Risk of stroke-specific mortality after radiotherapy in patients with primary brain tumours.

Introduction: Stroke is an established complication in cancer patients, amongst whom brain tumour patients have the highest risk of fatal stroke. Radiotherapy is an important treatment for brain tumours and is associated with increased risk of cerebrovascular disease. However, the impact of brain irradiation on stroke-related deaths in brain tumour patients is unknown, and the timing of any effect uncertain. This study investigates the relationship between radiotherapy and stroke-specific mortality (SSM) in patients with primary brain tumours. Methods: Patients of any age diagnosed with histologically confirmed primary brain tumours between 1992 and 2015 were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database. Primary outcome was impact of radiotherapy on 5-year SSM. Cumulative SSM rates under competing risk assumptions were estimated and stratified by intervention type. Time-dependent hazard ratios were estimated to identify when the radiotherapy impact was greatest. Results: 85,284 patients with primary brain tumour diagnoses were analysed. Overall, the 5-year cumulative SSM rate was low (0.6%) with the highest rate (0.76%) in patients receiving no treatment, in whom it mainly occurred < 1 month after diagnosis. SSM rates were lower in patients treated with radiotherapy alone (0.27%) or radiotherapy plus surgery (0.24%); stroke-related deaths also occurred later in these groups. While these patterns were observed in both glioblastoma and non-glioblastoma patients, stroke deaths tended to occur later in non-glioblastoma patients receiving radiotherapy. Relative to the 'no treatment' group, the highest risk of stroke mortality in radiotherapy treated patients occurred 3.5-4 years after diagnosis. Conclusion: The risk of SSM is low in patients with primary brain tumours and is not increased by radiotherapy. Two different patterns were observed: acute stroke mortality in patients receiving no treatment, and delayed stroke mortality in patients receiving radiotherapy (+/- surgery) with the latter peaking 3.5-4 years after diagnosis.

Functional herniated choroid plexus in brain parenchyma following VP shunt removal.

We report a 4-year-old male who presented with a blocked ventriculoperitoneal (VP) shunt inserted post excision of a WHO Grade 1 cerebellar pilocytic astrocytoma complicated post-operatively by pseudo meningocoele formation. Imaging revealed choroid plexus that had herniated along the shunt tract. Subsequent MRI showed development of cystic changes around the tract. The ectopic choroid plexus was still in continuity with the ventricular ependyma and was producing CSF in the left parietal lobe.

Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis.

Background: Automated brain tumor identification facilitates diagnosis and treatment planning. We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI. Methods: A systematic literature search from January 2000 to May 8, 2021 was conducted. Study quality was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Detection meta-analysis was performed using a unified hierarchical model. Segmentation studies were evaluated using a random effects model. Sensitivity analysis was performed for externally validated studies. Results: Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis. In detection, DL achieved a lower false positive rate compared to TML; 0.018 (95% CI, 0.011 to 0.028) and 0.048 (0.032 to 0.072) (P < .001), respectively. In segmentation, DL had a higher dice similarity coefficient (DSC), particularly for tumor core (TC); 0.80 (0.77 to 0.83) and 0.63 (0.56 to 0.71) (P < .001), persisting on sensitivity analysis. Both manual and automated whole tumor (WT) segmentation had "good" (DSC ≥ 0.70) performance. Manual TC segmentation was superior to automated; 0.78 (0.69 to 0.86) and 0.64 (0.53 to 0.74) (P = .014), respectively. Only 30% of studies reported external validation. Conclusions: The comparable performance of automated to manual WT segmentation supports its integration into clinical practice. However, manual outperformance for sub-compartmental segmentation highlights the need for further development of automated methods in this area. Compared to TML, DL provided superior performance for detection and sub-compartmental segmentation. Improvements in the quality and design of studies, including external validation, are required for the interpretability and generalizability of automated models.

Are UK medical schools using recommended national curricula for the teaching of clinical neuroscience?

BACKGROUND: Medical schools are responsible for training medical students to recognise and commence management for a broad spectrum of diseases, including clinical neuroscience conditions. To guide medical schools on topics that should be taught, speciality bodies have produced speciality-based core curricula. It is unknown to what extent these guidelines are used in designing each medical school's curriculum. This survey aimed at assessing the use of these guidelines in designing clinical neuroscience curricula. METHODS: This is a national survey. A 21-item questionnaire was sent to faculty members involved in the development of the clinical neuroscience curriculum in each medical school in the UK. Data collection occurred from1st September 2020-31 st August 2021. The Association of British Neurologists (ABN) and the Royal College of Surgeons England (RCSEng) guidelines were used as a benchmark. Descriptive statistics are reported. RESULTS: Data was collected from 91.9% of eligible UK medical schools. 61.8% respondents were aware of ABN guidelines and 35.3% were aware of RCSEng guidelines. 17/28 (60.7%) topics recommended by the guidelines were taught in the neuroscience curricula of over 90% of the medical schools. Neurologists were involved in the design of the clinical neuroscience curriculum in 94.1% (n = 32/34) of medical schools, and neurosurgeons in 61.8%. Tutorials/seminars were used by all medical schools to teach clinical neuroscience content. Neurologists were involved in teaching at all schools and neurosurgeons in 70.6%. Objective Structured Clinical Examination (OSCE)/oral examinations and single best answer (SBA)/multiple-choice question (MCQ) tests were used in all medical schools as methods of assessment. CONCLUSIONS: There is variation between medical schools on what clinical neuroscience topics are taught and by whom. Multi-modality educational delivery was evident. Some medical schools did not currently use, advertise, or recommend external clinical neuroscience educational resources; but there was support for future use of external resources including guidelines.