Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
22 06 2022
22 06 2022
Historique:
received:
17
06
2020
accepted:
27
05
2022
entrez:
22
6
2022
pubmed:
23
6
2022
medline:
25
6
2022
Statut:
epublish
Résumé
In glioblastoma, the response to treatment assessment is essentially based on the 2D tumor size evolution but remains disputable. Volumetric approaches were evaluated for a more accurate estimation of tumor size. This study included 57 patients and compared two volume measurement methods to determine the size of different glioblastoma regions of interest: the contrast-enhancing area, the necrotic area, the gross target volume and the volume of the edema area. The two methods, the ellipsoid formula (the calculated method) and the manual delineation (the measured method) showed a high correlation to determine glioblastoma volume and a high agreement to classify patients assessment response to treatment according to RANO criteria. This study revealed that calculated and measured methods could be used in clinical practice to estimate glioblastoma volume size and to evaluate tumor size evolution.
Identifiants
pubmed: 35732848
doi: 10.1038/s41598-022-13739-4
pii: 10.1038/s41598-022-13739-4
pmc: PMC9217851
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
10502Informations de copyright
© 2022. The Author(s).
Références
Ostrom, Q. T., Gittleman, H., Stetson, L., Virk, S. M. & Barnholtz-Sloan, J. S. Epidemiology of gliomas. Cancer Treat. Res. 163, 1–14 (2015).
pubmed: 25468222
doi: 10.1007/978-3-319-12048-5_1
Ellingson, B. M., Wen, P. Y. & Cloughesy, T. F. Evidence and context of use for contrast enhancement as a surrogate of disease burden and treatment response in malignant glioma. Neuro Oncol. 20, 457–471 (2018).
pubmed: 29040703
doi: 10.1093/neuonc/nox193
Galanis, E. 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. 8, 156–165 (2006).
pubmed: 16533757
pmcid: 1871930
doi: 10.1215/15228517-2005-005
Macdonald, D. R., Cascino, T. L., Schold, S. C. & Cairncross, J. G. Response criteria for phase II studies of supratentorial malignant glioma. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 8, 1277–1280 (1990).
doi: 10.1200/JCO.1990.8.7.1277
Wen, P. Y. et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 28, 1963–1972 (2010).
doi: 10.1200/JCO.2009.26.3541
Wang, M.-Y. et al. Measurement of tumor size in adult glioblastoma: classical cross-sectional criteria on 2D MRI or volumetric criteria on high resolution 3D MRI?. Eur. J. Radiol. 81, 2370–2374 (2012).
pubmed: 21652157
doi: 10.1016/j.ejrad.2011.05.017
Henson, J. W., Ulmer, S. & Harris, G. J. Brain tumor imaging in clinical trials. AJNR Am. J. Neuroradiol. 29, 419–424 (2008).
pubmed: 18272557
pmcid: 8118884
doi: 10.3174/ajnr.A0963
Henker, C. et al. Volumetric assessment of glioblastoma and its predictive value for survival. Acta Neurochir. (Wien) https://doi.org/10.1007/s00701-019-03966-6 (2019).
doi: 10.1007/s00701-019-03966-6
Ellingson, B. M., Wen, P. Y. & Cloughesy, T. F. Modified criteria for radiographic response assessment in glioblastoma clinical trials. Neurother. J. Am. Soc. Exp. Neurother. 14, 307–320 (2017).
Wen, P. Y. et al. Response assessment in neuro-oncology clinical trials. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 35, 2439–2449 (2017).
doi: 10.1200/JCO.2017.72.7511
Kickingereder, P. et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: A multicentre, retrospective study. Lancet Oncol. 20, 728–740 (2019).
pubmed: 30952559
doi: 10.1016/S1470-2045(19)30098-1
Ertl-Wagner, B. B. et al. Reliability of tumor volume estimation from MR images in patients with malignant glioma: Results from the American College of Radiology Imaging Network (ACRIN) 6662 trial. Eur. Radiol. 19, 599–609 (2009).
pubmed: 18925402
doi: 10.1007/s00330-008-1191-7
Iliadis, G. et al. The importance of tumor volume in the prognosis of patients with glioblastoma: Comparison of computerized volumetry and geometric models. Strahlenther. Onkol. Organ Dtsch. Rontgengesellschaft Al 185, 743–750 (2009).
doi: 10.1007/s00066-009-2015-7
Sreenivasan, S. A., Madhugiri, V. S., Sasidharan, G. M. & Kumar, R. V. R. Measuring glioma volumes: A comparison of linear measurement based formulae with the manual image segmentation technique. J. Cancer Res. Ther. 12, 161–168 (2016).
pubmed: 27072231
doi: 10.4103/0973-1482.153999
Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–2024 (2015).
pubmed: 25494501
doi: 10.1109/TMI.2014.2377694
Shah, G. D. et al. Comparison of linear and volumetric criteria in assessing tumor response in adult high-grade gliomas. Neuro Oncol. 8, 38–46 (2006).
pubmed: 16443946
pmcid: 1871928
doi: 10.1215/S1522851705000529
Chow, D. S. et al. Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme. AJNR Am. J. Neuroradiol. 35, 498–503 (2014).
pubmed: 23988756
pmcid: 7964732
doi: 10.3174/ajnr.A3724
Meier, R. et al. Clinical evaluation of a fully-automatic segmentation method for longitudinal brain tumor volumetry. Sci. Rep. 6, 23376 (2016).
pubmed: 27001047
pmcid: 4802217
doi: 10.1038/srep23376
Porz, N. et al. Multi-modal glioblastoma segmentation: Man versus machine. PLoS ONE 9, e96873 (2014).
pubmed: 24804720
pmcid: 4013039
doi: 10.1371/journal.pone.0096873
Liu, L., Kuang, L. & Ji, Y. Multimodal MRI brain tumor image segmentation using sparse subspace clustering algorithm. Comput. Math. Methods Med. 4, 2020 (2020).
Anwar, S. M., Yousaf, S. & Majid, M. Brain tumor segmentation on multimodal MRI scans using EMAP algorithm. in Annual International Conference on IEEE Engineering Medicine and Biology Society. Vol. 2018. 550–553 (2018).
Ghaffari, M., Sowmya, A. & Oliver, R. Automated brain tumor segmentation using multimodal brain scans: A survey based on models submitted to the BraTS 2012–2018 challenges. IEEE Rev. Biomed. Eng. 13, 156–168 (2020).
pubmed: 31613783
doi: 10.1109/RBME.2019.2946868
Huber, T. et al. Progressive disease in glioblastoma: Benefits and limitations of semi-automated volumetry. PLoS ONE 12, e0173112 (2017).
pubmed: 28245291
pmcid: 5330491
doi: 10.1371/journal.pone.0173112
Kanaly, C. W. et al. A novel method for volumetric MRI response assessment of enhancing brain tumors. PLoS ONE 6, e16031 (2011).
pubmed: 21298088
pmcid: 3027624
doi: 10.1371/journal.pone.0016031
Kanaly, C. W. et al. A novel, reproducible, and objective method for volumetric magnetic resonance imaging assessment of enhancing glioblastoma. J. Neurosurg. 121, 536–542 (2014).
pubmed: 25036205
pmcid: 4286293
doi: 10.3171/2014.4.JNS121952
Berntsen, E. M. et al. Volumetric segmentation of glioblastoma progression compared to bidimensional products and clinical radiological reports. Acta Neurochir. (Wien) 162, 379–387 (2020).
doi: 10.1007/s00701-019-04110-0
Sorensen, A. G. et al. Comparison of diameter and perimeter methods for tumor volume calculation. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 19, 551–557 (2001).
doi: 10.1200/JCO.2001.19.2.551
Pichler, J., Pachinger, C., Pelz, M. & Kleiser, R. MRI assessment of relapsed glioblastoma during treatment with bevacizumab: Volumetric measurement of enhanced and FLAIR lesions for evaluation of response and progression—A pilot study. Eur. J. Radiol. 82, e240-245 (2013).
pubmed: 23399039
doi: 10.1016/j.ejrad.2012.12.018
Wang, M.-Y. et al. Comparison of volumetric methods for tumor measurements on two and three dimensional MRI in adult glioblastoma. Neuroradiology 53, 565–569 (2011).
pubmed: 21057780
doi: 10.1007/s00234-010-0789-z
van den Bent, M. J., Vogelbaum, M. A., Wen, P. Y., Macdonald, D. R. & Chang, S. M. End point assessment in gliomas: Novel treatments limit usefulness of classical Macdonald’s criteria. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 27, 2905–2908 (2009).
doi: 10.1200/JCO.2009.22.4998
Dempsey, M. F., Condon, B. R. & Hadley, D. M. Measurement of tumor ‘size’ in recurrent malignant glioma: 1D, 2D, or 3D?. AJNR Am. J. Neuroradiol. 26, 770–776 (2005).
pubmed: 15814919
pmcid: 7977136
Huber, T. et al. Reliability of semi-automated segmentations in glioblastoma. Clin. Neuroradiol. 27, 153–161 (2017).
pubmed: 26490369
doi: 10.1007/s00062-015-0471-2
Sorensen, A. G., Batchelor, T. T., Wen, P. Y., Zhang, W.-T. & Jain, R. K. Response criteria for glioma. Nat. Clin. Pract. Oncol. 5, 634–644 (2008).
pubmed: 18711427
pmcid: 4795821
doi: 10.1038/ncponc1204
Egger, J. et al. GBM volumetry using the 3D slicer medical image computing platform. Sci. Rep. 3, 1364 (2013).
pubmed: 23455483
pmcid: 3586703
doi: 10.1038/srep01364
Fyllingen, E. H., Stensjøen, A. L., Berntsen, E. M., Solheim, O. & Reinertsen, I. Glioblastoma segmentation: Comparison of three different software packages. PLoS ONE 11, e0164891 (2016).
pubmed: 27780224
pmcid: 5079567
doi: 10.1371/journal.pone.0164891
Pope, W. B. & Hessel, C. Response assessment in neuro-oncology criteria: Implementation challenges in multicenter neuro-oncology trials. AJNR Am. J. Neuroradiol. 32, 794–797 (2011).
pubmed: 21474628
pmcid: 7965568
doi: 10.3174/ajnr.A2582
Ellingson, B. M. et al. Quantitative volumetric analysis of conventional MRI response in recurrent glioblastoma treated with bevacizumab. Neuro Oncol. 13, 401–409 (2011).
pubmed: 21324937
pmcid: 3064698
doi: 10.1093/neuonc/noq206
Buemi, F. et al. Apparent diffusion coefficient and tumor volume measurements help stratify progression-free survival of bevacizumab-treated patients with recurrent glioblastoma multiforme. Neuroradiol. J. 32, 241–249 (2019).
pubmed: 31066622
pmcid: 6639642
doi: 10.1177/1971400919847184
Huang, R. Y. et al. Recurrent glioblastoma: Volumetric assessment and stratification of patient survival with early posttreatment magnetic resonance imaging in patients treated with bevacizumab. Cancer 119, 3479–3488 (2013).
pubmed: 23821555
doi: 10.1002/cncr.28210
Gzell, C. E., Wheeler, H. R., McCloud, P., Kastelan, M. & Back, M. Small increases in enhancement on MRI may predict survival post radiotherapy in patients with glioblastoma. J. Neurooncol. 128, 67–74 (2016).
pubmed: 26879084
doi: 10.1007/s11060-016-2074-4
Zhuge, Y. et al. Brain tumor segmentation using holistically nested neural networks in MRI images. Med. Phys. 44, 5234–5243 (2017).
pubmed: 28736864
doi: 10.1002/mp.12481
Hwang, E. J. et al. Early response evaluation for recurrent high grade gliomas treated with bevacizumab: a volumetric analysis using diffusion-weighted imaging. J. Neurooncol. 112, 427–435 (2013).
pubmed: 23417358
doi: 10.1007/s11060-013-1072-z
Roberts, T. A. et al. Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response. Sci. Rep. 10, 9223 (2020).
pubmed: 32514049
pmcid: 7280197
doi: 10.1038/s41598-020-65956-4
Hu, L. S. et al. Accurate patient-specific machine learning models of glioblastoma invasion using transfer learning. AJNR Am. J. Neuroradiol. 40, 418–425 (2019).
pubmed: 30819771
pmcid: 6474354
Shaver, M. M. et al. Optimizing neuro-oncology imaging: A review of deep learning approaches for glioma imaging. Cancers 11, 829 (2019).
pmcid: 6627902
doi: 10.3390/cancers11060829
Juan-Albarracín, J., Fuster-Garcia, E., García-Ferrando, G. A. & García-Gómez, J. M. ONCOhabitats: A system for glioblastoma heterogeneity assessment through MRI. Int. J. Med. Inf. 128, 53–61 (2019).
doi: 10.1016/j.ijmedinf.2019.05.002
Chaddad, A. et al. Radiomics in glioblastoma: Current status and challenges facing clinical implementation. Front. Oncol. 9, 374 (2019).
pubmed: 31165039
pmcid: 6536622
doi: 10.3389/fonc.2019.00374
Bland, J. M. & Altman, D. G. Survival probabilities (the Kaplan-Meier method). BMJ 317, 1572–1580 (1998).
pubmed: 9836663
pmcid: 1114388
doi: 10.1136/bmj.317.7172.1572
Marubini, E. & Valsecchi, M. G. Analysing Survival Data from Clinical Trials and Observational Studies (Wiley, 2004).
Therneau, T. M. & Grambsch, P. M. The Cox model. in Modeling Survival Data: Extending the Cox Model (eds. Therneau, T. M. & Grambsch, P. M.). 39–77. https://doi.org/10.1007/978-1-4757-3294-8_3 (Springer, 2000).