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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 mm 2  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.

Perivascular spaces (PVS) are fluid filled compartments surrounding the small blood vessels in the brain. The impact of radiotherapy and chemotherapy on PVS remains unclear. The aim of this study is to investigate treatment effects of radiotherapy and chemotherapy at four time points (TPs) in pediatric medulloblastoma (MB) patients. We examined 778 scans from 241 MB patients at baseline (0M), after 12 weeks (about 3 months) of radiotherapy and rest (3M), after chemotherapy completion (12M), and a follow-up (FollowUp) at 18- or 21-months post-baseline. PVS was segmented by applying Frangi filter on the white matter regions on T1 weighted images acquired at 3T Siemens MRI scanner using MPRAGE. PVS volume and ratio, defined as the ratio of PVS volume to the white matter volume, were measured at the four TPs. The data was first statistically analyzed using a full model where all data were included, then a paired model, which included only patients who completed consecutive measurements under the same anesthesia and shunt conditions. Both the full model and paired model showed that PVS (including ratio and volume) increased at 3M post-radiotherapy compared to baseline. During chemotherapy, PVS decreased significantly from 3M to 12M. Subsequently, from 12M to FollowUp, PVS increased again. MRI exams under anesthesia exhibited significantly lower PVS than those without anesthesia. Patients who had undergone a shunt procedure exhibited a significantly reduced PVS compared to those who had not undergone the procedure. We concluded that craniospinal irradiation led to an elevated PVS. Conversely, chemotherapy or time post-irradiation decreased PVS. Anesthesia and shunt procedures can also influence perivascular space ratio or volume.

Medulloblastoma (MB) is a highly malignant pediatric brain tumor. In the latest classification, medulloblastoma is divided into four distinct groups: wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We analyzed the magnetic resonance imaging radiomics features to find the imaging surrogates of the 4 molecular subgroups of MB. Frozen tissue, imaging data, and clinical data of 38 patients with medulloblastoma were included from Taipei Medical University Hospital and Taipei Veterans General Hospital. Molecular clustering was performed based on the gene expression level of 22 subgroup-specific signature genes. A total 253 magnetic resonance imaging radiomic features were generated from each subject for comparison between different molecular subgroups. Our cohort consisted of 7 (18.4%) patients with WNT medulloblastoma, 12 (31.6%) with SHH tumor, 8 (21.1%) with Group 3 tumor, and 11 (28.9%) with Group 4 tumor. 8 radiomics gray-level co-occurrence matrix texture (GLCM) features were significantly different between 4 molecular subgroups of MB. In addition, for tumors with higher values in a gray-level run length matrix feature-Short Run Low Gray-Level Emphasis, patients have shorter survival times than patients with low values of this feature (p = 0.04). The receiver operating characteristic analysis revealed optimal performance of the preliminary prediction model based on GLCM features for predicting WNT, Group 3, and Group 4 MB (area under the curve = 0.82, 0.72, and 0.78, respectively). The preliminary result revealed that 8 contrast-enhanced T1-weighted imaging texture features were significantly different between 4 molecular subgroups of MB. Together with the prediction models, the radiomics features may provide suggestions for stratifying patients with MB into different risk groups.

Objectives To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs. Methods Data from 217 medulloblastoma (MB) patients over the past 4 years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan–Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model. Results Higher Rad-scores were significantly associated with worse OS in both the training and validation sets ( p  < 0.001 and p  = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning–based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively. Conclusions A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs. Clinical relevance statement A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time. Key Points • A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma . • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time . • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine .

Cerebellar mutism syndrome (CMS) is a well-known complication of posterior fossa (PF) tumour surgery. CMS has previously been reported in cases of non-tumour surgical aetiology in a limited number of publications. We report a case of a 10-year-old girl who suffered a cerebellar haemorrhage and subsequent CMS following surgical treatment of a ruptured arteriovenous malformation (AVM) in the cerebellar vermis. The AVM was removed acutely through a transvermian access, and hydrocephalus was treated with temporary external drainage. In the postoperative period, she suffered diffuse vasospasms of the anterior cerebral circulation and had a permanent shunt placed for hydrocephalus. Her mutism resolved after 45 days but severe ataxia persisted. To our knowledge, this is the first reported case of CMS related to a vermian haemorrhagic stroke with postoperative diffuse vasospasms. Based on this case, we present a literature review on CMS of non-tumour surgical origin in children.

BackgroundThis meta-analysis aims to assess the diagnostic performance of artificial intelligence (AI) based on magnetic resonance imaging (MRI) in detecting molecular subtypes of pediatric medulloblastoma (MB) in children.MethodsA thorough review of the literature was performed using PubMed, Embase, and Web of Science to locate pertinent studies released prior to October 2024. Selected studies focused on the diagnostic performance of AI based on MRI in detecting molecular subtypes of pediatric MB. A bivariate random-effects model was used to calculate pooled sensitivity and specificity, both with 95% confidence intervals (CI). Study heterogeneity was assessed using I2 statistics.ResultsAmong the 540 studies determined, eight studies (involving 1195 patients) were included. For the wingless (WNT), the combined sensitivity, specificity, and receiver operating characteristic curve (AUC) based on MRI were 0.73 (95% CI: 0.61–0.83, I2 = 19%), 0.94 (95% CI: 0.79–0.99, I2 = 93%), and 0.80 (95% CI: 0.77–0.83), respectively. For the sonic hedgehog (SHH), the combined sensitivity, specificity, and AUC were 0.64 (95% CI: 0.51–0.75, I2 = 69%), 0.84 (95% CI: 0.80–0.88, I2 = 54%), and 0.85 (95% CI: 0.81–0.88), respectively. For Group 3 (G3), the combined sensitivity, specificity, and AUC were 0.89 (95% CI: 0.52–0.98, I2 = 82%), 0.70 (95% CI: 0.62–0.77, I2 = 44%), and 0.88 (95% CI: 0.84–0.90), respectively. For Group 4 (G4), the combined sensitivity, specificity, and AUC were 0.77 (95% CI: 0.64–0.87, I2 = 54%), 0.91 (95% CI: 0.68–0.98, I2 = 80%), and 0.86 (95% CI: 0.83–0.89), respectively.ConclusionsMRI-based artificial intelligence shows high diagnostic performance in detecting molecular subtypes of pediatric MB. However, all included studies employed retrospective designs, which may introduce potential biases. More researches using external validation datasets are needed to confirm the results and assess their clinical applicability.

Purpose We aimed to evaluate the effect of deep learning-based reconstruction (DLR) on high-spatial-resolution three-dimensional T2-weighted fast asymmetric spin-echo (HR-3D T2-FASE) imaging in the preoperative evaluation of cerebellopontine angle (CPA) tumors. Methods This study included 13 consecutive patients who underwent preoperative HR-3D T2-FASE imaging using a 3 T MRI scanner. The reconstruction voxel size of HR-3D T2-FASE imaging was 0.23 × 0.23 × 0.5 mm. The contrast-to-noise ratios (CNRs) of the structures were compared between HR-3D T2-FASE images with and without DLR. The observers’ preferences based on four categories on the tumor side on HR-3D T2-FASE images were evaluated. The facial nerve in relation to the tumor on HR-3D T2-FASE images was assessed with reference to intraoperative findings. Results The mean CNR between the tumor and trigeminal nerve and between the cerebrospinal fluid and trigeminal nerve was significantly higher for DLR images than non-DLR-based images (14.3 ± 8.9 vs. 12.0 ± 7.6, and 66.4 ± 12.0 vs. 53.9 ± 8.5, P  < 0.001, respectively). The observer’s preference for the depiction and delineation of the tumor, cranial nerves, vessels, and location relation on DLR HR-3D T2FASE images was superior to that on non-DLR HR-3D T2FASE images in 7 (54%), 6 (46%), 6 (46%), and 6 (46%) of 13 cases, respectively. The facial nerves around the tumor on HR-3D T2-FASE images were visualized accurately in five (38%) cases with DLR and in four (31%) without DLR. Conclusion DLR HR-3D T2-FASE imaging is useful for the preoperative assessment of CPA tumors.

Pediatric posterior fossa (PF) tumor survivors experience long-term motor deficits. Specific cerebrocerebellar connections may be involved in incidence and severity of motor dysfunction. We examined the relationship between long-term ataxia as well as fine motor function and alteration of differential cerebellar efferent and afferent pathways using diffusion tensor imaging (DTI) and tractography. DTI-based tractography was performed in 19 patients (10 pilocytic astrocytoma (PA) and 9 medulloblastoma patients (MB)) and 20 healthy peers. Efferent Cerebello-Thalamo-Cerebral (CTC) and afferent Cerebro-Ponto-Cerebellar (CPC) tracts were reconstructed and analyzed concerning fractional anisotropy (FA) and volumetric measurements. Clinical outcome was assessed with the International Cooperative Ataxia Rating Scale (ICARS). Kinematic parameters of fine motor function (speed, automation, variability, and pressure) were obtained by employing a digitizing graphic tablet. ICARS scores were significantly higher in MB patients than in PA patients. Poorer ICARS scores and impaired fine motor function correlated significantly with volume loss of CTC pathway in MB patients, but not in PA patients. Patients with pediatric post-operative cerebellar mutism syndrome showed higher loss of CTC pathway volume and were more atactic. CPC pathway volume was significantly reduced in PA patients, but not in MB patients. Neither relationship was observed between the CPC pathway and ICARS or fine motor function. There was no group difference of FA values between the patients and healthy peers. Reduced CTC pathway volumes in our cohorts were associated with severity of long-term ataxia and impaired fine motor function in survivors of MBs. We suggest that the CTC pathway seems to play a role in extent of ataxia and fine motor dysfunction after childhood cerebellar tumor treatment. DTI may be a useful tool to identify relevant structures of the CTC pathway and possibly avoid surgically induced long-term neurological sequelae.