Gliomas

Stimulated Raman Histology and Machine learning provides an alternative approach in brain tumor diagnosis

The study published in Nature Medicine examined the diagnostic accuracy of brain tumor image classification through artificial intelligence tool, compared with the accuracy of pathologist interpretation of conventional histologic images. The results for both methods were comparable: the artificial intelligence based diagnosis was 94.6% accurate, compared with 93.9% for the traditional pathologist-based assessment and interpretation. …

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New biomarker based on expression of SHOX2 gene for predicting survival in Gliomas

Researchers at UT Southwestern Medical Center have found a new biomarker for glioma, a common type of brain cancer, that can help doctors determine how aggressive a cancer is and that could eventually help determine the best course of treatment. The findings are published in EBiomedicine. Researchers from the Harold C. Simmons Comprehensive Cancer Center found …

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