Abstract
This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It 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. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient’s prognoses.
Highlights
Introduction and backgroundBrain tumors The World Health Organization (WHO) has provided an update of brain tumor classification in 2016 incorporating genetic information
Artificial Intelligence (AI) is applied for the assessment of extra-axial brain tumors and pediatric tumors
Machine learning (ML) is a subset of AI techniques that utilize algorithms that evolve as new data are introduced
Summary
Brain tumors The World Health Organization (WHO) has provided an update of brain tumor classification in 2016 incorporating genetic information. Discrimination between different types of brain tumors is problematic at imaging. Accurate diagnosis is crucial for planning of treatment to improve patient’s outcome, helpful in the grading of tumors and response after therapy [1–7]. Brain tumor biopsy is considered the gold standard for diagnosis. It carries the risk of procedure-related complications in about 6% of cases [2, 3, 8, 9]
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