Progress in the Personalised Treatment of Brain Tumours

AI identifies mutations. Karl Landsteiner University of Health Sciences once again sets standards with machine learning methods
By: KL Krems
KREMS, Austria - May 22, 2024 - PRLog -- Machine learning (ML) methods can quickly and accurately diagnose mutations in gliomas – primary brain tumours. This is shown by a recent study by Karl Landsteiner University of Health Sciences (KL Krems). In this study, data from physio-metabolic magnetic resonance images were analysed to identify mutations in a metabolic gene using ML. Mutations of this gene have a significant influence on the course of the disease, and early diagnosis is important for treatment. The study also shows that there are currently still inconsistent standards for the obtaining physio-metabolic magnetic resonance images, which prevent routine clinical use of the method.

Gliomas are the most common primary brain tumours. Despite the still poor prognosis, personalised therapies can already significantly improve treatment success. However, the use of such advanced therapies is based on individual tumour data, which is not readily available for gliomas due to their location in the brain. Imaging techniques such as magnetic resonance imaging (MRI) can provide such data, but their analyses are complex, demanding and time-consuming. The Central Institute for Medical Radiology Diagnostics at St. Pölten University Hospital, a teaching and research site of KL Krems, has therefore been developing machine and deep learning methods for years in order to automate such analyses and integrate them into routine clinical operations. A further breakthrough has now been achieved.

Original Publication: Machine Learning-Based Prediction of Glioma IDH Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study). A. Stadlbauer, K. Nikolic, S. Oberndorfer, F. Marhold, T. M. Kinfe, A. Meyer-Bäse, D. Alina Bistrian, O. Schnell & A. Doerfler. Cancers 2024, 16, 1102.

Scientific Contact

Prof. Dr. Andreas Stadlbauer

Central Institute for Medical Radiology Diagnostics University Hospital St. Pölten

Karl Landsteiner University of Health Sciences

T +43 2742 9004-14198


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3400 Klosterneuburg / Austria

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