Tumor recurrence or treatment-related changes following chemoradiation in patients with glioblastoma: does pathology predict outcomes?



Despite surgical resection and chemoradiation, all patients with GBM invariably recur. Radiological imaging is limited in differentiating tumor recurrence (TR) from treatment-related changes (TRC); therefore, re-resection is often needed. Few studies have assessed the relationship between re-resection histopathology and overall survival (OS). We performed a large retrospective study to analyze the clinical significance of histopathology following re-resection and its influence on genomic sequencing results.


Clinical, radiographic, and histological information was compiled from 675 patients with GBM (2005–2017). 137-patients met the inclusion criteria. IDH1 p.R132H immunohistochemistry was performed in all patients. Next-generation sequencing interrogating 205 tumor-related genes was performed in 68-patients. Molecular alterations from initial and subsequent resections were compared in a subset of cases.


There were no differences in OS (17.3-months TRC vs. 21-months TR, p = 0.881) and survival from progression (9.0 vs. 11.7-months, p = 0.778) between patients with TR and TRC on re-resection. TR patients were more likely to receive salvage radiotherapy (26% vs. 0%) and tumor-treating fields (25% vs. 5%,) after the 2nd surgery than the TRC group (p = < 0.045). There was no correlation between mutations and TRC. IDH status was not predictive of TRC. Fifteen-patients had sequencing results from multiple surgeries without evident differences in genomic alterations.


Histopathologic findings following chemoradiation do not correlate with clinical outcomes. Such findings should be considered during patient management and clinical trial enrollment. Standardization of tissue sampling and interpretation following reoperation is urgently needed. Future work is required to understand the relationship between the mutation profile following TRC and outcomes.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

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  1. 1.

    Omuro A, DeAngelis LM (2013) Glioblastoma and other malignant gliomas: a clinical review. JAMA 310:1842–1850. https://doi.org/10.1001/jama.2013.280319

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Delgado-Lopez PD, Corrales-Garcia EM (2016) Survival in glioblastoma: a review on the impact of treatment modalities. Clin Transl Oncol 18:1062–1071. https://doi.org/10.1007/s12094-016-1497-x

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Chen L, Chaichana KL, Kleinberg L, Ye X, Quinones-Hinojosa A, Redmond K (2015) Glioblastoma recurrence patterns near neural stem cell regions. Radiother Oncol 116:294–300. https://doi.org/10.1016/j.radonc.2015.07.032

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Ali FS, Arevalo O, Zorofchian S, Patrizz A, Riascos R, Tandon N, Blanco A, Ballester LY, Esquenazi Y (2019) Cerebral radiation necrosis: incidence, pathogenesis, diagnostic challenges, and future opportunities. Curr Oncol Rep. https://doi.org/10.1007/s11912-019-0818-y

    Article  PubMed  Google Scholar 

  5. 5.

    Kruser TJ, Mehta MP, Robins HI (2013) Pseudoprogression after glioma therapy: a comprehensive review. Expert Rev Neurother 13:389–403. https://doi.org/10.1586/ern.13.7

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Chamberlain MC, Glantz MJ, Chalmers L, Van Horn A, Sloan AE (2007) Early necrosis following concurrent Temodar and radiotherapy in patients with glioblastoma. J Neurooncol 82:81–83. https://doi.org/10.1007/s11060-006-9241-y

    Article  PubMed  Google Scholar 

  7. 7.

    Topkan E, Topuk S, Oymak E, Parlak C, Pehlivan B (2012) Pseudoprogression in patients with glioblastoma multiforme after concurrent radiotherapy and temozolomide. Am J Clin Oncol 35:284–289. https://doi.org/10.1097/COC.0b013e318210f54a

    Article  PubMed  Google Scholar 

  8. 8.

    Tihan T, Barletta J, Parney I, Lamborn K, Sneed PK, Chang S (2006) Prognostic value of detecting recurrent glioblastoma multiforme in surgical specimens from patients after radiotherapy: should pathology evaluation alter treatment decisions? Hum Pathol 37:272–282

    Article  Google Scholar 

  9. 9.

    Woodworth GF, Garzon-Muvdi T, Ye X, Blakeley JO, Weingart JD, Burger PC (2013) Histopathological correlates with survival in reoperated glioblastomas. J Neurooncol 113:485–493. https://doi.org/10.1007/s11060-013-1141-3

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG (2009) Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42:377–381. https://doi.org/10.1016/j.jbi.2008.08.010

    Article  PubMed  Google Scholar 

  11. 11.

    Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J et al (2019) The REDCap consortium: building an international community of software platform partners. J Biomed Inform 95:103208

    Article  Google Scholar 

  12. 12.

    Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization Classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820. https://doi.org/10.1007/s00401-016-1545-1

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Zorofchian S, El-Achi H, Yan Y, Esquenazi Y, Ballester LY (2018) Characterization of genomic alterations in primary central nervous system lymphomas. J Neurooncol 140:509–517. https://doi.org/10.1007/s11060-018-2990-6

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, Schnall-Levin M, White J, Sanford EM, An P et al (2013) Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol 31:1023–1031. https://doi.org/10.1038/nbt.2696

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Schwaederle M, Krishnamurthy N, Daniels GA, Piccioni DE, Kesari S, Fanta PT, Schwab RB, Patel SP, Parker BA, Kurzrock R (2018) Telomerase reverse transcriptase promoter alterations across cancer types as detected by next-generation sequencing: a clinical and molecular analysis of 423 patients. Cancer 124:1288–1296. https://doi.org/10.1002/cncr.31175

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Kanda Y (2013) Investigation of the freely available easy-to-use software “EZR” for medical statistics. Bone Marrow Transplant 48:452–458. https://doi.org/10.1038/bmt.2012.244

    CAS  Article  Google Scholar 

  17. 17.

    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol) 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    Article  Google Scholar 

  18. 18.

    Chukwueke UN, Wen PY (2019) Use of the Response Assessment in Neuro-Oncology (RANO) criteria in clinical trials and clinical practice. CNS Oncol. https://doi.org/10.2217/cns-2018-0007

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Kim JH, Bae Kim Y, Han JH, Cho KG, Kim SH, Sheen SS, Lee HW, Jeong SY, Kim BY, Lee KB (2012) Pathologic diagnosis of recurrent glioblastoma: morphologic, immunohistochemical, and molecular analysis of 20 paired cases. Am J Surg Pathol 36:620–628. https://doi.org/10.1097/PAS.0b013e318246040c

    Article  PubMed  Google Scholar 

  20. 20.

    Haider AS, van den Bent M, Wen PY, Vogelbaum MA, Chang S, Canoll PD, Horbinski CM, Huse JT (2020) Toward a standard pathological and molecular characterization of recurrent glioma in adults: a Response Assessment in Neuro-Oncology effort. Neuro Oncol 22:450–456. https://doi.org/10.1093/neuonc/noz233

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Rusthoven KE, Olsen C, Franklin W, Kleinschmidt-DeMasters BK, Kavanagh BD, Gaspar LE, Lillehei K, Waziri A, Damek DM, Chen C (2011) Favorable prognosis in patients with high-grade glioma with radiation necrosis: the University of Colorado reoperation series. Int J Radiat Oncol Biol Phys 81:211–217. https://doi.org/10.1016/j.ijrobp.2010.04.069

    Article  PubMed  Google Scholar 

  22. 22.

    Grossman R, Shimony N, Hadelsberg U, Soffer D, Sitt R, Strauss N, Corn BW, Ram Z (2016) Impact of resecting radiation necrosis and pseudoprogression on survival of patients with glioblastoma. World Neurosurg 89:37–41. https://doi.org/10.1016/j.wneu.2016.01.020

    Article  PubMed  Google Scholar 

  23. 23.

    Dalle Ore CL, Chandra A, Rick J, Lau D, Shahin M, Nguyen AT, McDermott M, Berger MS, Aghi MK (2019) Presence of histopathological treatment effects at resection of recurrent glioblastoma: incidence and effect on outcome. Neurosurgery 85:793–800. https://doi.org/10.1093/neuros/nyy501

    Article  PubMed  Google Scholar 

  24. 24.

    Holdhoff M, Ye X, Piotrowski AF, Strowd RE, Seopaul S, Lu Y, Barker NJ, Sivakumar A, Rodriguez FJ, Grossman SA et al (2019) The consistency of neuropathological diagnoses in patients undergoing surgery for suspected recurrence of glioblastoma. J Neurooncol 141:347–354. https://doi.org/10.1007/s11060-018-03037-3

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Jain R (2011) Perfusion CT imaging of brain tumors: an overview. AJNR Am J Neuroradiol 32:1570–1577. https://doi.org/10.3174/ajnr.A2263

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Metaweh NAK, Azab AO, El Basmy AAH, Mashhour KN, El Mahdy WM (2018) Contrast-enhanced perfusion MR imaging to differentiate between recurrent/residual brain neoplasms and radiation necrosis. Asian Pac J Cancer Prev 19:941–948

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Xu Q, Liu Q, Ge H, Ge X, Wu J, Qu J, Xu K (2017) Tumor recurrence versus treatment effects in glioma: a comparative study of three dimensional pseudo-continuous arterial spin labeling and dynamic susceptibility contrast imaging. Medicine (Baltimore) 96:e9332. https://doi.org/10.1097/MD.0000000000009332

    Article  Google Scholar 

  28. 28.

    Brandes AA, Franceschi E, Tosoni A, Blatt V, Pession A, Tallini G, Bertorelle R, Bartolini S, Calbucci F, Andreoli A et al (2008) MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients. J Clin Oncol 26:2192–2197. https://doi.org/10.1200/JCO.2007.14.8163

    Article  PubMed  Google Scholar 

  29. 29.

    McConnell HL, Schwartz DL, Richardson BE, Woltjer RL, Muldoon LL, Neuwelt EA (2016) Ferumoxytol nanoparticle uptake in brain during acute neuroinflammation is cell-specific. Nanomedicine 12:1535–1542. https://doi.org/10.1016/j.nano.2016.03.009

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Henderson F Jr, Brem S, O’Rourke DM, Nasrallah M, Buch VP, Young AJ, Doot RK, Pantel A, Desai A, Bagley SJ et al (2020) (18)F-Fluciclovine PET to distinguish treatment-related effects from disease progression in recurrent glioblastoma: PET fusion with MRI guides neurosurgical sampling. Neurooncol Pract 7:152–157. https://doi.org/10.1093/nop/npz068

    Article  PubMed  Google Scholar 

  31. 31.

    Soler DC, Young AB, Cooper KD, Kerstetter-Fogle A, Barnholtz-Sloan JS, Gittleman H, McCormick TS, Sloan AE (2017) The ratio of HLA-DR and VNN2(+) expression on CD14(+) myeloid derived suppressor cells can distinguish glioblastoma from radiation necrosis patients. J Neurooncol 134:189–196. https://doi.org/10.1007/s11060-017-2508-7

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Sabari JK, Offin M, Stephens D, Ni A, Lee A, Pavlakis N, Clarke S, Diakos CI, Datta S, Tandon N et al (2019) A Prospective Study Of Circulating Tumor DNA to guide matched targeted therapy in lung cancers. J Natl Cancer Inst 111:575–583. https://doi.org/10.1093/jnci/djy156

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Ma J, Benitez JA, Li J, Miki S, Ponte de Albuquerque C, Galatro T, Orellana L, Zanca C, Reed R, Boyer A et al (2019) Inhibition of nuclear PTEN tyrosine phosphorylation enhances glioma radiation sensitivity through attenuated DNA repair. Cancer Cell 35:504.e7-518.e7

    Article  Google Scholar 

  34. 34.

    Cesare AJ, Reddel RR (2010) Alternative lengthening of telomeres: models, mechanisms and implications. Nat Rev Genet 11:319–330. https://doi.org/10.1038/nrg2763

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Smogorzewska A, de Lange T (2004) Regulation of telomerase by telomeric proteins. Annu Rev Biochem 73:177–208. https://doi.org/10.1146/annurev.biochem.73.071403.160049

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Shay JW, Wright WE (2011) Role of telomeres and telomerase in cancer. Semin Cancer Biol 21:349–353. https://doi.org/10.1016/j.semcancer.2011.10.001

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Gao K, Li G, Qu Y, Wang M, Cui B, Ji M, Shi B, Hou P (2016) TERT promoter mutations and long telomere length predict poor survival and radiotherapy resistance in gliomas. Oncotarget 7:8712–8725. https://doi.org/10.18632/oncotarget.6007

    Article  PubMed  Google Scholar 

  38. 38.

    Khurana R, Rath S, Singh HB, Rastogi M, Nanda SS, Chauhan A, Kaif M, Hussain N (2020) Correlation of molecular markers in high grade gliomas with response to chemo-radiation. Asian Pac J Cancer Prev 21:755–760

    CAS  Article  Google Scholar 

  39. 39.

    Yao Q, Cai G, Yu Q, Shen J, Gu Z, Chen J, Shi W, Shi J (2018) IDH1 mutation diminishes aggressive phenotype in glioma stem cells. Int J Oncol 52:270–278. https://doi.org/10.3892/ijo.2017.4186

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Christians A, Adel-Horowski A, Banan R, Lehmann U, Bartels S, Behling F, Barrantes-Freer A, Stadelmann C, Rohde V, Stockhammer F et al (2019) The prognostic role of IDH mutations in homogeneously treated patients with anaplastic astrocytomas and glioblastomas. Acta Neuropathol Commun. https://doi.org/10.1186/s40478-019-0817-0

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Rao AM, Quddusi A, Shamim MS (2018) The significance of MGMT methylation in Glioblastoma Multiforme prognosis. J Pak Med Assoc 68:1137–1139

    PubMed  Google Scholar 

  42. 42.

    Binabaj MM, Bahrami A, ShahidSales S, Joodi M, Joudi Mashhad M, Hassanian SM, Anvari K, Avan A (2018) The prognostic value of MGMT promoter methylation in glioblastoma: a meta-analysis of clinical trials. J Cell Physiol 233:378–386. https://doi.org/10.1002/jcp.25896

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Barthel FP, Johnson KC, Varn FS, Moskalik AD, Tanner G, Kocakavuk E, Anderson KJ, Abiola O, Aldape K, Alfaro KD et al (2019) Longitudinal molecular trajectories of diffuse glioma in adults. Nature 576:112–120. https://doi.org/10.1038/s41586-019-1775-1

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Alter BP (2002) Radiosensitivity in Fanconi’s anemia patients. Radiother Oncol 62:345–347

    Article  Google Scholar 

  45. 45.

    Taylor AM, Harnden DG, Arlett CF, Harcourt SA, Lehmann AR, Stevens S, Bridges BA (1975) Ataxia telangiectasia: a human mutation with abnormal radiation sensitivity. Nature 258:427–429. https://doi.org/10.1038/258427a0

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Wang TM, Shen GP, Chen MY, Zhang JB, Sun Y, He J, Xue WQ, Li XZ, Huang SY, Zheng XH et al (2019) Genome-wide association study of susceptibility loci for radiation-induced brain injury. J Natl Cancer Inst 111:620–628. https://doi.org/10.1093/jnci/djy150

    CAS  Article  PubMed  Google Scholar 

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Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number K08CA241651 (LYB). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Study design: AP, LYB, and YE. Data Recollection: AP and AD. Data analysis: AD and PZ. Manuscript writing: AP, AD, and YE. Manuscript revision and editing: AP, AD, NT, LYB, and YE. Study Supervision: LYB and YE. Approved final manuscript: all authors.

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Correspondence to Leomar Y. Ballester or Yoshua Esquenazi.

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This retrospective study was approved by the institutional review board of The University of Texas Health Science Center at Houston and Memorial Hermann Hospital, Houston, TX following the 1964 Helsinki Declaration and its later amendments.

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Patrizz, A., Dono, A., Zhu, P. et al. Tumor recurrence or treatment-related changes following chemoradiation in patients with glioblastoma: does pathology predict outcomes?. J Neurooncol 152, 163–172 (2021). https://doi.org/10.1007/s11060-020-03690-7

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  • Glioblastoma
  • Tumor recurrence
  • Radiation necrosis
  • Radiotherapy
  • Treatment-related changes
  • Reoperation