Research on Classification Diagnosis and Treatment of Gliomas Based on Multimodal MRI and Artificial Intelligence Assisted Decision making

Abstract

Glioma is the most common type of primary intracranial malignant tumor; The highly heterogeneous and aggressive characteristics pose great challenges to the diagnosis and treatment of patients. Accurate preoperative grading, molecular typing prediction, and personalized treatment are of great significance in improving patient survival time and prognosis. With the development of multimodal magnetic resonance imaging technology, it can reflect the pathological and physiological characteristics of tumors from multiple angles and in all directions, including changes in morphology, function, and metabolism. At the same time, with the development of artificial intelligence, especially machine learning and deep learning, powerful technical means have been provided for mining deep level information from image big data and establishing objective quantitative analysis models. This article introduces the methods of using various MRI data for characteristic analysis of gliomas, and presents the process of constructing an artificial intelligence assisted diagnostic model, including image data preprocessing, tumor segmentation, feature extraction and screening, grading diagnosis, and molecular typing prediction model design and validation. Finally, the support of artificial intelligence assisted decision support systems for clinical diagnosis and treatment was discussed, including preoperative planning, prognosis assessment, efficacy evaluation, and AI visualization assisted decision support in multidisciplinary consultations; The combination of multimodal imaging and artificial intelligence is expected to promote the transformation of glioma from imaging diagnosis to diagnosis and treatment decision-making, and promote the development of its grading diagnosis and treatment model.