DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification.

The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE).

Leveraging Attention-Based Convolutional Neural Networks for Meningioma Classification in Computational Histopathology.

Convolutional neural networks (CNNs) are becoming increasingly valuable tools for advanced computational histopathology, promoting precision medicine through exceptional visual decoding abilities. Meningiomas, the most prevalent primary intracranial tumors, necessitate accurate grading and classification for informed clinical decision-making. Recently, DNA methylation-based molecular classification of meningiomas has proven to be more effective in predicting tumor recurrence than traditional histopathological methods.

Update on quality assurance in neuropathology: Summary of the round robin trials on promoter mutation, mutation, 1p/19q codeletion, and fusion testing in Germany in 2020 and 2021.

We previously reported on the first neuropathological round robin trials operated together with Quality in Pathology (QuIP) GmbH in 2018 and 2019 in Germany, i.e., the trials on mutational testing and promoter methylation analysis [1].