Primary Tumor or Metastasis? Deep Learning and Radiomics Allow Precise Differentiation in Brain Tumors
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.