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Volumetric segmentation of glioblastoma progression compared to bidimensional products and clinical radiological reports

  • Original Article - Tumor - Glioma
  • Published:
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Abstract

Background

Detection of progression is clinically important for the management of glioblastoma. We sought to assess the accuracy of clinical radiological reporting and measured bidimensional products to identify radiological glioblastoma progression. The two were compared to volumetric segmentation.

Methods

In this retrospective study, we included 106 patients with histopathologically verified glioblastomas and two separate MRI scans obtained before surgery. Bidimensional products based on measurements on the axial slice with the largest tumor area were calculated, and growth estimations from the clinical radiological reports were retrieved. The two growth estimations were compared to manual volumetric segmentations. Inter-observer agreement using the bidimensional product was assessed using Kappa-statistics and by calculating the difference between two neuroradiologist in percentage change of the bidimensional product for each tumor.

Results

Clinical radiological reports and bidimensional products showed fairly equal accuracy when compared to volumetric segmentation with an accuracy of 67% and 66–68%, respectively. There was a difference in median volume increase of 6.9 mL (2.4 vs 9.3 mL, p < 0.001) between tumors evaluated as stable and progressed based on the clinical radiological reports. This difference was 8.1 mL (2.0 vs 10.1 ml, p < 0.001) when using the bidimensional products. The bidimensional product reached a moderate inter-observer agreement with a Kappa value of 0.689. For 32% of the tumors, the two neuroradiologists calculated a difference of more than 25% using bidimensional products.

Conclusions

Clinical radiological reporting and the bidimensional product exhibit similar accuracy. The bidimensional product has moderate inter-observer agreement and is prone to underestimating tumor progression, as an average glioblastoma had to grow 10 mL in order to be classified as progressed. These findings underline the assumption that one should try to move towards volumetric assessment of glioblastoma growth in the future.

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References

  1. Boxerman JL, Zhang Z, Safriel Y, Larvie M, Snyder BS, Jain R, Chi TL, Sorensen AG, Gilbert MR, Barboriak DP (2013) Early post-bevacizumab progression on contrast-enhanced MRI as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 Central Reader Study. Neuro-Oncology 15:945–954. https://doi.org/10.1093/neuonc/not049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Chow DS, Qi J, Guo X, Miloushev VZ, Iwamoto FM, Bruce JN, Lassman AB, Schwartz LH, Lignelli A, Zhao B, Filippi CG (2014) Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme. AJNR Am J Neuroradiol 35:498–503. https://doi.org/10.3174/ajnr.A3724

    Article  CAS  PubMed  Google Scholar 

  3. D'Arco F, O'Hare P, Dashti F, Lassaletta A, Loka T, Tabori U, Talenti G, Thust S, Messalli G, Hales P, Bouffet E, Laughlin S (2018) Volumetric assessment of tumor size changes in pediatric low-grade gliomas: feasibility and comparison with linear measurements. Neuroradiology 60:427–436. https://doi.org/10.1007/s00234-018-1979-3

    Article  PubMed  Google Scholar 

  4. Fyllingen EH, Stensjoen AL, Berntsen EM, Solheim O, Reinertsen I (2016) Glioblastoma segmentation: comparison of three different software packages. PLoS One 11:e0164891. https://doi.org/10.1371/journal.pone.0164891

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Gahrmann R, van den Bent M, van der Holt B, Vernhout RM, Taal W, Vos M, de Groot JC, Beerepoot LV, Buter J, Flach ZH, Hanse M, Jasperse B, Smits M (2017) Comparison of 2D (RANO) and volumetric methods for assessment of recurrent glioblastoma treated with bevacizumab-a report from the BELOB trial. Neuro-Oncology 19:853–861. https://doi.org/10.1093/neuonc/now311

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gui C, Lau JC, Kosteniuk SE, Lee DH, Megyesi JF (2019) Radiology reporting of low-grade glioma growth underestimates tumor expansion. Acta Neurochir 161:569–576. https://doi.org/10.1007/s00701-018-03783-3

    Article  PubMed  Google Scholar 

  7. Jakola AS, Reinertsen I (2019) Radiological evaluation of low-grade glioma: time to embrace quantitative data? Acta Neurochir 161:577–578. https://doi.org/10.1007/s00701-019-03816-5

    Article  PubMed  Google Scholar 

  8. Kanaly CW, Mehta AI, Ding D, Hoang JK, Kranz PG, Herndon JE 2nd, Coan A, Crocker I, Waller AF, Friedman AH, Reardon DA, Sampson JH (2014) A novel, reproducible, and objective method for volumetric magnetic resonance imaging assessment of enhancing glioblastoma. J Neurosurg 121:536–542. https://doi.org/10.3171/2014.4.JNS121952

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M, Kessler T, Foltyn M, Harting I, Sahm F, Prager M, Nowosielski M, Wick A, Nolden M, Radbruch A, Debus J, Schlemmer HP, Heiland S, Platten M, von Deimling A, van den Bent MJ, Gorlia T, Wick W, Bendszus M, Maier-Hein KH (2019) Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 20:728–740. https://doi.org/10.1016/S1470-2045(19)30098-1

    Article  PubMed  Google Scholar 

  10. Levin VA, Crafts DC, Norman DM, Hoffer PB, Spire JP, Wilson CB (1977) Criteria for evaluating patients undergoing chemotherapy for malignant brain tumors. J Neurosurg 47:329–335. https://doi.org/10.3171/jns.1977.47.3.0329

    Article  CAS  PubMed  Google Scholar 

  11. Macdonald DR, Cascino TL, Schold SC Jr, Cairncross JG (1990) Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 8:1277–1280. https://doi.org/10.1200/jco.1990.8.7.1277

    Article  CAS  PubMed  Google Scholar 

  12. McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 22:276–282

    Article  Google Scholar 

  13. Miller AB, Hoogstraten B, Staquet M, Winkler A (1981) Reporting results of cancer treatment. Cancer 47:207–214

    Article  CAS  Google Scholar 

  14. Provenzale JM, Mancini MC (2012) Assessment of intra-observer variability in measurement of high-grade brain tumors. J Neuro-Oncol 108:477–483. https://doi.org/10.1007/s11060-012-0843-2

    Article  Google Scholar 

  15. Sorensen AG, Patel S, Harmath C, Bridges S, Synnott J, Sievers A, Yoon YH, Lee EJ, Yang MC, Lewis RF, Harris GJ, Lev M, Schaefer PW, Buchbinder BR, Barest G, Yamada K, Ponzo J, Kwon HY, Gemmete J, Farkas J, Tievsky AL, Ziegler RB, Salhus MR, Weisskoff R (2001) Comparison of diameter and perimeter methods for tumor volume calculation. J Clin Oncol 19:551–557. https://doi.org/10.1200/JCO.2001.19.2.551

    Article  CAS  PubMed  Google Scholar 

  16. Stensjoen AL, Solheim O, Kvistad KA, Haberg AK, Salvesen O, Berntsen EM (2015) Growth dynamics of untreated glioblastomas in vivo. Neuro-Oncology 17:1402–1411. https://doi.org/10.1093/neuonc/nov029

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, Degroot J, Wick W, Gilbert MR, Lassman AB, Tsien C, Mikkelsen T, Wong ET, Chamberlain MC, Stupp R, Lamborn KR, Vogelbaum MA, van den Bent MJ, Chang SM (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28:1963–1972. https://doi.org/10.1200/JCO.2009.26.3541

    Article  PubMed  Google Scholar 

  18. World Health O (1979) WHO handbook for reporting results of cancer treatment. WHO offset publication ; no. 48., vol Accessed from http://nla.gov.au/nla.cat-vn2889857. World Health Organization ; sold by WHO Publications Centre USA], Geneva : [Albany, N.Y

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Correspondence to Erik Magnus Berntsen.

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The study was approved by the regional ethics committee and adhered to the 1964 Helsinki Declaration. The current study was a retrospective study where no formal consent was required.

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Berntsen, E.M., Stensjøen, A.L., Langlo, M.S. et al. Volumetric segmentation of glioblastoma progression compared to bidimensional products and clinical radiological reports. Acta Neurochir 162, 379–387 (2020). https://doi.org/10.1007/s00701-019-04110-0

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