Search results for "Brain tumours"
showing 4 items of 4 documents
Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV 1H MRS: Evaluation as …
2014
The results of a randomized pilot study and qualitative evaluation of the clinical decision support system Curiam BT are reported. We evaluated the system's feasibility and potential value as a radiological information procedure complementary to magnetic resonance (MR) imaging to assist novice radiologists in diagnosing brain tumours using MR spectroscopy (1.5 and 3.0T). Fifty-five cases were analysed at three hospitals according to four non-exclusive diagnostic questions. Our results show that Curiam BT improved the diagnostic accuracy in all the four questions. Additionally, we discuss the findings of the users' feedback about the system, and the further work to optimize it for real envir…
Medical exposure to ionising radiation and the risk of brain tumours: Interphone study group, Germany
2007
Abstract Background The role of exposure to low doses of ionising radiation in the aetiology of brain tumours has yet to be clarified. The objective of this study was to investigate the association between medically or occupationally related exposure to ionising radiation and brain tumours. Methods We used self-reported medical and occupational data collected during the German part of a multinational case–control study on mobile phone use and the risk of brain tumours (Interphone study) for the analyses. Results For any exposure to medical ionising radiation we found odds ratios (ORs) of 0.63 (95% confidence interval (CI) = 0.48–0.83), 1.08 (95% CI = 0.80–1.45) and 0.97 (95% CI = 0.54–1.75)…
Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis
2011
In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally…
Accurate classification of childhood brain tumours by in vivo H-1 MRS - A multi-centre study
2013
Aims: To evaluate the accuracy of single-voxel Magnetic Resonance Spectroscopy (1H-MRS) as a non-invasive diagnostic aid for pediatric brain tumours in a multi-national study. Our hypotheses are (1) that automated classification based on 1H-MRS provides an accurate non-invasive diagnosis in multi-centre datasets and (2) using a protocol which increases the metabolite information improves the diagnostic accuracy. Methods: 78 patients under 16 years old with histologically proven brain tumours from 10 international centres were investigated. Discrimination of 29 medulloblastomas, 11 ependymomas and 38 pilocytic astrocytomas was evaluated. Single-voxel MRS was undertaken prior to diagnosis (1.…