Authors: Jan-Philipp Redlich, Friedrich Feuerhake, Joachim Weis, Nadine S. Schaadt, Sarah Teuber-Hanselmann, Christoph Buck, Sabine Luttmann, Andrea Eberle, Stefan Nikolin, Arno Appenzeller, Andreas Portmann, André Homeyer

Published on: January 26, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2401.15022

Summary

• What is new: Integration of clinical data, omics data, or magnetic resonance imaging (MRI) with AI for glioma diagnosis.
• Why this is important: Increasing complexity in the diagnosis of gliomas and the need for support in diagnosis and outcome prediction.
• What the research proposes: Using artificial intelligence to analyze whole-slide histopathology images for tasks like subtyping, grading, molecular marker prediction, and survival prediction.
• Results: AI-based methods have shown promising results, yet they are not implemented in actual clinical settings.

Technical Details

Technological frameworks used: Convolutional Neural Networks (CNNs)

Models used: nan

Data used: Public datasets like TCGA, focusing on glioblastoma and low-grade glioma.

Potential Impact

Healthcare diagnostics, particularly companies involved in diagnostic tools for glioma and AI-based diagnostic platforms.

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