Artificial intelligence for radiomics; diagnostic biomarkers for neuro-oncology

Recent advances in medical image analysis have been made to improve our understanding of how disease develops, behaves, and responds treatment. Magnetic resonance imaging (MRI) positron emission tomography (PET) advanced strategies provide structural functional phenotypic biomarkers that correlate with key processes. Through radiomics radiogenomics, ML-medical has opened up new perspectives high-grade glioma diagnosis. As a result, non-invasive vivo for patient survival, tumor recurrence, genomics are identified. Tumor genomic signatures can help identify patients who benefit from targeted therapies. Molecular characterization gliomas prediction their evolution would allow treatment optimization. Radiomics-based more in-depth pathophysiologic processes insights into diagnosing better, classifying, stratifying, prognosticating brain tumors assessing response therapy. Radiomics is data-driven approach answer clinical questions like diagnosis, prognosis, response. With encouraging outcomes patients, deep learning still not widely used practice, requiring extensive practical studies.