RESUMEN
Proton magnetic resonance spectroscopy (1H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1H-MRS may have better diagnostic performance for paediatric brain tumours.
Asunto(s)
Neoplasias Encefálicas , Espectroscopía de Protones por Resonancia Magnética , Relación Señal-Ruido , Humanos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Neoplasias Encefálicas/metabolismo , Niño , Espectroscopía de Protones por Resonancia Magnética/métodos , Femenino , Masculino , Preescolar , Adolescente , Estudios Retrospectivos , LactanteRESUMEN
1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.
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Biomarcadores de Tumor , Neoplasias Encefálicas , Humanos , Niño , Biomarcadores de Tumor/metabolismo , Neoplasias Encefálicas/metabolismo , Espectroscopía de Resonancia Magnética/métodos , Imagen por Resonancia MagnéticaRESUMEN
1 H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation.
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Neoplasias Cerebelosas/diagnóstico , Espectroscopía de Resonancia Magnética/métodos , Adolescente , Neoplasias Cerebelosas/patología , Niño , Preescolar , Sistemas de Apoyo a Decisiones Clínicas , Femenino , Humanos , Lactante , Imagen por Resonancia Magnética , Masculino , Estudios ProspectivosRESUMEN
MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC=0.81±0.01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC=0.78±0.01 ) and total creatine (P < 0.05, AUC=0.77±0.01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC=0.79±0.01 ), total N-acetylaspartate (P < 0.05, AUC=0.79±0.01 ) and total choline (P < 0.05, AUC=0.75±0.01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.
Asunto(s)
Neoplasias Encefálicas , Ependimoma , Neoplasias Encefálicas/metabolismo , Humanos , Aprendizaje Automático , Estudios Retrospectivos , Máquina de Vectores de SoporteRESUMEN
BACKGROUND: Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice. OBJECTIVE: We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors. MATERIALS AND METHODS: Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBVuncorr) and corrected (rCBVcorr) relative cerebral blood volume, and the leakage parameter, K2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types. RESULTS: Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBVuncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. -0.14±5.55; P<0.01). Average median rCBV increased following leakage correction (2.54±1.63 vs. 1.68±1.36; P=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBVuncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference. CONCLUSION: Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children's brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors.
Asunto(s)
Astrocitoma , Neoplasias Encefálicas , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Volumen Sanguíneo Cerebral , Niño , Medios de Contraste , Humanos , Imagen por Resonancia Magnética/métodosRESUMEN
PURPOSE: 3T magnetic resonance scanners have boosted clinical application of 1 H-MR spectroscopy (MRS) by offering an improved signal-to-noise ratio and increased spectral resolution, thereby identifying more metabolites and extending the range of metabolic information. Spectroscopic data from clinical 1.5T MR scanners has been shown to discriminate between pediatric brain tumors by applying machine learning techniques to further aid diagnosis. The purpose of this multi-center study was to investigate the discriminative potential of metabolite profiles obtained from 3T scanners in classifying pediatric brain tumors. METHODS: A total of 41 pediatric patients with brain tumors (17 medulloblastomas, 20 pilocytic astrocytomas, and 4 ependymomas) were scanned across four different hospitals. Raw spectroscopy data were processed using TARQUIN. Borderline synthetic minority oversampling technique was used to correct for the data skewness. Different classifiers were trained using linear discriminative analysis, support vector machine, and random forest techniques. RESULTS: Support vector machine had the highest balanced accuracy for discriminating the three tumor types. The balanced accuracy achieved was higher than the balanced accuracy previously reported for similar multi-center dataset from 1.5T magnets with echo time 20 to 32 ms alone. CONCLUSION: This study showed that 3T MRS can detect key differences in metabolite profiles for the main types of childhood tumors. Magn Reson Med 79:2359-2366, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Neoplasias Encefálicas/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética , Reconocimiento de Normas Patrones Automatizadas , Adolescente , Algoritmos , Astrocitoma/diagnóstico por imagen , Niño , Análisis por Conglomerados , Diagnóstico por Computador , Ependimoma/diagnóstico por imagen , Femenino , Humanos , Imagenología Tridimensional , Aprendizaje Automático , Espectroscopía de Resonancia Magnética , Masculino , Meduloblastoma/diagnóstico por imagen , Pediatría/métodos , Análisis de Componente Principal , Reproducibilidad de los Resultados , Relación Señal-Ruido , Máquina de Vectores de Soporte , Adulto JovenRESUMEN
AIMS: Metabolite levels can be measured non-invasively using in vivo 1H magnetic resonance spectroscopy (MRS). These tumour metabolite profiles are highly characteristic for tumour type in childhood brain tumours; however, the relationship between metabolite values and conventional histopathological characteristics has not yet been fully established. This study systematically tests the relationship between metabolite levels detected by MRS and specific histological features in a range of paediatric brain tumours. METHODS: Single-voxel MRS was performed routinely in children with brain tumours along with the clinical imaging prior to treatment. Metabolites were quantified using LCModel. Histological features were assessed semi-quantitatively for 27 children on H&E and immunostained slides, blind to the metabolite values. Statistical analysis included 2-tailed independent-samples t tests and 2-tailed Spearman rank correlation tests. RESULTS: Ki67, cellular atypia, and mitosis correlated positively with choline metabolites, and phosphocholine in particular. Apoptosis and necrosis were both associated with lipid levels, with the relationship dependent on the use of long or short echo time MRS acquisitions. Neuronal components correlated negatively and glial components positively with N-acetyl-aspartate. Glial components correlated positively with myoinositol. CONCLUSION: Metabolite levels in children's brain tumours measured by MRS are closely associated with key histological features routinely assessed by histopathologists in the diagnostic process. This further elucidates our understanding of this important non-invasive diagnostic tool and strengthens our understanding of the relationship between metabolites and histological features.
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Biomarcadores de Tumor/análisis , Neoplasias Encefálicas/metabolismo , Apoptosis , Encéfalo/diagnóstico por imagen , Encéfalo/metabolismo , Encéfalo/patología , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Niño , Humanos , Antígeno Ki-67/análisis , Espectroscopía de Resonancia Magnética , Necrosis , Coloración y EtiquetadoRESUMEN
BACKGROUND: A tool for diagnosing childhood cerebellar tumours using magnetic resonance (MR) spectroscopy peak height measurement has been developed based on retrospective analysis of single-centre data. OBJECTIVE: To determine the diagnostic accuracy of the peak height measurement tool in a multicentre prospective study, and optimise it by adding new prospective data to the original dataset. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) and single-voxel MR spectroscopy were performed on children with cerebellar tumours at three centres. Spectra were processed using standard scanner software and peak heights for N-acetyl aspartate, creatine, total choline and myo-inositol were measured. The original diagnostic tool was used to classify 26 new tumours as pilocytic astrocytoma, medulloblastoma or ependymoma. These spectra were subsequently combined with the original dataset to develop an optimised scheme from 53 tumours in total. RESULTS: Of the pilocytic astrocytomas, medulloblastomas and ependymomas, 65.4% were correctly assigned using the original tool. An optimized scheme was produced from the combined dataset correctly assigning 90.6%. Rare tumour types showed distinctive MR spectroscopy features. CONCLUSION: The original diagnostic tool gave modest accuracy when tested prospectively on multicentre data. Increasing the dataset provided a diagnostic tool based on MR spectroscopy peak height measurement with high levels of accuracy for multicentre data.
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Neoplasias Cerebelosas/diagnóstico por imagen , Espectroscopía de Resonancia Magnética/métodos , Biomarcadores de Tumor/metabolismo , Neoplasias Cerebelosas/metabolismo , Niño , Diagnóstico Diferencial , Femenino , Humanos , Interpretación de Imagen Asistida por Computador , Masculino , Estudios ProspectivosRESUMEN
OBJECTIVES: This UK-wide study defines the natural history of argininosuccinic aciduria and compares long-term neurological outcomes in patients presenting clinically or treated prospectively from birth with ammonia-lowering drugs. METHODS: Retrospective analysis of medical records prior to March 2013, then prospective analysis until December 2015. Blinded review of brain MRIs. ASL genotyping. RESULTS: Fifty-six patients were defined as early-onset (n = 23) if symptomatic < 28 days of age, late-onset (n = 23) if symptomatic later, or selectively screened perinatally due to a familial proband (n = 10). The median follow-up was 12.4 years (range 0-53). Long-term outcomes in all groups showed a similar neurological phenotype including developmental delay (48/52), epilepsy (24/52), ataxia (9/52), myopathy-like symptoms (6/52) and abnormal neuroimaging (12/21). Neuroimaging findings included parenchymal infarcts (4/21), focal white matter hyperintensity (4/21), cortical or cerebral atrophy (4/21), nodular heterotopia (2/21) and reduced creatine levels in white matter (4/4). 4/21 adult patients went to mainstream school without the need of additional educational support and 1/21 lives independently. Early-onset patients had more severe involvement of visceral organs including liver, kidney and gut. All early-onset and half of late-onset patients presented with hyperammonaemia. Screened patients had normal ammonia at birth and received treatment preventing severe hyperammonaemia. ASL was sequenced (n = 19) and 20 mutations were found. Plasma argininosuccinate was higher in early-onset compared to late-onset patients. CONCLUSIONS: Our study further defines the natural history of argininosuccinic aciduria and genotype-phenotype correlations. The neurological phenotype does not correlate with the severity of hyperammonaemia and plasma argininosuccinic acid levels. The disturbance in nitric oxide synthesis may be a contributor to the neurological disease. Clinical trials providing nitric oxide to the brain merit consideration.
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Aciduria Argininosuccínica/patología , Aciduria Argininosuccínica/terapia , Adolescente , Adulto , Amoníaco/metabolismo , Ácido Argininosuccínico/sangre , Aciduria Argininosuccínica/sangre , Aciduria Argininosuccínica/genética , Niño , Preescolar , Femenino , Estudios de Seguimiento , Genotipo , Humanos , Hiperamonemia/metabolismo , Hiperamonemia/patología , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Mutación/genética , Fenotipo , Estudios Prospectivos , Estudios Retrospectivos , Adulto JovenRESUMEN
PURPOSE: To investigate how arterial input functions (AIFs) vary with age in children and compare the use of individual and population AIFs for calculating gray matter CBV values. Quantitative measures of cerebral blood volume (CBV) using dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) require measurement of an AIF. AIFs are affected by numerous factors including patient age. Few data presenting AIFs in the pediatric population exists. MATERIALS AND METHODS: Twenty-two previously treated pediatric brain tumor patients (mean age, 6.3 years; range, 2.0-15.3 years) underwent DSC-MRI scans on a 3T MRI scanner over 36 visits. AIFs were measured in the middle cerebral artery. A functional form of an adult population AIF was fitted to each AIF to obtain parameters reflecting AIF shape. The relationship between parameters and age was assessed. Correlations between gray matter CBV values calculated using the resulting population and individual patient AIFs were explored. RESULTS: There was a large variation in individual patient AIFs but correlations between AIF shape and age were observed. The center (r = 0.596, P < 0.001) and width of the first-pass peak (r = 0.441, P = 0.007) were found to correlate significantly with age. Intrapatient coefficients of variation were significantly lower than interpatient values for all parameters (P < 0.001). Differences in CBV values calculated with an overall population and age-specific population AIF compared to those calculated with individual AIFs were 31.3% and 31.0%, respectively. CONCLUSION: Parameters describing AIF shape correlate with patient age in line with expected changes in cardiac output. In pediatric DSC-MRI studies individual patient AIFs are recommended.
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Neoplasias Encefálicas/diagnóstico por imagen , Encéfalo/irrigación sanguínea , Circulación Cerebrovascular , Sustancia Gris/patología , Imagen por Resonancia Magnética , Adolescente , Determinación del Volumen Sanguíneo , Encéfalo/diagnóstico por imagen , Neoplasias Encefálicas/patología , Niño , Preescolar , Sustancia Gris/diagnóstico por imagen , Humanos , Aumento de la Imagen/métodos , Lactante , Reproducibilidad de los ResultadosRESUMEN
AIM: Paediatric patients with drug-resistant focal epilepsy (DRFE) who have no clear focal lesion identified on conventional structural magnetic resonance imaging (MRI) are a particularly challenging cohort to treat and form an increasing part of epilepsy surgery programs. A recently developed deep-learning-based MRI lesion detection algorithm, the Multicentre Lesion Detection (MELD) algorithm, has been shown to aid detection of focal cortical dysplasia (FCD). We applied this algorithm retrospectively to a cohort of MRI-negative children with refractory focal epilepsy who underwent stereoelectroencephalography (SEEG) to determine its accuracy in identifying unseen epileptic lesions, seizure onset zones and clinical outcomes. METHODS: We retrospectively applied the MELD algorithm to a consecutive series of MRI-negative patients who underwent SEEG at our tertiary Paediatric Epilepsy Surgery centre. We assessed the extent to which the identified MELD cluster or lesion area corresponded with the clinical seizure hypothesis, the epileptic network, and the positron emission tomography (PET) focal hypometabolic area. In those who underwent resective surgery, we analysed whether the region of MELD abnormality corresponded with the surgical target and to what extent this was associated with seizure freedom. RESULTS: We identified 37 SEEG studies in 28 MRI-negative children in whom we could run the MELD algorithm. Of these, 14 (50â¯%) children had clusters identified on MELD. Nine (32â¯%) children had clusters concordant with seizure hypothesis, 6 (21â¯%) had clusters concordant with PET imaging, and 5 (18â¯%) children had at least one cluster concordant with SEEG electrode placement. Overall, 4 MELD clusters in 4 separate children correctly predicted either seizure onset zone or irritative zone based on SEEG stimulation data. Sixteen children (57â¯%) went on to have resective or lesional surgery. Of these, only one patient (4â¯%) had a MELD cluster which co-localised with the resection cavity and this child had an Engel 1â¯A outcome. CONCLUSIONS: In our paediatric cohort of MRI-negative patients with drug-resistant focal epilepsy, the MELD algorithm identified abnormal clusters or lesions in half of cases, and identified one radiologically occult focal cortical dysplasia. Machine-learning-based lesion detection is a promising area of research with the potential to improve seizure outcomes in this challenging cohort of radiologically occult FCD cases. However, its application should be approached with caution, especially with regards to its specificity in detecting FCD lesions, and there is still work to be done before it adds to diagnostic utility.
RESUMEN
BACKGROUND: The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification 'gold-standard', typically delivered 3-4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS). METHODS: Metabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival. FINDINGS: Group-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4-8.1, p = 0.025). INTERPRETATION: Tissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis. FUNDING: Children with Cancer UK, Cancer Research UK, Children's Cancer North and a Newcastle University PhD studentship.
Asunto(s)
Neoplasias Encefálicas , Neoplasias Cerebelosas , Meduloblastoma , Niño , Humanos , Masculino , Femenino , Meduloblastoma/diagnóstico , Meduloblastoma/genética , Meduloblastoma/metabolismo , Neoplasias Cerebelosas/diagnóstico , Glutamatos , Ácido gamma-Aminobutírico , ADNRESUMEN
Aicardi-Goutières syndrome (AGS) is an encephalopathy of early childhood which is most commonly inherited as an autosomal recessive trait. The disorder demonstrates significant genetic heterogeneity with causative mutations in five genes identified to date. Although most patients with AGS experience a severe neonatal or infantile presentation, poor neurodevelopmental outcome and reduced survival, clinical variability in the onset and severity of the condition is being increasingly recognized. A later presentation with a more variable effect on development, morbidity and mortality has been particularly observed in association with mutations in SAMHD1 and RNASEH2B. In contrast, the recurrent c.205C > T (p.R69W) RNASEH2C Asian founder mutation has previously only been identified in children with a severe AGS phenotype. Here, to our knowledge, we present the first report of marked phenotypic variability in siblings both harboring this founder mutation in the homozygous state. In this family, one female child had a severe AGS phenotype with an onset in infancy and profound developmental delay, whilst an older sister was of completely normal intellect with a normal head circumference and was only diagnosed because of the presence of chilblains and a mild hemiplegia. An appreciation of intrafamilial phenotypic expression is important in the counseling of families considering prenatal diagnosis, and may also be relevant to the assessment of efficacy in future clinical trials. In addition, marked phenotypic variation raises the possibility that more mildly affected patients are not currently identified.
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Enfermedades Autoinmunes del Sistema Nervioso/genética , Malformaciones del Sistema Nervioso/genética , Ribonucleasa H/genética , Anomalías Múltiples/genética , Encefalopatías/genética , Eritema Pernio/genética , Preescolar , Consanguinidad , Femenino , Efecto Fundador , Hemiplejía/genética , Humanos , Lactante , FenotipoRESUMEN
OBJECTIVE: Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. METHODS: 1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. RESULTS: Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. CONCLUSION: The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification. ADVANCES IN KNOWLEDGE: A new automated quality control method was developed, which trained machine learning classifiers using QR results.
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Aprendizaje Automático , Imagen por Resonancia Magnética , Humanos , Niño , Sensibilidad y Especificidad , Curva ROCRESUMEN
BACKGROUND: Magnetic resonance spectroscopy (MRS) has been successful in characterising a range of brain tumours and is a useful aid to non-invasive diagnosis. The pineal region poses considerable surgical challenges and a major surgical resection is not required in the management of all tumours. Improved non-invasive assessment of pineal region tumours would be of considerable benefit. METHODS: Single voxel MRS (TE 30 ms, TR 1500, 1.5 T) was performed on 15 pineal tumours: 5 germinomas, 1 non-germinomatous secreting germ cell tumour (GCT), 2 teratomas, 5 pineoblastomas, 1 pineal parenchymal tumour (PPT) of intermediate differentiation and 1 pineocytoma. Two germinomas outside the pineal gland were also studied. Metabolite, lipid and macromolecule concentrations were determined with LCModel™. RESULTS: Germ cell tumours had significantly higher lipid and macromolecule concentrations than other tumours (t-test; P < 0.05). The teratomas had significantly lower total choline and creatine levels than germinomas (z test; P < 0.05). Taurine was convincingly detected in germinomas as well as PPTs. CONCLUSIONS: Magnetic resonance spectroscopy is useful for characterising pineal region tumours, aiding the non-invasive diagnosis and giving additional biological insight.
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Neoplasias Encefálicas/diagnóstico , Glándula Pineal/patología , Pinealoma/diagnóstico , Neoplasias Encefálicas/cirugía , Niño , Humanos , Imagen por Resonancia Magnética , Resonancia Magnética Nuclear Biomolecular , Glándula Pineal/cirugía , Pinealoma/cirugía , Protones , Factores de TiempoRESUMEN
Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols.
Asunto(s)
Neoplasias Encefálicas/mortalidad , Encéfalo/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador , Aprendizaje Automático , Adolescente , Teorema de Bayes , Biopsia , Encéfalo/patología , Encéfalo/cirugía , Neoplasias Encefálicas/diagnóstico , Neoplasias Encefálicas/patología , Neoplasias Encefálicas/terapia , Niño , Preescolar , Imagen de Difusión por Resonancia Magnética , Femenino , Humanos , Lactante , Recién Nacido , Estimación de Kaplan-Meier , Angiografía por Resonancia Magnética , Masculino , Clasificación del Tumor , Medición de Riesgo/métodos , Análisis de SupervivenciaRESUMEN
To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10-3 mm2 s-1 with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.
Asunto(s)
Neoplasias Encefálicas/clasificación , Neoplasias Encefálicas/diagnóstico por imagen , Imagen de Difusión por Resonancia Magnética/métodos , Aprendizaje Automático , Adolescente , Astrocitoma/diagnóstico , Astrocitoma/diagnóstico por imagen , Astrocitoma/patología , Neoplasias Encefálicas/diagnóstico , Neoplasias Encefálicas/patología , Neoplasias Cerebelosas/diagnóstico , Neoplasias Cerebelosas/diagnóstico por imagen , Neoplasias Cerebelosas/patología , Niño , Preescolar , Imagen de Difusión por Resonancia Magnética/estadística & datos numéricos , Ependimoma/diagnóstico , Ependimoma/diagnóstico por imagen , Ependimoma/patología , Femenino , Humanos , Lactante , Masculino , Meduloblastoma/diagnóstico , Meduloblastoma/diagnóstico por imagen , Meduloblastoma/patología , Pediatría/normasRESUMEN
Phospholipase associated neurodegeneration (PLAN) comprises a heterogeneous group of autosomal recessive neurological disorders caused by mutations in the PLA2G6 gene. Direct gene sequencing detects approximately 85% mutations in infantile neuroaxonal dystrophy. We report the novel use of multiplex ligation-dependent probe amplification (MLPA) analysis to detect novel PLA2G6 duplications and deletions. The identification of such copy number variants (CNVs) expands the PLAN mutation spectrum and may account for up to 12.5% of PLA2G6 mutations. MLPA should thus be employed to detect CNVs of PLA2G6 in patients who show clinical features of PLAN but in whom both disease-causing mutations cannot be identified on routine sequencing.
Asunto(s)
Fosfolipasas A2 Grupo VI/genética , Técnicas de Amplificación de Ácido Nucleico/métodos , Secuencia de Bases , Encéfalo/patología , Preescolar , Consanguinidad , Eliminación de Gen , Duplicación de Gen , Trastornos Heredodegenerativos del Sistema Nervioso/diagnóstico , Trastornos Heredodegenerativos del Sistema Nervioso/genética , Humanos , Lactante , Imagen por Resonancia Magnética , Masculino , Datos de Secuencia Molecular , Mutación , Patología MolecularRESUMEN
The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.
Asunto(s)
Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Aprendizaje Automático , Imagen por Resonancia Magnética/métodos , Neuroimagen/métodos , Niño , Humanos , Clasificación del TumorRESUMEN
OBJECTIVE:: To investigate correlations between MRI perfusion metrics measured by dynamic susceptibility contrast and arterial spin labelling in paediatric brain tumours. METHODS:: 15 paediatric patients with brain tumours were scanned prospectively using pseudo-continuous arterial spin labelling (ASL) and dynamic susceptibility contrast (DSC-) MRI with a pre-bolus to minimise contrast agent leakage. Cerebral blood flow (CBF) maps were produced using ASL. Cerebral blood volume (CBV) maps with and without contrast agent leakage correction using the Boxerman technique and the leakage parameter, K2, were produced from the DSC data. Correlations between the metrics produced were investigated. RESULTS:: Histology resulted in the following diagnoses: pilocytic astrocytoma (n = 7), glioblastoma (n = 1), medulloblastoma (n = 1), rosette-forming glioneuronal tumour of fourth ventricle (n = 1), atypical choroid plexus papilloma (n = 1) and pilomyxoid astrocytoma (n = 1). Three patients had a non-invasive diagnosis of low-grade glioma. DSC CBV maps of T1-enhancing tumours were difficult to interpret without the leakage correction. CBV values obtained with and without leakage correction were significantly different (p < 0.01). A significant positive correlation was observed between ASL CBF and DSC CBV (r = 0.516, p = 0.049) which became stronger when leakage correction was applied (r = 0.728, p = 0.002). K2 values were variable across the group (mean = 0.35, range = -0.49 to 0.64). CONCLUSION:: CBV values from DSC obtained with and without leakage correction were significantly different. Large increases in CBV were observed following leakage correction in highly T1-enhancing tumours. DSC and ASL perfusion metrics were found to correlate significantly in a range of paediatric brain tumours. A stronger relationship between DSC and ASL was seen when leakage correction was applied to the DSC data. Leakage correction should be applied when analysing DSC data in enhancing paediatric brain tumours. ADVANCES IN KNOWLEDGE:: We have shown that leakage correction should be applied when investigating enhancing paediatric brain tumours using DSC-MRI. A stronger correlation was found between CBF derived from ASL and CBV derived from DSC when a leakage correction was employed.