Magma (New York, N.Y.) · 2025
Research progress of deep learning based on magnetic resonance imaging in meningioma.
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Gui Yuan, Zhang Jing
Аннотация
OBJECTIVE: This review aims to summarize the research progress of magnetic resonance imaging (MRI)-based deep learning ( DL) in meningiomas, analyze its advantages, limitations, and key issues in clinical translation, provide technical references for relevant medical researchers and clinicians, thereby promoting the faster and more standardized application of DL in clinical diagnosis and treatment, and ultimately benefiting patients.
BACKGROUND: The early detection and accurate grading and classification of meningiomas are crucial for formulating personalized treatment plans. DL has achieved breakthrough progress in the field of meningioma imaging analysis. By adopting objective and quantitative analysis methods, it effectively overcomes the limitation of traditional diagnostic methods that rely on subjective human visual judgment, opening up broad prospects for the precise diagnosis and treatment of meningiomas.
METHODS: The literature search and selection process for this review was conducted as follows: Search period: 1 January 2019 to 31 October 2024; Databases searched: PubMed, Web of Science, and Embase; Search string: (("meningioma" OR "meningiomas") AND ("magnetic resonance imaging" OR "MRI") AND ("deep learning" OR "convolutional neural network" OR "CNN" OR "transformer" OR "neural network" OR "neural networks")).
CONCLUSIONS: The application of DL in meningioma research marks that medical imaging diagnosis has entered a new intelligent stage. By providing doctors with more objective and accurate diagnostic basis, it facilitates the formulation of personalized treatment plans, thereby improving patients' treatment outcomes and quality of life. The continuous breakthroughs of DL in the field of meningiomas indicate that the future of medical imaging diagnosis will be more intelligent and precise.
BACKGROUND: The early detection and accurate grading and classification of meningiomas are crucial for formulating personalized treatment plans. DL has achieved breakthrough progress in the field of meningioma imaging analysis. By adopting objective and quantitative analysis methods, it effectively overcomes the limitation of traditional diagnostic methods that rely on subjective human visual judgment, opening up broad prospects for the precise diagnosis and treatment of meningiomas.
METHODS: The literature search and selection process for this review was conducted as follows: Search period: 1 January 2019 to 31 October 2024; Databases searched: PubMed, Web of Science, and Embase; Search string: (("meningioma" OR "meningiomas") AND ("magnetic resonance imaging" OR "MRI") AND ("deep learning" OR "convolutional neural network" OR "CNN" OR "transformer" OR "neural network" OR "neural networks")).
CONCLUSIONS: The application of DL in meningioma research marks that medical imaging diagnosis has entered a new intelligent stage. By providing doctors with more objective and accurate diagnostic basis, it facilitates the formulation of personalized treatment plans, thereby improving patients' treatment outcomes and quality of life. The continuous breakthroughs of DL in the field of meningiomas indicate that the future of medical imaging diagnosis will be more intelligent and precise.