scholarly journals Astroblastoma

Pulse ◽  
2011 ◽  
Vol 4 (1) ◽  
pp. 38-39
Author(s):  
A Khaled ◽  
A Joarder ◽  
M Chandy ◽  
TA Nasir
Keyword(s):  
Young Adults ◽  
Brain Tumour ◽  
Brain Tumours ◽  
Histological Picture ◽  
Children And Young Adults ◽  
Unusual Type

Aims and Objectives: Astroblastoma is one of the very unusual type of brain tumours, whose histogenesis has not been clarified. It occurs mainly among children and young adults. Astroblastoma has characteristic histological picture and varied biological behavioural. We report a 10-year old girl diagnosed as a case of astroblastoma.Clinical presentation: A 10-year old girl was examined for intermittent frontal headache and convulsion for two years. MRI revealed brain tumour which was later confirmed as astroblastoma by histopathological examination.Conclusion: Astroblastoma is rare because of its varied biological behaviour.DOI: http://dx.doi.org/10.3329/pulse.v4i1.6966Pulse Vol.4 January 2010 p.38-39

scholarly journals A pilot study of machine-learning based automated planning for primary brain tumours

2022 ◽  
Vol 17 (1) ◽  
Author(s):  
Derek S. Tsang ◽  
Grace Tsui ◽  
Chris McIntosh ◽  
Thomas Purdie ◽  
Glenn Bauman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Young Adults ◽  
Brain Tumour ◽  
Brain Tumours ◽  
Organs At Risk ◽  
Image Features ◽  
High Quality ◽  
Primary Brain Tumours ◽  
Children And Young Adults ◽  
Testing Set

Abstract Purpose High-quality radiotherapy (RT) planning for children and young adults with primary brain tumours is essential to minimize the risk of late treatment effects. The feasibility of using automated machine-learning (ML) to aid RT planning in this population has not previously been studied. Methods and materials We developed a ML model that identifies learned relationships between image features and expected dose in a training set of 95 patients with a primary brain tumour treated with focal radiotherapy to a dose of 54 Gy in 30 fractions. This ML method was then used to create predicted dose distributions for 15 previously-treated brain tumour patients across two institutions, as a testing set. Dosimetry to target volumes and organs-at-risk (OARs) were compared between the clinically-delivered (human-generated) plans versus the ML plans. Results The ML method was able to create deliverable plans in all 15 patients in the testing set. All ML plans were generated within 30 min of initiating planning. Planning target volume coverage with 95% of the prescription dose was attained in all plans. OAR doses were similar across most structures evaluated; mean doses to brain and left temporal lobe were lower in ML plans than manual plans (mean difference to left temporal, – 2.3 Gy, p = 0.006; mean differences to brain, – 1.3 Gy, p = 0.017), whereas mean doses to right cochlea and lenses were higher in ML plans (+ 1.6–2.2 Gy, p < 0.05 for each). Conclusions Use of an automated ML method to aid RT planning for children and young adults with primary brain tumours is dosimetrically feasible and can be successfully used to create high-quality 54 Gy RT plans. Further evaluation after clinical implementation is planned.

Identifying brain tumours in children and young adults

BMJ ◽  
2013 ◽  
Vol 347 (oct09 4) ◽  
pp. f5844-f5844 ◽  
Author(s):  
S. H. Wilne ◽  
R. A. Dineen ◽  
R. M. Dommett ◽  
T. P. C. Chu ◽  
D. A. Walker
Keyword(s):  
Young Adults ◽  
Brain Tumours ◽  
Children And Young Adults
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