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C Majós

Publications and source records attributed to C Majós.

12 recordsLinked to original sources
PubMed2004

Assignment of the 2.03 ppm resonance in in vivo 1H MRS of human brain tumour cystic fluid: contribution of macromolecules.

MRI and MRS are established techniques for the evaluation of intracranial mass lesions and cysts. The 2.03 ppm signal recorded in their (1)H-MRS spectra is often assigned to NAA from outer volume contamination, although it has also been detected in non-infiltrating tumours and large cysts. We have investigated the molecular origin of this resonance in ten samples of cystic fluids from human brain tumours. The NMR detected content of the 2.03 ppm resonance in 136 ms echo time spectra, assuming an N- CH(3) origin, was 3.19 +/- 1.01 mM. Only one third (34 +/- 12%) of the N-acetyl containing compound (NAC) signal could be extracted by perchloric acid (PCA) indicating that most of it originated in a macromolecular PCA-insoluble component. Chemical analysis of the cyst fluids showed that sialic acid bound to macromolecules would account for 64.3% and hexuronic containing compounds for 29.2% of the NMR-detectable ex vivo signal, 93.4% of the signal at TE 136 ms. Lactate content measured by NMR (6.4 +/- 4.4 mM) and the predominance of NAC originating in sialic acid point to a major origin from tumour rather than from plasma for this 2.03 ppm resonance.

Brain↗
PubMed2004

Brain tumor classification based on long echo proton MRS signals.

There has been a growing research interest in brain tumor classification based on proton magnetic resonance spectroscopy (1H MRS) signals. Four research centers within the EU funded INTERPRET project have acquired a significant number of long echo 1H MRS signals for brain tumor classification. In this paper, we present an objective comparison of several classification techniques applied to the discrimination of four types of brain tumors: meningiomas, glioblastomas, astrocytomas grade II and metastases. Linear and non-linear classifiers are compared: linear discriminant analysis (LDA), support vector machines (SVM) and least squares SVM (LS-SVM) with a linear kernel as linear techniques and LS-SVM with a radial basis function (RBF) kernel as a non-linear technique. Kernel-based methods can perform well in processing high dimensional data. This motivates the inclusion of SVM and LS-SVM in this study. The analysis includes optimal input variable selection, (hyper-) parameter estimation, followed by performance evaluation. The classification performance is evaluated over 200 stratified random samplings of the dataset into training and test sets. Receiver operating characteristic (ROC) curve analysis measures the performance of binary classification, while for multiclass classification, we consider the accuracy as performance measure. Based on the complete magnitude spectra, automated binary classifiers are able to reach an area under the ROC curve (AUC) of more than 0.9 except for the hard case glioblastomas versus metastases. Although, based on the available long echo 1H MRS data, we did not find any statistically significant difference between the performances of LDA and the kernel-based methods, the latter have the strength that no dimensionality reduction is required to obtain such a high performance.

Artificial Intelligence↗
PubMed2004

Classification of brain tumours using short echo time 1H MR spectra.

The purpose was to objectively compare the application of several techniques and the use of several input features for brain tumour classification using Magnetic Resonance Spectroscopy (MRS). Short echo time 1H MRS signals from patients with glioblastomas (n = 87), meningiomas (n = 57), metastases (n = 39), and astrocytomas grade II (n = 22) were provided by six centres in the European Union funded INTERPRET project. Linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel and LS-SVM with radial basis function kernel were applied and evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of binary classifiers, while the percentage of correct classifications was used to evaluate the multiclass classifiers. The influence of several factors on the classification performance has been tested: L2- vs. water normalization, magnitude vs. real spectra and baseline correction. The effect of input feature reduction was also investigated by using only the selected frequency regions containing the most discriminatory information, and peak integrated values. Using L2-normalized complete spectra the automated binary classifiers reached a mean test AUC of more than 0.95, except for glioblastomas vs. metastases. Similar results were obtained for all classification techniques and input features except for water normalized spectra, where classification performance was lower. This indicates that data acquisition and processing can be simplified for classification purposes, excluding the need for separate water signal acquisition, baseline correction or phasing.

Brain Chemistry↗
PubMed2003

Utility of proton MR spectroscopy in the diagnosis of radiologically atypical intracranial meningiomas.

Our aim was to evaluate the usefulness of proton MR spectroscopy ((1)H MRS) in the diagnosis of radiologically atypical brain meningiomas. We studied 37 patients with intracranial meningiomas with MRI and (1)H MRS (TE 136 ms). Their spectra were quantitatively assessed and compared with those of 93 other intracranial brain neoplasms: 15 low-grade and 14 anaplastic astrocytomas, 30 glioblastomas and 34 metastases. The most characteristic features of meningiomas were the presence of alanine, high relative concentrations of choline and glutamine/glutamate and low concentrations of creatine-containing compounds, N-acetyl-containing compounds and lipids. These resonances were assembled in algorithms for two-way differentiation between meningioma and the other tumours. The performance of the algorithms was tested in the 130 patients using the leave-one-out method, with 94% success in differentiating between meningioma and other tumour. Of the 37 meningiomas, five (14%) were thought atypical on MRI, and in only one of these, found to be malignant on histology, was a diagnosis other than meningioma suggested by the algorithm. The other four were correctly classified. We suggest that (1)H MRS provides information on intracranial meningiomas which may be useful in diagnosis of radiologically atypical cases.

Adolescent↗
PubMed2001

A prototype decision support system for MR spectroscopy-assisted diagnosis of brain tumours.

Our objective is to develop a decision support system that improves the accuracy of non-invasive brain tumour diagnosis and grading by enabling radiologists to use data from Magnetic Resonance Spectroscopy (MRS). The system, which uses pattern recognition techniques, is trained on a validated database of spectra and associated clinical information to provide automated classification of spectra from brain tumours. An innovative user-interface presents classification results as a two-dimensional overview plot in which points representing cases of different diseases form distinct clusters. Users can inspect any cases in these plots and compare them with the new, unknown spectrum. Hence, the overview plot can both communicate the classification of a case and help provide explanation for that classification in part by supporting human case-based reasoning. This paper describes the development of a prototype system implemented in JAVA.

Brain Neoplasms↗
PubMed2000

Intraventricular mass lesions of the brain.

Intraventricular tumours represent a diverse group of lesions, some of them infrequent, with a wide variety of radiological features. Determination of their precise aetiology or origin can be difficult. Nevertheless, considering patient's age, location within the ventricles, and some specific radiological features, the radiologist should be able to narrow down the differential diagnosis. This paper reviews the characteristic radiological appearances of the diverse intraventricular lesions emphasising its differential diagnosis.

Adolescent↗
PubMed1999

Intraventricular meningiomas: MR imaging and MR spectroscopic findings in two cases.

CT, MR imaging, MR spectroscopy, and angiography were performed in two men (ages 21 and 48, respectively) with intraventricular meningioma. In both cases, CT and MR imaging showed large tumors located in the trigone of the right lateral ventricle that enhanced intensely after contrast administration. MR spectroscopy was helpful in supporting a preoperative diagnosis of meningioma in both cases.

Adult↗
PubMed1998

Intraventricular ganglioglioma: case report.

We report a case of ganglioglioma located in the body and frontal horn of the right lateral ventricle. The lesion was found in a 71-year-old man who suffered from recurrent episodes of right hemicranial headache. CT and MRI showed a heterogeneous intraventricular tumour with multiple microcysts that suggested a diagnosis of subependymoma or central neurocytoma, but the pathological findings were consistent with ganglioglioma. Although the most frequent intracranial location of ganglioglioma is the temporal lobe, any location in the central nervous system may be seen. Nevertheless an exclusively intraventricular location is exceptional. To our knowledge this is the first complete radiological report of a purely intraventricular ganglioglioma.

Aged↗
PubMed1998

Imaging of giant pituitary adenomas.

We present five proven giant pituitary adenomas studied by CT and MRI, and review the clinical and imaging findings. Our aim was to examine the radiologic appearances and to search for criteria useful in distinguishing these tumors from other sellar and suprasellar tumours, mainly craniopharyngioma. The main differences from small adenomas were high prevalence of macrocysts, a more invasive behaviour and a clinical picture dominated by mass effect rather than endocrine disturbance. Factors supporting the diagnosis of pituitary adenoma in a giant intra- and suprasellar mass include: infrasellar extension, absence of calcification and presence of low-signal cysts on T1-weighted images.

Adenoma↗
PubMed1998

POEMS syndrome: unusual radiographic, scintigraphic and CT features.

POEMS syndrome is a multisystemic disorder related to a plasma cell dyscrasia. Radiologically, this syndrome is characterized by sclerotic focal bone lesions with a normal radionuclide bone scan. We report a case of POEMS syndrome with an expansile lytic lesion in the sternum showing periosteal reaction and soft tissue mass, which revealed locally increased uptake of radiotracer in bone scintigraphy. These unusual findings and the differential diagnosis are discussed.

Biopsy, Needle↗