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Modeling Meningiomas: Optimizing Treatment Approach.

Majid Khan1, Chadwin Hanna2, Matthew Findlay3

  • 1Reno School of Medicine, University of Nevada, Reno, NV, USA.

Neurosurgery Clinics of North America
|May 20, 2023
PubMed
Summary

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Developing preclinical meningioma models is crucial for understanding tumor growth and testing treatments. Recent advancements, including AI and advanced rodent models, are improving our ability to study these challenging brain tumors.

Area of Science:

  • Neuro-oncology
  • Translational research
  • Tumor modeling

Background:

  • Preclinical meningioma models are essential for advancing research but have been historically difficult to establish.
  • Limited spontaneous rodent models exist, necessitating the development of alternative approaches.
  • Clinical heterogeneity of meningiomas presents challenges for effective treatment strategies.

Purpose of the Study:

  • To review and evaluate the current landscape of preclinical meningioma models.
  • To assess the utility of these models in understanding meningioma development and treatment.
  • To identify advancements in meningioma modeling, including AI and radiomics.

Main Methods:

  • Systematic literature review adhering to PRISMA guidelines.
Keywords:
Emerging treatmentsMeningiomasPreclinical modelsSubclassification

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  • Inclusion of 127 laboratory and animal studies focused on preclinical meningioma modeling.
  • Analysis of emerging technologies like artificial intelligence, radiomics, and neural networks in meningioma research.

    • Main Results:

    • Preclinical models are vital for gaining molecular insights into meningioma progression.
    • Established and emerging models (cell culture, in vivo, AI-driven) aid in differentiating clinical heterogeneity.
    • These models facilitate the evaluation of targeted therapies, including chemotherapy and radiation.

    Conclusions:

    • Advancements in preclinical meningioma models are critical for accelerating the development of targeted therapies.
    • The integration of AI and radiomics offers new avenues for understanding meningioma complexity.
    • Effective preclinical models are key to improving treatment outcomes for specific meningioma subtypes.