Biomedical subjects

Margarida Julià-Sapé

Publications and source records attributed to Margarida Julià-Sapé.

4 recordsLinked to original sources

PubMed2006

A multi-centre, web-accessible and quality control-checked database of in vivo MR spectra of brain tumour patients.

OBJECTIVE: To describe an Internet-accessible database that contains validated in vivo MR spectra and clinical data of brain tumour patients. MATERIALS AND METHODS: All data from patients entering the INTERPRET project (International Network for Pattern Recognition of Tumours Using Magnetic Resonance, ) were stored in a web-accessible database (iDB) and selected using its query functionality. Criteria for selection were that the case had a single voxel (SV) short-echo (20-32 ms) 1.5 T spectrum acquired from a nodular region of the tumour, that the voxel had been positioned in the same region as where subsequent biopsy was obtained, that the short-echo spectrum had not been discarded because of acquisition artefacts or other reasons, and that a histopathological diagnosis was agreed among a committee of neuropathologists. When the spectra were obtained from normal volunteers or were of abscesses or clinically proven metastases, biopsy was not required. RESULTS: A subset of 304 cases (22 normal volunteers and 282 tumour patients) was obtained. These cases were migrated to another similar database (validated-DB). CONCLUSION: The validated-DB complies with ethics regulations and represents the population studied. It is accessible by neuroradiologists willing to use information provided by MRS to help in the non-invasive diagnosis of brain tumours.

Biomarkers, Tumor↗

PubMed2006

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra.

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 1H 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.

Algorithms↗

PubMed2006

Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database.

OBJECT: The aim of this study was to estimate the accuracy of routine magnetic resonance (MR) imaging studies in the classification of brain tumors in terms of both cell type and grade of malignancy. METHODS: The authors retrospectively assessed the correlation between neuroimaging classifications and histopathological diagnoses by using multicenter database records from 393 patients with brain tumors. An ontology was devised to establish diagnostic agreement. Each tumor category was compared with the corresponding histopathological diagnoses by dichotomization. Sensitivity, specificity, positive and negative predictive values (PPVs and NPVs, respectively), and the Wilson 95% confidence intervals (CI) for each were calculated. In routine reporting of MR imaging examinations, tumor types and grades were classified with a high specificity (85.2-100%); sensitivity varied, depending on the tumor type and grade, alone or in combination. The recognition of broad diagnostic categories (neuroepithelial or meningeal lesions) was highly sensitive, whereas when both detailed type and grade were considered, sensitivity diverged, being highest in low-grade meningioma (sensitivity 100%, 95% CI 96.2-100.0%) and lowest in high-grade meningioma (sensitivity 0.0%, 95% CI 0.0-65.8%) and low-grade oligodendroglioma (sensitivity 15%, 95% CI 5.2-36.0%). In neuroepithelial tumors, sensitivity was inversely related to the precision in reporting of grade and cellular origin; "glioma" was a frequent neuroimaging classification associated with higher sensitivity in the corresponding category. The PPVs varied among categories, in general being greater than their prevalence in this dataset. The NPV was high in all categories (69.8-100%). CONCLUSIONS: The PPVs and NPVs provided in this study may be used as estimates of posttest probabilities of diagnostic accuracy using MR imaging. This study targets the need for noninvasively increasing sensitivity in categorizing most brain tumor types while retaining high specificity, especially in the differentiation of high- and low-grade glial tumor classes.

Adolescent↗

PubMedDate not supplied

Brain tumor classification by proton MR spectroscopy: comparison of diagnostic accuracy at short and long TE.

BACKGROUND AND PURPOSE: Different TE can be used for obtaining MR spectra of brain tumors. The purpose of this study was to determine the influence of the TE used in brain tumor classification by comparing the performance of spectra obtained at two different TE (30 ms and 136 ms). METHODS: One hundred fifty-one studies of patients with brain tumors (37 meningiomas, 12 low grade astrocytomas, 16 anaplastic astrocytomas, 54 glioblastomas, and 32 metastases) were retrospectively selected from a series of 378 consecutive examinations of brain masses. Single voxel proton MR spectroscopy at TE 30 ms and 136 ms was performed with point-resolved spectroscopy in all cases. Fitted areas of nine resonances of interest were normalized to water. Tumors were classified into four groups (meningioma, low grade astrocytoma, anaplastic astrocytoma, and glioblastoma-metastases) by means of linear discriminant analysis. The performance of linear discriminant analysis at each TE was assessed by using the leave-one-out method. RESULTS: Tumor classification was slightly better at short TE (123 [81%] of 151 cases correctly classified) than at long TE (118 [78%] of 151 cases correctly classified). Meningioma was the only group that showed higher sensitivity and specificity at long TE. Improved results were obtained when both TE were considered simultaneously: the suggested diagnosis was correct in 105 (94%) of 112 cases when both TE agreed, whereas the correct diagnosis was suggested by at least one TE in 136 (90%) of 151 cases. CONCLUSION: Short TE provides slightly better tumor classification, and results improve when both TE are considered simultaneously. Meningioma was the only tumor group in which long TE performed better than short TE.

Adolescent↗