Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours.

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.

Metabolite selection for machine learning in childhood brain tumour classification.

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.

Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study.

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.

Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1 H-MR spectroscopy-A multi-center study.

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.

Management of paediatric high-grade blunt renal trauma: a 10-year single-centre UK experience.

OBJECTIVE: To report the management and outcome of paediatric patients sustaining high-grade blunt renal trauma. PATIENTS AND METHODS: Medical records were examined for all American Association for the Surgery of Trauma (AAST) grade III-V blunt renal trauma cases admitted to a paediatric trauma centre from 2005 to 2015. Data collected and analysed included: demographics, imaging modalities, management, length of hospital stay (LOS), complications, and follow-up outcomes. RESULTS: In all, 18 children (12 boys, six girls) with mean (range) age 11 (4-15) years were included. According to the AAST grading criteria, 39% (seven of 18) of the patients had grade III, 50% (nine of 18) grade IV, and 11% (two of 18) grade V injuries; 44% (eight of 18) had concomitant injuries. Most of the patients were managed conservatively (89%, 16 of 18), although two of the 16 subsequently needed JJ-stent insertion during inpatient stay for symptomatic urinoma(s). In all, 11% (two of 18) of the patients required interventional radiology service(s), involving selective embolisation for life-threatening renal tract haemorrhage. Blood transfusion for renal injury exclusively was required in 11% (two of 18) of the patients. In all, 89% (16 of 18) of the patients had at least one follow-up imaging study before hospital discharge; most (13 of 16) had ultrasonography and three required computed tomography. The median (range) LOS was 11 (4-31) days. In all, 17% (three of 18) of the patients required hospital re-admission within 30 days for complications and all required interventional procedures: JJ stent for urinoma (one), embolisation of renal arterio-venous fistula (one), and embolisation for a post-traumatic pseudoaneurysm (one). Overall, the median (range) follow-up was 6 (2-60) months. In all, 78% (14 of 18) of the patients had dimercaptosuccinic acid studies, with 11 showing reductions in renal function (range 3-44%). CONCLUSIONS: This study supports a care pathway strategy advocating conservative management of high-grade renal injuries in children. However, patients may experience a relative decline in renal function with higher grade injuries indicating the need for monitoring and follow-up.

The impact of intraoperative magnetic resonance in routine pediatric neurosurgical practice-a 6-year appraisal.

BACKGROUND: The intraoperative magnetic resonance scanner (ioMR) was introduced in our unit in 2009, and has been used routinely since then. OBJECTIVE: This study aims to describe indications, radiological features, and clinical outcomes of the patients operated on with ioMRI and analyze our experience. METHODS: A retrospective analysis of a prospective surgical database has been performed, including surgical procedure, intent, radiological reports, need for second-look surgery, and complications, supplemented by further review of the clinical notes and the scans. RESULTS: From 2009 to 2015, 255 surgical procedures with ioMR were performed: 175 were craniotomies for tumor excision, 65 were epilepsy related, and 15 were biopsies or cyst drainages. The mean age was 9.4 years. One ioMR was performed in 79.5% patients; the mean duration of the MR was 41 min. In 172 cases (67.4%), no actions followed the ioMR. When the aim of the surgery was debulking of the tumor, the percentage of patients in which the ioMR was followed by resection was higher than when complete resection was the aim (56 vs 27.5%). The complication rate was not increased when compared with our previous results (infection 1%, neurological deficits 12%). CONCLUSION: This is the largest published series of ioMRI-aided pediatric neurosurgery to date. We have demonstrated that it can be used safely and routinely in pediatric neurosurgical procedures at any age, assisting the surgeon in achieving the best extent of resection and aiding in intra-operative decision-making for tumor- and non-tumor-related intracranial pathology.

Effect of thyroxine on brain microstructure in extremely premature babies: magnetic resonance imaging findings in the TIPIT study.

BACKGROUND: In order to assess relationships between thyroid hormone status and findings on brain MRI, a subset of babies was recruited to a multi-centre randomised, placebo-controlled trial of levothyroxine (LT4) supplementation for babies born before 28 weeks' gestation (known as the TIPIT study, for Thyroxine supplementation In Preterm InfanTs). These infants were imaged at term-equivalence. MATERIALS AND METHODS: Forty-five TIPIT participants had brain MRI using diffusion tensor imaging (DTI) to estimate white matter development by apparent diffusion coefficient (ADC), fractional anisotropy (FA) and tractography metrics of number and length of streamlines. We made comparisons between babies with the lowest and highest plasma FT4 concentrations during the initial 4 weeks after birth. RESULTS: There were no differences in DTI metrics between babies who had received LT4 supplementation and those who had received a placebo. Among recipients of a placebo, babies in the lowest quartile of plasma-free thyroxine (FT4) concentrations had significantly higher apparent diffusion coefficient measurements in the posterior corpus callosum and streamlines that were shorter and less numerous in the right internal capsule. Among LT4-supplemented babies, those who had plasma FT4 concentrations in the highest quartile had significantly lower apparent diffusion coefficient values in the left occipital lobe, higher fractional anisotropy in the anterior corpus callosum and longer and more numerous streamlines in the anterior corpus callosum. CONCLUSION: DTI variables were not associated with allocation of placebo or thyroid supplementation. Markers of poorly organised brain microstructure were associated with low plasma FT4 concentrations after birth. The findings suggest that plasma FT4 concentrations affect brain development in very immature infants and that the effect of LT4 supplementation for immature babies with low FT4 plasma concentrations warrants further study.

Association between Noonan syndrome and Chiari I malformation: a case-based update.

BACKGROUND: Chiari I malformations (CM-I) have been associated with a variety of developmental abnormalities in the literature. A few cases of CM-I in patients with Noonan syndrome (NS) have been reported; however, opinion remains divided as to whether the observed association is coincidental. DISCUSSION: Six previous cases of CM-I in patients with NS have been described in the literature. Many of these had other neurological abnormalities; however, neurological problems are not a prominent part of earlier descriptions of NS. A statistically significant association between NS and CM-I is difficult to obtain at present due to availability and logistical issues with scanning many asymptomatic patients. Although we believe a link exists between CM-I and NS, there is little understanding on how NS may cause CM-I. The most logical cause would be posterior fossa abnormality; however, the most common genetic mutation in NS tends to cause frontal and facial abnormalities, and the posterior fossa tends to be relatively spared. Other genetic mutations may also affect the posterior cranium and thus create the appropriate conditions for a CM-I to develop. ILLUSTRATIVE CASE: We report a case of CM-I in a 9-year-old patient with Noonan syndrome, severe scoliosis and syringomyelia. CONCLUSION: We believe that CM-I is a part of Noonan syndrome; however, statistical validation of this opinion is necessary.

High field strength magnetic resonance imaging in paediatric brain tumour surgery--its role in prevention of early repeat resections.

PURPOSE: The purpose of this study is to compare the surgical and imaging outcome in children who underwent brain tumour surgery with intention of complete tumour resection, prior to and following the start of intra-operative MRI (ioMRI) service. METHODS: ioMRI service for brain tumour resection commenced in October 2009. A cohort of patients operated between June 2007 and September 2009 with a pre-surgical intention of complete tumour resection were selected (Group A). A similar number of consecutive cases were selected from a prospective database of patients undergoing ioMRI (Group B). The demographics, imaging, pathology and surgical outcome of both groups were compared. RESULTS: Thirty-six of 47 cases from Group A met the inclusion criterion and 36 cases were selected from Group B; 7 of the 36 cases in Group A had unequivocal evidence of residual tumour on the post-operative scan; 5 (14%) of them underwent repeat resection within 6 months post-surgery. In Group B, ioMRI revealed unequivocal evidence of residual tumour in 11 of the 36 cases following initial resection. In 10 of these 11 cases, repeat resections were performed during the same surgical episode and none of these 11 cases required repeat surgery in the following 6 months. Early repeat resection rate was significantly different between both groups (p = 0.003). CONCLUSION: Following the advent of ioMRI at our institution, the need for repeat resection within 6 months has been prevented in cases where ioMRI revealed unequivocal evidence of residual tumour.

Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data.

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.

Intraoperative 3-Tesla MRI in the management of paediatric cranial tumours--initial experience.

BACKGROUND: Intraoperative MRI (ioMRI) has been gaining recognition because of its value in the neurosurgical management of cranial tumours. There is limited documentation of its value in children. OBJECTIVES: To review the initial experience of a paediatric 3-Tesla ioMRI unit in the management of cranial tumours. MATERIALS AND METHODS: Thirty-eight children underwent ioMRI during 40 cranial tumour resections using a 3-Tesla MR scanner co-located with the neurosurgical operating theatre. IoMRI was performed to assess the extent of tumour resection and/or to update neuronavigation. The intraoperative and follow-up scans, and the clinical records were reviewed. RESULTS: In 27/40 operations, complete resection was intended. IoMRI confirmed complete resection in 15/27 (56%). As a consequence, surgical resection was extended in 5/27 (19%). In 6/27 (22%), ioMRI was equivocal for residual tumour. In 13/40 (33%) operations, the surgical aim was to partially resect the tumour. In 7 of the 13 (54%), surgical resection was extended following ioMRI. CONCLUSION: In our initial experience, ioMRI has increased the rate of complete resection, with intraoperative surgical strategy being modified in 30% of procedures. Collaborative analysis of ioMRI by the radiologist and neurosurgeon is vital to avoid errors in interpretation.

Classification of paediatric brain tumours by diffusion weighted imaging and machine learning.

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.

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.

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.

Anaplastic ependymoma with sclerotic bone metastases.

Ependymomas are glial central nervous system (CNS) tumors that arise from the ependymal layer of brain and spinal cord. These are heterogeneous group of tumors with varied histopathological features and prognosis. They frequently relapse at the primary site and may disseminate to other CNS sites. Extraneural metastases are, however, extremely rare. We present a case of ependymoma in a child with widespread metastasis to her bones, a previously unreported event.

Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study.

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.

Magnetic resonance spectroscopy assessment of brain injury after moderate hypothermia in neonatal encephalopathy: a prospective multicentre cohort study.

BACKGROUND: In neonatal encephalopathy, the clinical manifestations of injury can only be reliably assessed several years after an intervention, complicating early prognostication and rendering trials of promising neuroprotectants slow and expensive. We aimed to determine the accuracy of thalamic proton magnetic resonance (MR) spectroscopy (MRS) biomarkers as early predictors of the neurodevelopmental abnormalities observed years after neonatal encephalopathy. METHODS: We did a prospective multicentre cohort study across eight neonatal intensive care units in the UK and USA, recruiting term and near-term neonates who received therapeutic hypothermia for neonatal encephalopathy. We excluded infants with life-threatening congenital malformations, syndromic disorders, neurometabolic diseases, or any alternative diagnoses for encephalopathy that were apparent within 6 h of birth. We obtained T1-weighted, T2-weighted, and diffusion-weighted MRI and thalamic proton MRS 4-14 days after birth. Clinical neurodevelopmental tests were done 18-24 months later. The primary outcome was the association between MR biomarkers and an adverse neurodevelopmental outcome, defined as death or moderate or severe disability, measured using a multivariable prognostic model. We used receiver operating characteristic (ROC) curves to examine the prognostic accuracy of the individual biomarkers. This trial is registered with ClinicalTrials.gov, number NCT01309711. FINDINGS: Between Jan 29, 2013, and June 25, 2016, we recruited 223 infants who all underwent MRI and MRS at a median age of 7 days (IQR 5-10), with 190 (85%) followed up for neurological examination at a median age of 23 months (20-25). Of those followed up, 31 (16%) had moderate or severe disability, including one death. Multiple logistic regression analysis could not be done because thalamic N-acetylaspartate (NAA) concentration alone accurately predicted an adverse neurodevelopmental outcome (area under the curve [AUC] of 0·99 [95% CI 0·94-1·00]; sensitivity 100% [74-100]; specificity 97% [90-100]; n=82); the models would not converge when any additional variable was examined. The AUC (95% CI) of clinical examination at 6 h (n=190) and at discharge (n=167) were 0·72 (0·65-0·78) and 0·60 (0·53-0·68), respectively, and the AUC of abnormal amplitude integrated EEG at 6 h (n=169) was 0·73 (0·65-0·79). On conventional MRI (n=190), cortical injury had an AUC of 0·67 (0·60-0·73), basal ganglia or thalamic injury had an AUC of 0·81 (0·75-0·87), and abnormal signal in the posterior limb of internal capsule (PLIC) had an AUC of 0·82 (0·76-0·87). Fractional anisotropy of PLIC (n=65) had an AUC of 0·82 (0·76-0·87). MRS metabolite peak-area ratios (n=160) of NAA-creatine (<1·29) had an AUC of 0·79 (0·72-0·85), of NAA-choline had an AUC of 0·74 (0·66-0·80), and of lactate-NAA (>0·22) had an AUC of 0·94 (0·89-0·97). INTERPRETATION: Thalamic proton MRS measures acquired soon after birth in neonatal encephalopathy had the highest accuracy to predict neurdevelopment 2 years later. These methods could be applied to increase the power of neuroprotection trials while reducing their duration. FUNDING: National Institute for Health Research UK.

TIPIT: a randomised controlled trial of thyroxine in preterm infants under 28 weeks gestation: magnetic resonance imaging and magnetic resonance angiography protocol.

BACKGROUND: Infants born at extreme prematurity are at high risk of developmental disability. A major risk factor for disability is having a low level of thyroid hormone described as hypothyroxinaemia, which is recognised to be a frequent phenomenon in these infants. Derangements of critical thyroid function during the sensitive window in prematurity when early development occurs, may have a range of long term effects for brain development. Further research in preterm infants using neuroimaging techniques will increase our understanding of the specificity of the effects of hypothyroxinaemia on the developing foetal brain. This is an explanatory double blinded randomised controlled trial which is aimed to assess the effect of thyroid hormone supplementation on brain size, key brain structures, extent of myelination, white matter integrity and vessel morphology, somatic growth and the hypothalamic-pituitary-adrenal axis. METHODS: The study is a multi-centred double blinded randomised controlled trial of thyroid hormone supplementation in babies born below 28 weeks' gestation. All infants will receive either levothyroxine or placebo until 32 weeks corrected gestational age. The primary outcomes will be width of the sub-arachnoid space measured using cranial ultrasound and head circumference at 36 weeks corrected gestational age. The secondary outcomes will be thyroid hormone concentrations, the hypothalamic pituitary axis status and auxological data between birth and expected date of delivery; thyroid gland volume, brain size, volumes of key brain structures, extent of myelination and brain vessel morphology at expected date of delivery and markers of morbidity which include duration of mechanical ventilation and/or oxygen requirement and chronic lung disease. Trial registration Current Controlled Trials ISRCTN89493983.

Quantitative measurement of blood flow in paediatric brain tumours-a comparative study of dynamic susceptibility contrast and multi time-point arterial spin labelled MRI.

OBJECTIVE: Arterial spin-labelling (ASL) MRI uses intrinsic blood water to quantify the cerebral blood flow (CBF), removing the need for the injection of a gadolinium-based contrast agent used for conventional perfusion imaging such as dynamic susceptibility contrast (DSC). Owing to the non-invasive nature of the technique, ASL is an attractive option for use in paediatric patients. This work compared DSC and multi-timepoint ASL measures of CBF in paediatric brain tumours. METHODS: Patients (n = 23; 20 low-grade tumours and 3 high-grade tumours) had DSC and multi-timepoint ASL with and without vascular crushers (VC). VC removes the contribution from larger vessel blood flow. Mean perfusion metrics were extracted from control and T1-enhanced tumour regions of interest (ROIs): arterial arrival time (AAT) and CBF from the ASL images with and without VC, relative cerebral blood flow (rCBF), relative cerebral blood volume, delay time (DT) and mean transit time (MTT) from the DSC images. RESULTS: Significant correlations existed for: AAT and DT (r = 0.77, p = 0.0002) and CBF and rCBF (r = 0.56, p = 0.02) in control ROIs for ASL-noVC. No significant correlations existed between DSC and ASL measures in the tumour region. Significant differences between control and tumour ROI were found for MTT (p < 0.001) and rCBF (p < 0.005) measures. CONCLUSION: Significant correlations between ASL-noVC and DSC measures in the normal brain suggest that DSC is most sensitive to macrovascular blood flow. The absence of significant correlations within the tumour ROI suggests that ASL is sensitive to different physiological mechanisms compared with DSC measures. ADVANCES IN KNOWLEDGE: ASL provides information which is comparable with that of DSC in healthy tissues, but appears to reflect a different physiology in tumour tissues.

Diffusion abnormalities on intraoperative magnetic resonance imaging as an early predictor for the risk of posterior fossa syndrome.

BACKGROUND: Posterior fossa syndrome (PFS) is an important complication of posterior fossa surgery in children. The pathophysiology of this condition remains unclear, but there is evidence implicating surgical injury of the proximal efferent cerebellar pathway (pECP) and the cerebellar vermis to PFS. We aimed to evaluate if diffusion abnormalities involving these structures on the final intraoperative MRI can predict the development of PFS. METHODS: Diffusion-weighted imaging from 31 posterior fossa resections were anonymized and evaluated for abnormalities involving the dentate nucleus, superior cerebellar peduncle, and the mesencephalic tegmentum forming the pECP, vermis, and middle cerebellar peduncle. The case notes were independently evaluated for evidence of PFS. RESULTS: The diffusion imaging in 28 cases was of optimal quality for evaluation. Diffusion abnormalities were identified in 10 cases, 7 of which involved the pECP. Retrospective evaluation revealed evidence of PFS in 6 cases. There was a significant association between abnormalities involving pECP structures (P = .001) and development of PFS. Bilateral involvement of pECP (P = .006) was a highly specific risk factor for predicting the development of PFS. Diffusion abnormality of the inferior vermis was significantly associated with PFS (P = .001) but may not represent a risk factor in isolation. CONCLUSION: This study demonstrates the feasibility of identifying children at risk for developing PFS at the earliest stage post tumor resection and thus adds to the growing evidence base on its pathophysiology.