro Oncol 9:424–429. doi: 10.1215/15228517-2007-023 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Gil-Perotin S, Marin-Husstege M, Li J, Soriano-Navarro M, Zindy F, Roussel MF, Garcia-Verdugo JM, Casaccia-Bonnefil P (2006) Loss of p53 induces changes in the behavior of subventricular zone cells: implication for the genesis of glial tumors. J Neurosci 26:1107–1116. doi: 10.1523/JNEUROSCI.3970-05.2006 CrossRefPubMedGoogle Scholar
  34. 34.
    Galli R, Binda E, Orfanelli U, Cipelletti B, Gritti A, De Vitis S, Fiocco R, Foroni C, Dimeco F, Vescovi A (2004) Isolation and characterization of tumorigenic, stem-like neural precursors from human glioblastoma. Cancer Res 64:7011–7021. doi: 10.1158/0008-5472.CAN-04-1364 CrossRefPubMedGoogle Scholar
  35. 35.
    Jafri NF, Clarke JL, Weinberg V, Barani IJ, Cha S (2013) Relationship of glioblastoma multiforme to the subventricular zone is associated with survival. Neuro Oncol 15:91–96. doi: 10.1093/neuonc/nos268 CrossRefPubMedGoogle Scholar
  36. 36.
    Chaichana K, Parker S, Olivi A, Quinones-Hinojosa A (2010) A proposed classification system that projects outcomes based on preoperative variables for adult patients with glioblastoma multiforme. J Neurosurg 112:997–1004. doi: 10.3171/2009.9.JNS09805 CrossRefPubMedGoogle Scholar
  37. 37.
    Adeberg S, Bostel T, Konig L, Welzel T, Debus J, Combs SE (2014) A comparison of long-term survivors and short-term survivors with glioblastoma, subventricular zone involvement: a predictive factor for survival? Radiat Oncol 9:95. doi: 10.1186/1748-717X-9-95 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Park CK, Kim JH, Nam DH, Kim CY, Chung SB, Kim YH, Seol HJ, Kim TM, Choi SH, Lee SH, Heo DS, Kim IH, Kim DG, Jung HW (2013) A practical scoring system to determine whether to proceed with surgical resection in recurrent glioblastoma. Neuro Oncol 15:1096–1101. doi: 10.1093/neuonc/not069 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Scott JG, Suh JH, Elson P, Barnett GH, Vogelbaum MA, Peereboom DM, Stevens GH, Elinzano H, Chao ST (2011) Aggressive treatment is appropriate for glioblastoma multiforme patients 70 years old or older: a retrospective review of 206 cases. Neuro Oncol 13:428–436. doi: 10.1093/neuonc/nor005 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Orringer D, Lau D, Khatri S, Zamora-Berridi GJ, Zhang K, Wu C, Chaudhary N, Sagher O (2012) Extent of resection in patients with glioblastoma: limiting factors, perception of resectability, and effect on survival. J Neurosurg 117:851–859. doi: 10.3171/2012.8.JNS12234 CrossRefPubMedGoogle Scholar
  41. 41.
    Babu R, Komisarow JM, Agarwal VJ, Rahimpour S, Iyer A, Britt D, Karikari IO, Grossi PM, Thomas S, Friedman AH, Adamson C (2016) Glioblastoma in the elderly: the effect of aggressive and modern therapies on survival. J Neurosurg 124:998–1007. doi: 10.3171/2015.4.JNS142200 CrossRefPubMedGoogle Scholar
  42. 42.
    Putz F, Knippen S, Lahmer G, Fietkau R, Semrau S (2015) A Model to Predict the Feasibility of Concurrent Chemoradiotherapy With Temozolomide in Glioblastoma Multiforme Patients Over Age 65. Am J Clin Oncol. doi: 10.1097/COC.0000000000000198 PubMedGoogle Scholar
  43. 43.
    Bauchet L, Zouaoui S, Darlix A, Menjot de Champfleur N, Ferreira E, Fabbro M, Kerr C, Taillandier L (2014) Assessment and treatment relevance in elderly glioblastoma patients. Neuro Oncol 16:1459–1468. doi: 10.1093/neuonc/nou063 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO, European Organisation for R, Treatment of Cancer Brain T, Radiotherapy G, National Cancer Institute of Canada Clinical Trials G (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352: 987–996 doi: 10.1056/NEJMoa043330 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Radiology, Research Institute of Radiological ScienceYonsei University College of MedicineSeoulSouth Korea
  2. 2.Department of Radiology, Konkuk University Medical CenterKonkuk University School of MedicineSeoulSouth Korea
  3. 3.Department of NeurosurgeryYonsei University College of MedicineSeoulSouth Korea
  4. 4.Department of PathologyYonsei University College of MedicineSeoulSouth Korea
  5. 5.Biostatistics Collaboration Unit, Department of Research AffairsYonsei University College of MedicineSeoulSouth Korea

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