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Objective <p>This review aims to summarize the research progress of magnetic resonance imaging (MRI)-based deep learning ( DL) inmeningiomas, analyze its advantages, limitations, and key issues in clinical translation, provide technical references for relevantmedical researchers and clinicians, thereby promoting the faster and more standardized application of DL in clinical diagnosis andtreatment, and ultimately benefiting patients.</p> Background <p>The early detection and accurate grading and classification of meningiomas are crucial for formulating personalizedtreatment plans. DL has achieved breakthrough progress in the field of meningioma imaging analysis. By adopting objective andquantitative analysis methods, it effectively overcomes the limitation of traditional diagnostic methods that rely on subjective humanvisual judgment, opening up broad prospects for the precise diagnosis and treatment of meningiomas.</p> Methods <p>The literature search and selection process for this review was conducted as follows: Search period: 1 January 2019 to 31October 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”)).</p> Conclusions <p>The application of DL in meningioma research marks that medical imaging diagnosis has entered a new intelligentstage. By providing doctors with more objective and accurate diagnostic basis, it facilitates the formulation of personalized treatmentplans, thereby improving patients' treatment outcomes and quality of life. The continuous breakthroughs of DL in the field ofmeningiomas indicate that the future of medical imaging diagnosis will be more intelligent and precise.</p>

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Research progress of deep learning based on magnetic resonance imaging in meningioma

  • 期刊论文

  • 发表日期：

    2025年12月26日

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  • Yuan Gui,
  • Jing Zhang

摘要

Objective

This review aims to summarize the research progress of magnetic resonance imaging (MRI)-based deep learning ( DL) inmeningiomas, analyze its advantages, limitations, and key issues in clinical translation, provide technical references for relevantmedical researchers and clinicians, thereby promoting the faster and more standardized application of DL in clinical diagnosis andtreatment, and ultimately benefiting patients.

Background

The early detection and accurate grading and classification of meningiomas are crucial for formulating personalizedtreatment plans. DL has achieved breakthrough progress in the field of meningioma imaging analysis. By adopting objective andquantitative analysis methods, it effectively overcomes the limitation of traditional diagnostic methods that rely on subjective humanvisual 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 31October 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 intelligentstage. By providing doctors with more objective and accurate diagnostic basis, it facilitates the formulation of personalized treatmentplans, thereby improving patients' treatment outcomes and quality of life. The continuous breakthroughs of DL in the field ofmeningiomas indicate that the future of medical imaging diagnosis will be more intelligent and precise.