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In this educational review, the authors take a comprehensive look at various aspects and applications of artificial intelligence (AI) in the field of neuro-oncology, including machine learning, deep learning, and radiomics. The merits and challenges of the deployment and use of AI tools in neuro-oncology are put under the microscope, with the authors concluding that AI has a promising future in the field, highlighting its ability to improve patient outcomes and treatment in areas such as differentiation and grading of brain tumors, pre- and post-treatment assessment, and more.

Key points

  • AI methods utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions.
  • AI used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis.
  • AI has a role in the grading, prediction of treatment response, and prognosis of gliomas.
  • Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment.
  • AI is applied for the assessment of extra-axial brain tumors and pediatric tumors.

Article: Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

Authors: Ahmed Abdel Khalek Abdel Razek, Ahmed Alksas, Mohamed Shehata, Amr AbdelKhalek, Khaled Abdel Baky, Ayman El-Baz & Eman Helmy

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Welcome to the blog on Artificial Intelligence of the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.