Journal Article

Advancements in the application of MRI radiomics in meningioma

Cai, Ruoyu, Song, Dengpan, Yan, Dongming, Xu, Dingkang, Lou, Yuanhao, Zhang, Kaiyuan, Guo, Fuyou

2025

Overview

Meningiomas are among the most common intracranial tumors, and challenges still remain in terms of tumor classification, treatment, and management. With the popularization of artificial intelligence technology, radiomics has been further developed and more extensively applied in the study of meningiomas. This objective and quantitative technique has played an important role in the identification, classification, grading, pathology, treatment, and prognosis of meningiomas, although new problems have also emerged. This review examines the application of magnetic resonance imaging (MRI) techniques in meningioma research. A database search was conducted for articles published between November 2017 and April 2025, with a total of 87 studies included after screening. These studies were summarized in detail, and the risk of bias and the certainty of the evidence were assessed using the Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and radiomics quality scores (RQS). All the studies were retrospective, with most being single-center studies. Contrast-enhanced T1-weighted imaging (T1C) and T2-weighted imaging (T2WI) are the most commonly used MRI sequences. Current research focuses on five topics, namely, differentiation, grade and subtypes, molecular pathology, biological behavior, treatment, and complications, with 14, 32, 14, 12, and 19 studies addressing these topics (some of which are multiple topics). Combined imaging features with clinical or pathological features often outperform traditional clinical models. Most studies show a low to moderate risk of bias. Large, prospective, multicenter studies are needed to validate the performance of radiomic models in diverse patient populations before their clinical implementation can be considered.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Analysis / Artificial intelligence / Bias / Biomedical and Life Sciences / Biomedicine / Brain cancer / Cancer Research / Care and treatment / Classification / Deep learning / Demographic aspects / Diagnosis / Edema / Health aspects / Humans / Imaging / Machine learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Medical imaging / Medical prognosis / Meningeal Neoplasms - diagnostic imaging / Meningeal Neoplasms - pathology / Meningioma / Meningioma - diagnostic imaging / Meningioma - pathology / Oncology / Pathology / Patient outcomes / Patients / Prognosis / Quality assessment / Quality control / Radiology / Radiomics / Radiotherapy / Regression analysis / Review / Tumors