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The fifth edition of the World Health Organization Classification of Tumours of the Central Nervous System (WHO CNS5) published in 2021 builds on the 2016 edition and incorporates output from the Consortium to Inform Molecular and Practical Approaches to CNS Tumour Taxonomy (cIMPACT-NOW). WHO CNS5 introduces fundamental changes to brain tumour classification through the introduction of new tumour families and types, especially in the paediatric population, and a revision of diagnostic criteria for some of the existing neoplasms. Neuroradiologists are central to brain tumour diagnostics, and it is therefore essential that they become familiar with the key updates. This review aims to summarise the most relevant updates for the neuroradiologist and, where available, discuss the known radiophenotypes of various new tumour types to allow for increased accuracy of language and diagnosis. Of particular importance, WHO CNS5 places greater emphasis on organising tumours by molecular type to reflect biology, as well as to allow for better planning of treatment. The principal updates in adult tumours concern the molecular definition of glioblastoma, restructuring of diffuse gliomas, and the introduction of several new tumour types. The updates to the paediatric classification are protean, ranging from the introduction of new types to establishing separate tumour families for paediatric-type gliomas. This review summarises the most significant revisions and captures the rationale and radiological implications for the major updates.

The use of standardized imaging protocols is paramount in order to facilitate comparable, reproducible images and, consequently, to optimize patient care. Standardized MR protocols are lacking when studying head and neck pathologies in the pediatric population. We propose an international, multicenter consensus paper focused on providing the best combination of acquisition time/technical requirements and image quality. Distinct protocols for different regions of the head and neck and, in some cases, for specific pathologies or clinical indications are recommended. This white paper is endorsed by several international scientific societies and it is the result of discussion, in consensus, among experts in pediatric head and neck imaging.

Background Postoperative pediatric cerebellar mutism syndrome (pCMS) is a common but severe complication that may arise following the resection of posterior fossa tumors in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalization of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN). Methods An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset was collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumors. The ANN was trained on this dataset and its performance was evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. The area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed-rank test, with P Results Two hundred and four children were included, of whom 80 developed pCMS. The performance of the ANN (AUC 0.949; accuracy 90.9%) exceeded that of logistic regression (P Conclusion Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods.

BACKGROUND: Postoperative paediatric cerebellar mutism syndrome (pCMS) is a common but severe complication which may arise following the resection of posterior fossa tumours in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalisation of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN). METHODS: An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumours. The ANN was trained on this dataset and its performance evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. Area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed rank test, with p

BACKGROUND: The CNS neuroblastoma with forkhead box R2 (CNS NB-FOXR2) activation is one of the new embryonal tumor entities in the WHO CNS tumor classification 2021. The data currently available suggest a comparably favourable outcome after craniospinal irradiation and chemotherapy. The entity is poorly characterized on imaging. We therefore aimed to describe imaging features in a multi-center patient cohort. MATERIAL AND METHODS: Molecularly and histologically confirmed CNS NB-FOXR2 tumors were retrospectively collected at six pediatric oncology centers. The imaging features on preoperative MRI and CT data were recorded by 8 experienced pediatric (neuro)radiologists in consensus. RESULTS: Twenty-five patients were identified (13 females). The majority of tumors were large at presentation (mean 115 ± SD 83 ml), showed mostly none or little perifocal edema, enhanced only partly, were often calcified and/or hemorrhagic, were always T2w hyperintense to gray matter, and often had cystic and/or necrotic components. The min. mean ADC values were relatively low (mean 687.8 ± SD 136.3 in 10-6mm2/s). The tumors originated never infratentorial and were never centered in the midline. Metastases were rare. CONCLUSIONS: For the first time, imaging characteristics of the recently described CNS NB-FOXR2 are described in a multi-center cohort. The results of this study may assist in diagnosing these tumors preoperatively. Collaborative pooling of cases is important to gain insight into typical imaging features of rare tumors, especially in view of the increasing molecular-driven subtyping.