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

ABSTRACT: 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. However, DNA methylation profiling is expensive, labor-intensive, and not widely accessible. Consequently, a digital histology-based prediction of DNA methylation classes would be advantageous, complementing molecular classification. In this study, we developed and rigorously

SUBMITTER: Sehring J 

PROVIDER: S-EPMC10647687 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature