Intracranial dermoid cyst rupture with subarachnoid and intraventricular fat dissemination.
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Adipose Tissue↗
Biomedical subjects
Carles Majós
Publications and source records attributed to Carles Majós.
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↗
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↗
Automated classification of short echo time in in vivo 1H brain tumor spectra: a multicenter study.
Automated pattern recognition techniques are needed to help radiologists categorize MRS data of brain tumors according to histological type and grade. A major question is whether a computer program "trained" on spectra from one hospital will be able to classify those from another, particularly if the acquisition protocol is different. A subset of 144 histopathologically validated brain tumor spectra in the INTERPRET database, obtained from three of the collaborating centers, was grouped into meningiomas, low-grade astrocytomas, and "aggressive tumors" (glioblastomas and metastases). Spectra from two centers formed the training set (94 spectra) while the third acted as the test set (50 spectra). Linear discriminant analysis successfully classified 48/50 in the test set; the remaining two were atypical cases. When the training and test sets were combined, 133 of the 144 spectra were correctly classified using the leave-one-out procedure. These spectra had been obtained using different sequences (STEAM and PRESS), different echo times (20, 30, 31, and 32 ms), different repetition times (1600 and 2000 ms), and different manufacturers' instruments (GE and Philips). Pattern recognition algorithms are less sensitive to acquisition parameters than had been expected.
Astrocytoma↗
Proton magnetic resonance spectroscopy ((1)H MRS) of human brain tumours: assessment of differences between tumour types and its applicability in brain tumour categorization.
Our objective was to evaluate the usefulness of proton magnetic resonance spectroscopy ((1)H MRS) in categorizing brain tumours. In vivo single-voxel (1)H MRS at an echo time of 136 ms was performed in 108 patients with brain neoplasms that included 29 meningiomas (MEN), 15 low-grade astrocytomas (LGA), 12 anaplastic astrocytomas (AA), 25 glioblastomas (GBM) and 27 metastases (MET). Time-domain fitted areas of nine resonances were evaluated in all spectra. Twenty-five additional tumours were prospectively included as independent test set. Differences in at least two resonances were found in all pairwise comparisons of tumour groups except in GBM vs MET. Large lipid resonance at 1.30 ppm was found to be characteristic of GBM and MET, and alanine was characteristic of MEN. Significant differences were found between LGA and AA in choline-containing compounds and total creatine resonances. When implemented in a stepwise algorithm, these findings correctly classified 84% (21 of 25) tumours in the independent test set. Some additional utility was found in glycine/myo-inositol at 3.55 ppm for bilateral differentiation between GBM and MET (9 of 11, 82% correct classification in the test set). (1)H MRS provides useful information to categorize the most common brain tumours that can be implemented in clinical practice with satisfactory results.
Adolescent↗
Adult primitive neuroectodermal tumor: proton MR spectroscopic findings with possible application for differential diagnosis.
PURPOSE: To assess the utility of proton magnetic resonance (MR) spectroscopy in the clinical categorization of primitive neuroectodermal tumors (PNETs) in adults. MATERIALS AND METHODS: In vivo proton MR spectroscopy was performed with an echo time of 136 msec in nine adults with PNET, and findings were retrospectively compared with spectroscopic findings of 22 meningiomas, 12 low-grade astrocytomas, eight anaplastic astrocytomas, 23 glioblastomas, and 21 metastases. Nine resonances were semiquantitatively evaluated. Statistical analysis was performed by using Kruskal-Wallis and Mann-Whitney U tests. The Hochberg correction was applied for multiple comparisons. Results were prospectively validated in 24 tumors of the six types included in the study. RESULTS: The resonances of choice for identifying PNET were alanine (P <.001) and glutamate and glutamine (P =.004), both decreased with respect to meningioma; choline increased with respect to low-grade (P <.001) and anaplastic astrocytoma (P =.055); and lipids at 1.30 ppm decreased and choline and other trimethyl-amine-containing compounds increased with respect to glioblastoma (P <.001 and P =.004, respectively) and metastasis (P <.001 and P =.021, respectively). We developed an algorithm for bilateral differential diagnosis between PNET and other tumor types. The leave-one-out method was used to test the five possible differential situations in the retrospective data set, with the following results: PNET versus meningioma, 31/23/5/3 (number of total/correct/unclassifiable/incorrect procedures); PNET versus low-grade astrocytoma, 21/19/2/0; PNET versus anaplastic astrocytoma, 17/6/9/2; PNET versus glioblastoma, 32/28/2/2; and PNET versus metastasis, 30/27/1/2. In total, 131 consecutive procedures produced 103 (79%) correct classifications and nine (7%) misclassifications. Twenty-five (78%) of 32 possible procedures in the prospective independent test set produced correct classifications and four (13%) produced incorrect classifications. CONCLUSION: In vivo proton MR spectroscopy provides useful information in clinical differentiation between PNETs and common brain tumors in adults.
Adolescent↗
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↗