Unveiling The Neuropathology Tumour Landscape: 10-Year Statistical Analysis With Global Comparison - Single Centre Experience.

Introduction: Central nervous system (CNS) tumours represent a significant public health issue worldwide, and their incidence and distribution vary across different populations. Although studies on CNS tumours have been conducted in various countries, there is a lack of information regarding their patterns in Macedonia. Therefore, this study is aimed at investigating the distribution, histopathological types and subtypes and demographic features of CNS tumours in our country. Materials and Methods: A cross sectional study was conducted using the electronic database of the Institute of Pathology - Medical Faculty, University "Ss. Cyril and Methodius" in Skopje which contains data from 3286 received and analysed surgical specimens, mainly from the University Clinic of Neurosurgery in Skopje, and a smaller number of surgical specimens from the University Surgical Centre "St. Naum Ohridski" in Skopje between 2012 and 2022. The collected and analysed data includes patient age, sex and histopathological types and subtypes of the tumours. Results: The majority of CNS tumours were diagnosed in adults aged between 50-70, with a male to female ratio of 1.5:1. The most common location of the tumours was the cerebrum, followed by the pituitary gland and cerebellum. The most frequent histological groups were gliomas, with glioblastoma as the most common diagnosis, followed by meningiomas. Conclusion: Following a detailed and thorough review of the CNS tumours in our study, we can conclude that the R. of Macedonia follows global statistics and trends regarding brain tumours.

Large Neck Teratoma in a Newborn with Respiratory Distress Syndrome.

Neonatal tumours in the neck region are a rare finding. Teratomas typically comprise all three germ cell layers with tissues usually foreign to the anatomic site of origin. Head and neck teratomas account a smaller part of congenital teratomas. They can cause major airway obstruction due to the external compression that oropharyngeal or neck masses produce. In addition, there can be an intrinsic lesion in the larynx or trachea. We describe a premature, 30-gestational week-old newborn with large subcutaneous neck mass. Pre-delivery ultrasound showed heterogeneous tumor structure and displaced larynx. The intubation was successful. The newborn developed respiratory distress syndrome immediately after birth which rendered the surgical removal of the neck tumor impossible. An autopsy was done, and the histopathology revealed mature teratoma comprising muscle, brain, salivary and pulmonary tissues, as well as well-developed hyaline membranes in the alveoli. The combination of the respiratory distress syndrome and the neck tumor compression proved fatal. Prenatal diagnosis, therapeutic options and ex utero intrapartum treatment (EXIT) procedures are discussed for the diagnosis and management of this very rare tumor.

Measurement-oriented deep-learning workflow for improved segmentation of myelin and axons in high-resolution images of human cerebral white matter.

BACKGROUND: Standard segmentation of high-contrast electron micrographs (EM) identifies myelin accurately but does not translate easily into measurements of individual axons and their myelin, even in cross-sections of parallel fibers. We describe automated segmentation and measurement of each myelinated axon and its sheath in EMs of arbitrarily oriented human white matter from autopsies. NEW METHODS: Preliminary segmentation of myelin, axons and background by machine learning, using selected filters, precedes automated correction of systematic errors. Final segmentation is done by a deep neural network (DNN). Automated measurement of each putative fiber rejects measures encountering pre-defined artifacts and excludes fibers failing to satisfy pre-defined conditions. RESULTS: Improved segmentation of three sets of 30 annotated images each (two sets from human prefrontal white matter and one from human optic nerve) is achieved with a DNN trained only with a subset of the first set from prefrontal white matter. Total number of myelinated axons identified by the DNN differed from expert segmentation by 0.2%, 2.9%, and -5.1%, respectively. G-ratios differed by 2.96%, 0.74% and 2.83%. Intraclass correlation coefficients between DNN and annotated segmentation were mostly >0.9, indicating nearly interchangeable performance. COMPARISON WITH EXISTING METHOD(S): Measurement-oriented studies of arbitrarily oriented fibers from central white matter are rare. Published methods are typically applied to cross-sections of fascicles and measure aggregated areas of myelin sheaths and axons, allowing estimation only of average g-ratio. CONCLUSIONS: Automated segmentation and measurement of axons and myelin is complex. We report a feasible approach that has so far proven comparable to manual segmentation.