This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Management of diffuse low-grade glioma (DLGG) relies extensively on tumour volume estimation from MRI datasets. Two methods are currently clinically used to define this volume: the commonly used three-diameters solution and the more rarely used software-based volume reconstruction from the manual segmentations approach. The authors conducted an initial study of inter-practitioners’ variability of software-based manual segmentations on DLGGs MRI datasets. A panel of 13 experts from various specialties and years of experience delineated 12 DLGGs’ MRI scans. A statistical analysis on the segmented tumour volumes and pixels indicated that the individual practitioner, the years of experience and the specialty seem to have no significant impact on the segmentation of DLGGs. This is an interesting result as it had not yet been demonstrated and as it encourages cross-disciplinary collaboration. Their second study was with the three-diameters method, investigating its impact and that of the software-based volume reconstruction from manual segmentations method on tumour volume. They relied on the same dataset and on a participant from the first study. They compared the average of tumour volumes acquired by software reconstruction from manual segmentations method with tumour volumes obtained with the three-diameters method. The authors found that there is no statistically significant difference between the volumes estimated with the two approaches. These results correspond to non-operated and easily delineable DLGGs and are particularly interesting for time-consuming CUBE MRIs. Nonetheless, the three-diameters method has limitations in estimating tumour volumes for resected DLGGs, for which case the software-based manual segmentation method becomes more appropriate.
References
-
-
1)
-
2. MICCAI: ‘Miccai-BRATS 2016’, .
-
2)
-
4. Bauer, S., Nolte, L.-P., Reyes, M.: ‘Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization’. Int. Conf. Medical Image Computing and Computer-Assisted Intervention, 2011, pp. 354–361.
-
3)
-
8. Xie, K., Yang, J., Zhang, Z.G., et al: ‘Semi-automated brain tumor and edema segmentation using MRI’, Eur. J. Radiol., 2005, 56, (1), pp. 12–19 (doi: 10.1016/j.ejrad.2005.03.028).
-
4)
-
9. Kaus, M.R., Warfield, S.K., Nabavi, A., et al: ‘Automated segmentation of MR images of brain tumor’, Radiology, 2001, 218, (2), pp. 586–591 (doi: 10.1148/radiology.218.2.r01fe44586).
-
5)
-
5. Zikic, D., Ioannou, Y., Brown, M., et al: ‘Segmentation of brain tumor tissues with convolutional neural networks’. Proc. MICCAI-BRATS, 2014, pp. 36–39.
-
6)
-
15. Pett, M.A.: ‘Nonparametric statistics for health care research: statistics for small samples and unusual distributions’ (Sage Publications, 2015).
-
7)
-
3. Menze, B.H., Jakab, A., Bauer, S., et al: ‘The multimodal brain tumor image segmentation benchmark (BRATS)’, IEEE Trans. Med. Imaging, 2015, 34, (10), pp. 1993–2024 (doi: 10.1109/TMI.2014.2377694).
-
8)
-
10. Chamberlain, M.C.: ‘Is the volume of low-grade glioma measurable and is it clinically relevant?’, Neuro-Oncology, 2014, 16, (8), pp. 1027–1028 (doi: 10.1093/neuonc/nou119).
-
9)
-
7. Kamnitsas, K., Ferrante, E., Parisot, S., et al: ‘DeepMedic for brain tumor segmentation’. Int. Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2016, pp. 138–149.
-
10)
-
1. Mandonnet, E., Pallud, J., Clatz, O., et al: ‘Computational modeling of the WHO grade II glioma dynamics: principles and applications to management paradigm’, Neurosurg. Rev., 2008, 31, (3), pp. 263–269 (doi: 10.1007/s10143-008-0128-6).
-
11)
-
14. Altman, D.G.: ‘Practical statistics for medical research’ (CRC Press, Boca Raton, FL, USA, 1990).
-
12)
-
11. Pallud, J., Blonski, M., Mandonnet, E., et al: ‘Velocity of tumor spontaneous expansion predicts long-term outcomes for diffuse low-grade gliomas’, Neuro-Oncology, 2013, 15, (5), pp. 595–606 (doi: 10.1093/neuonc/nos331).
-
13)
-
6. Havaei, M., Dutil, F., Pal, C., et al: ‘A convolutional neural network approach to brain tumor segmentation’. Int. Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2015, pp. 195–208.
-
14)
-
13. Ben Abdallah, M., Blonski, M., Wantz-Mézières, S., et al: ‘Statistical evaluation of manual segmentation of a diffuse low-grade glioma MRI dataset’. 38th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, Orlando, FL, 2016, pp. 4403–4406, .
-
15)
-
12. Sorensen, A.G., Patel, S., Harmath, C., et al: ‘Comparison of diameter and perimeter methods for tumor volume calculation’, J. Clin. Oncol., 2001, 19, (2), pp. 551–557 (doi: 10.1200/JCO.2001.19.2.551).
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