The DOI matches its Crossref record.
Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra
Open accessAbstract
A computer‐based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi‐centre INTERPRET project. Spectra from a database of 1 H single‐voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four different centres, are clustered according to their pathology, using automated pattern recognition techniques and the results are presented as a two‐dimensional scatterplot using an intuitive graphical user interface (GUI). Formal quality control procedures were performed to standardize the performance of the instruments and check each spectrum, and teams of expert neuroradiologists, neurosurgeons, neurologists and neuropathologists clinically validated each case. The prototype decision support system (DSS) successfully classified 89% of the cases in an independent test set of 91 cases of the most frequent tumour types (meningiomas, low‐grade gliomas and high‐grade malignant tumours—glioblastomas and metastases). It also helps to resolve diagnostic difficulty in borderline cases. When the prototype was tested by radiologists and other clinicians it was favourably received. Results of the preliminary clinical analysis of the added value of using the DSS for brain tumour diagnosis with MRS showed a small but significant improvement over MRI used alone. In the comparison of individual pathologies, PNETs were significantly better diagnosed with the DSS than with MRI alone. Copyright © 2006 John Wiley & Sons, Ltd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
eD octor: machine learning and the future of medicine
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12822 · Cited by 956
Artificial neural networks in medical diagnosis
Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013) · Journal of Applied Biomedicine · doi:10.2478/v10136-012-0031-x · Cited by 898
Glutamate and glutamine: a review of in vivo MRS in the human brain
Ramadan, S., Lin, A., & Stanwell, P. (2013) · NMR in Biomedicine · doi:10.1002/nbm.3045 · Cited by 282
Metadata
- Authors
- Tate, A. R., Underwood, J., Acosta, D. M., Julià‐Sapé, M., Majós, C., Moreno‐Torres, À., Howe, F. A., van der Graaf, M., Lefournier, V., Murphy, M. M., Loosemore, A., Ladroue, C., Wesseling, P., Luc Bosson, J., Cabañas, M. E., Simonetti, A. W., Gajewicz, W., Calvar, J., Capdevila, A., Wilkins, P. R., Bell, B. A., Rémy, C., Heerschap, A., Watson, D., Griffiths, J. R., & Arús, C.
- Venue
- NMR in Biomedicine
- Publisher
- Wiley
- Published
- 2006-06
- Volume
- 19
- Issue
- 4
- Pages
- 411-434
- ISSN
- 0952-3480 (ISSN-L)
- 1099-1492
- OpenAlex
- W2121800619
- Cited by
- 228
- References
- 0
- FWCI
- 16.88
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. https://app.cochat.ai/papers/10.1002/nbm.1016
- The paper
- Tate, A. R., Underwood, J., Acosta, D. M., Julià‐Sapé, M., Majós, C., Moreno‐Torres, À., Howe, F. A., van der Graaf, M., Lefournier, V., Murphy, M. M., Loosemore, A., Ladroue, C., Wesseling, P., Luc Bosson, J., Cabañas, M. E., Simonetti, A. W., Gajewicz, W., Calvar, J., Capdevila, A., . . . Arús, C. (2006). Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. NMR in Biomedicine. https://doi.org/10.1002/nbm.1016
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
