Dionisio Acosta | PubMed Health

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

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↗