Primary Tumor or Metastasis? Deep Learning and Radiomics for Precise Differentiation in Brain Tumor

By: Karl Landsteiner University of Health Sciences
KREMS, Austria - Jan. 18, 2023 - PRLog -- The distinction between primary tumors and metastases can be made quickly and accurately in brain tumors using radiomics and deep learning algorithms. This is the key message of a study from Karl Landsteiner University of Health Sciences (KL Krems) now published in Metabolites. It shows that magnetic resonance-based radiological data of tumor O2 metabolism provide an excellent basis for discrimination using neural networks. This combination of so-called "oxygen metabolic radiomics" with analyses by special artificial intelligence was clearly superior to evaluations by human experts in all essential criteria. This is all the more impressive because essential oxygen values did not differ significantly between tumor types – and neuronal networks were nevertheless able to make clear distinctions on the basis of these values.

Glioblastoma, a primary tumor, and brain metastases are the most common types of brain tumors in adults. Their treatment must be fundamentally different, and a rapid and clear diagnosis therefore influences clinical outcome. However, their differentiation is difficult, as they are hardly distinguishable on classical magnetic resonance (MR) images. This is different with so-called physio-metabolic MR, which can record metabolic processes in tumor tissue. However, this provides such large amounts of data that its use in routine diagnostics would require evaluations by artificial intelligence. Their reliability is now being demonstrated by a team led by Prof. Andreas Stadlbauer of KL Krems using a specially developed deep learning algorithm and MR-based data on the O2 metabolism of the two tumor types.

Original publication: Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning. A. Stadlbauer, G. Heinz, F. Marhold, A. Meyer-Bäse, O. Ganslandt, M. Buchfelder & S. Oberndorfer. Metabolites 2022, 12 (12), 1264.

Scientific Contact
Prof. Dr. Andreas Stadlbauer
Institute of Medical Radiology
University Hospital St.Pölten
Karl Landsteiner University of Health Sciences
Dr.-Karl-Dorrek-Straße 30
3500 Krems / Austria
T +43 2742 9004-14198

Copy Editing & Distribution
PR&D – Public Relations for Research & Education
Dr. Barbara Bauder
Kollersteig 68
3400 Klosterneuburg
M +43 664 1576 350

Source:Karl Landsteiner University of Health Sciences
Email:*** Email Verified
Location:Krems - Lower Austria - Austria
Account Email Address Verified     Account Phone Number Verified     Disclaimer     Report Abuse
PR&D - Public Releations for Research & Education News
Most Viewed
Daily News

Like PRLog?
Click to Share