4101 ORAL Multicentre Prospective Classification of Childhood Brain Tumours Using Magnetic Resonance Spectroscopy

2011 ◽  
Vol 47 ◽  
pp. S284
Author(s):  
A.C. Peet ◽  
T.N. Arvanitis ◽  
D. Auer ◽  
R.G. Grundy ◽  
T. Jaspan ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Brain Tumours ◽  
Resonance Spectroscopy ◽  
Childhood Brain Tumours

Short echo time 1 H magnetic resonance spectroscopy of childhood brain tumours

2006 ◽  
Vol 23 (2) ◽  
pp. 163-169 ◽  
Author(s):  
A. C. Peet ◽  
S. Lateef ◽  
L. MacPherson ◽  
K. Natarajan ◽  
S. Sgouros ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Brain Tumours ◽  
Resonance Spectroscopy ◽  
Childhood Brain Tumours ◽  
Echo Time ◽  
Short Echo Time

scholarly journals The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy

2011 ◽  
Vol 26 (3) ◽  
pp. 353-363 ◽  
Author(s):  
Alexander Gibb ◽  
John Easton ◽  
Nigel Davies ◽  
YU Sun ◽  
Lesley MacPherson ◽  
...  
Keyword(s):  
User Interface ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Brain Tumour ◽  
Brain Tumours ◽  
Resonance Spectroscopy ◽  
Non Invasive ◽  
Childhood Brain Tumours ◽  
Tumour Diagnosis ◽  
Brain Tumour Diagnosis

AbstractMagnetic resonance spectroscopy (MRS) is a non-invasive method, which can provide diagnostic information on children with brain tumours. The technique has not been widely used in clinical practice, partly because of the difficulty of developing robust classifiers from small patient numbers and the challenge of providing decision support systems (DSSs) acceptable to clinicians. This paper describes a participatory design approach in the development of an interactive clinical user interface, as part of a distributed DSS for the diagnosis and prognosis of brain tumours. In particular, we consider the clinical need and context of developing interactive elements for an interface that facilitates the classification of childhood brain tumours, for diagnostic purposes, as part of the HealthAgents European Union project. Previous MRS-based DSS tools have required little input from the clinician user and a raw spectrum is essentially processed to provide a diagnosis sometimes with an estimate of error. In childhood brain tumour diagnosis where there are small numbers of cases and a large number of potential diagnoses, this approach becomes intractable. The involvement of clinicians directly in the designing of the DSS for brain tumour diagnosis from MRS led to an alternative approach with the creation of a flexible DSS that, allows the clinician to input prior information to create the most relevant differential diagnosis for the DSS. This approach mirrors that which is currently taken by clinicians and removes many sources of potential error. The validity of this strategy was confirmed for a small cohort of children with cerebellar tumours by combining two diagnostic types, pilocytic astrocytomas (11 cases) and ependymomas (four cases) into a class of glial tumours which then had similar numbers to the other diagnostic type, medulloblastomas (18 cases). Principal component analysis followed by linear discriminant analysis on magnetic resonance spectral data gave a classification accuracy of 91% for a three-class classifier and 94% for a two-class classifier using a leave-one-out analysis. This DSS provides a flexible method for the clinician to use MRS for brain tumour diagnosis in children.

Application of CT Energy Spectrum Combined with MR Spectroscopy in the Classification of Meningiomas

2020 ◽  
Vol 10 (8) ◽  
pp. 1949-1954
Author(s):  
Ruiping Chai ◽  
Hong Zhang ◽  
Hongcan Ma ◽  
Ximi Xu ◽  
Hamris Andrew
Keyword(s):  
Energy Spectrum ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Mr Spectroscopy ◽  
Low Energy ◽  
Resonance Spectroscopy ◽  
Who Grade ◽  
Quantitative Parameters ◽  
Cr Concentration

Objective: To study the application value of CT spectroscopy combined with MR spectroscopy in the classification and classification of meningioma, in order to provide more information for the classification and classification of meningioma from the perspective of imaging, and to improve the intraoperative tumor resection. Provide reference for selection and evaluation of prognosis, judgment of efficacy, and prediction of recurrence. Methods: A total of 102 patients who underwent preoperative energy spectroscopy and 3.0T magnetic resonance spectroscopy were enrolled in our hospital from March 2017 to March 2018. There were 48 patients with spectral CT scan, 43 patients with MR spectroscopy, and 11 patients with energy spectrum CT and MR spectroscopy. Correlation between grading and typing of meningiomas was performed using quantitative parameters of energy spectrum CT and magnetic resonance spectroscopy. Study and evaluate the diagnostic efficacy of quantitative parameters. Results: The results of CT spectra combined with MR spectroscopy in the pathological grading of meningiomas showed significant differences in CT values at low energy (40–70 KeV) (P < 0.05), which showed that the CT values of WHOI-level single energy were less than WHO II grade; WHO grade I meningioma curve slope was significantly smaller than WHO II grade (P < 0.05), statistically significant; also found that WHO grade I meningioma Cho concentration, Cr concentration, Cho/Cr ratio and WHO II grade There was a significant difference (P < 0.05). Conclusion: CT spectroscopy combined with MR spectroscopy imaging in the diagnosis of meningioma, the single-energy CT value and slope of the low-energy (40–70 KeV) level of WHO grade I meningioma, the Cho concentration is significantly lower than the WHO II-level meninges. Tumor, and the Cr concentration is significantly higher than the WHO I level, the combination of the two can more accurately diagnose the meningioma.

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