Table Info

Table 3.

Dice scores and Hausdorff95 distance of WT, ET, and NET for all datasets

Dataset	Evaluation parameters	Dice score			Hausdorff95 distance
Dataset	Evaluation parameters	WT	ET	NET	WT	ET	NET
Training data (64)	Mean	0.857	0.797	0.752	1.507	2.987	3.639
	SD	0.234	0.247	0.289	1.014	4.197	3.825
	25th quartile	0.870	0.751	0.693	0.870	1.000	1.000
	75th quartile	0.952	0.957	0.985	1.560	3.000	4.030
Validation data (9)	Mean	0.712	0.507	0.437	4.328	8.475	8.502
	SD	0.136	0.285	0.215	3.181	6.862	4.596
	25th quartile	0.593	0.351	0.354	0.593	2.236	5.029
	75th quartile	0.804	0.747	0.599	7.039	13.140	11.720
Test data (21)	Mean	0.606	0.586	0.312	8.130	3.412	20.430
	SD	0.342	0.370	0.384	17.500	3.145	21.260
	25th quartile	0.573	0.198	2.95e–8	0.573	1.207	6.140
	75th quartile	0.834	0.831	0.500	5.024	4.802	20.700

The numbers in parentheses are indicates number of examples used for training, validation and test

Declarations

Acknowledgments

We acknowledge our institute Tata Memorial Hospital, for providing us with the means and resources to be able to write this article and access the images.

Author contributions

A Mahajan: Conceptualization, Project administration, Investigation, Methodology, Supervision, Validation, Writing—review & editing. MB: Conceptualization, Data curation, Formal analysis, Investigation, Writing—review & editing, Writing—original draft. BK: Supervision. A Mlv: Data curation, Formal analysis, Software. MB, UA, BKSS, A Mlv, AG, A Sahu, AC, V Pawar, V Punia, SE, A Sahay, TG, GC, PS and A Moiyadi: Data curation, Investigation. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that there is no conflict of interest.

Ethical approval

The study was conducted after clearance from Institutional Ethics Committee [3296] and conducted in accordance with the guidelines of the Indian Council of Medical Research 2017. The research of this article meets the requirements of the Declaration of Helsinki.

Consent to participate

Informed consent to participate in the study was obtained from all participants.

Consent to publication

Informed consent to publication was obtained from relevant participants.

Availability of data and materials

The datasets generated in this study are available on request to the corresponding author, Abhishek Mahajan (drabhishek.mahajan@yahoo.in; abhiradiology@gmail.com).

Funding

Not applicable.

Copyright

References

Madhavan R, Kannabiran BP, Nithya AM, Kani J, Balasubramaniam P, Shanmugakumar S. Pediatric brain tumors: an analysis of 5 years of data from a tertiary cancer care center, India. Indian J Cancer. 2016;53:562–5. [DOI] [PubMed]

AlRayahi J, Zapotocky M, Ramaswamy V, Hanagandi P, Branson H, Mubarak W, et al. Pediatric brain tumor genetics: what radiologists need to know. Radiographics. 2018;38:2102–22. [DOI] [PubMed]

Lacayo A, Farmer PM. Brain tumors in children: a review. Ann Clin Lab Sci. 1991;21:26–35. [PubMed]

Panigrahy A, Blüml S. Neuroimaging of pediatric brain tumors: from basic to advanced magnetic resonance imaging (MRI). J Child Neurol. 2009;24:1343–65. [DOI] [PubMed]

Packer RJ. Childhood brain tumors: accomplishments and ongoing challenges. J Child Neurol. 2008;23:1122–7. [DOI] [PubMed] [PMC]

Hong S, Kim IH, Wang KC. Outcome and prognostic factors of childhood diffuse brainstem glioma. Cancer Res Treat. 2005;37:109–13. [DOI] [PubMed] [PMC]

Ali ZA, Habib RM, Fotoh SA. Role of magnetic resonance imaging in diagnosis of pediatric posterior fossa tumors. Menoufia Medical J. 2020;33:326–31. [DOI]

Mahajan A, Vaidya T, Gupta A, Rane S, Gupta S. Artificial intelligence in healthcare in developing nations: the beginning of a transformative journey. Cancer Res Stat Treat. 2019;2:182. [DOI]

Bothra M, Mahajan A. Mining artificial intelligence in oncology: Tata Memorial Hospital journey. Cancer Res Stat Treat. 2020;3:622–4. [DOI]

10.

Davatzikos C, Barnholtz-Sloan JS, Bakas S, Colen R, Mahajan A, Quintero CB, et al. AI-based prognostic imaging biomarkers for precision neuro-oncology: the ReSPOND consortium. Neuro Oncol. 2020;22:886–8. [DOI] [PubMed] [PMC]

11.

Cherian Kurian N, Sethi A, Reddy Konduru A, Mahajan A, Rane SU. A 2021 update on cancer image analytics with deep learning. WIREs Data Mining Knowl Discov. 2021;11:e1410. [DOI]

12.

Akbari H, Mohan S, Garcia JA, Kazerooni AF, Sako C, Bakas S, et al. NIMG-22. Prediction of glioblastoma cellular infiltration and recurrence using machine learning and multi-parametric mri analysis: results from the multi-institutional respond consortium. Neuro-Oncology. 2021;23:vi132–3. [DOI]

13.

Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv: 1811.02629 [Preprint]. 2019 [cited 2019 Apr 23]. Available from: https://doi.org/10.48550/arXiv.1811.02629

14.

Baid U, Rane SU, Talbar S, Gupta S, Thakur MH, Moiyadi A, et al. Overall survival prediction in glioblastoma with radiomic features using machine learning. Front Comput Neurosci. 2020;14:61. [DOI] [PubMed] [PMC]

15.

Baid U, Ghodasara S, Mohan S, Bilello M, Calabrese E, Colak E, et al. RSNA-ASNR-MICCAI BraTS 2021 Benchmarkon brain tumor segmentation and radiogenomic classification. arXiv:2107.02314 [Preprint]. 2021 [cited 2021 Sep 12]. Available from: https://doi.org/10.48550/arXiv.2107.02314

16.

Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, et al. QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation--analysis of ranking metrics and benchmarking results. arXiv:2112.10074 [Preprint]. 2022 [cited 2022 Aug 23]. Available from: https://doi.org/10.48550/arXiv.2112.10074

17.

Baid U, Talbar S, Rane S, Gupta S, Thakur MH, Moiyadi A, et al. A novel approach for fully automatic intra-tumor segmentation with 3D U-Net architecture for gliomas. Front Comput Neurosci. 2020;14:10. [DOI] [PubMed] [PMC]

18.

Rudie JD, Weiss DA, Colby JB, Rauschecker AM, Laguna B, Braunstein S, et al. Three-dimensional U-Net convolutional neural network for detection and segmentation of intracranial metastases. Radiol Artif Intell. 2021;3:e200204. [DOI] [PubMed] [PMC]

19.

Zhang J, Shen X, Zhuo T, Zhou H. Brain tumor segmentation based on refined fully convolutional neural networks with a hierarchical dice loss. arXiv:1712.09093 [Preprint]. 2018 [cited 2018 Feb 13]. Available from: https://doi.org/10.48550/arXiv.1712.09093

20.

Sudre CH, Li W, Vercauteren T, Ourselin S, Jorge Cardoso M. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso MJ, Arbel T, Carneiro G, Syeda-Mahmood T, Tavares JMRS, Moradi M, et al., editors. Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer International Publishing; 2017. pp. 240–8. [DOI] [PubMed] [PMC]

21.

Piramuthu S. The Hausdorff distance measure for feature selection in learning applications. In: Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of full papers. 32nd Annual Hawaii International Conference on System Sciences; 1999 Jan 5–8; Maui, HI, USA. IEEE; 2002. [DOI]

22.

Zimmerman RA. Neuroimaging of pediatric brain stem diseases other than brain stem glioma. Pediatr Neurosurg. 1996;25:83–92. [DOI] [PubMed]

23.

Duc NM, Huy HQ. Magnetic resonance imaging features of common posterior fossa brain tumors in children: a preliminary vietnamese study. Open Access Maced J Med Sci. 2019;7:2413–8. [DOI] [PubMed] [PMC]

24.

Mangalore S, Aryan S, Prasad C, Santosh V. Imaging characteristics of supratentorial ependymomas: study on a large single institutional cohort with histopathological correlation. Asian J Neurosurg. 2015;10:276–81. [DOI] [PubMed] [PMC]

25.

Arora RS, Alston RD, Eden TO, Estlin EJ, Moran A, Birch JM. Age–incidence patterns of primary CNS tumors in children, adolescents, and adults in England. Neuro-oncology. 2009;11:403–13. [DOI] [PubMed] [PMC]

26.

Hussain IZ, Mohd Zaki F, Mukari SA, Md Pauzi SH, Loh CK, Alias H. Correlation between MRI characteristics of medulloblastoma with histopathological subtypes and 2-year survival. Indian J Radiol Imaging. 2020;30:46–51. [DOI] [PubMed] [PMC]

27.

Yeom KW, Mobley BC, Lober RM, Andre JB, Partap S, Vogel H, et al. Distinctive MRI features of pediatric medulloblastoma subtypes. AJR Am J Roentgenol. 2013;200:895–903. [DOI] [PubMed]

28.

Poretti A, Meoded A, Huisman TA. Neuroimaging of pediatric posterior fossa tumors including review of the literature. J Magn Reson Imaging. 2012;35:32–47. [DOI] [PubMed]

29.

Plaza MJ, Borja MJ, Altman N, Saigal G. Conventional and advanced MRI features of pediatric intracranial tumors: posterior fossa and suprasellar tumors. AJR Am J Roentgenol. 2013;200:1115–24. [DOI] [PubMed]

30.

Camacho AC, Chaljub G, Uribe T, Patterson JT, Swischuk LE. MR imaging of pediatric posterior fossa tumors. Contemp Diagn Radiol. 2007;30:1–6. [DOI] [PubMed]

31.

Quon JL, Bala W, Chen LC, Wright J, Kim LH, Han M, et al. Deep learning for pediatric posterior fossa tumor detection and classification: a multi-institutional study. AJNR Am J Neuroradiol. 2020;41:1718–25. [DOI] [PubMed] [PMC]

32.

Zhou H, Hu R, Tang O, Hu C, Tang L, Chang K, et al. Automatic machine learning to differentiate pediatric posterior fossa tumors on routine MR imaging. AJNR Am J Neuroradiol. 2020;41:1279–85. [DOI] [PubMed] [PMC]