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Research progress on predicting molecular subtypes of adult-type diffuse gliomas using image-based artificial intelligence
LUO Pan  CHEN Chunhui  ZHANG Bin  HAN Tao  SUN Jiachen  ZHOU Junlin 

DOI:10.12015/issn.1674-8034.2026.05.027.


[Abstract] Adult-type diffuse gliomas, the precise diagnosis and treatment of which are highly dependent on molecular subtyping, represent the most common primary malignant tumors of the central nervous system. The 2021 fifth edition of the WHO Classification of Tumors of the Central Nervous System formally established the central role of molecular subtyping in the diagnosis, treatment, and prognosis assessment of gliomas. MRI, as the routine and core diagnostic tool for CNS tumors, leverages diverse techniques and emerging analytical methods to provide multidimensional insights into tumor biology. In recent years, image-based artificial intelligence technologies, particularly radiomics and deep learning, have significantly advanced our ability to harness latent information from these images, opening new avenues for the non-invasive prediction of key molecular biomarkers in adult-type diffuse gliomas. Based on the latest classification criteria, this article provides a review of recent research advances in the use of medical imaging artificial intelligence for predicting the molecular subtypes of adult-type diffuse gliomas. It offers an in-depth analysis of its clinical value, current challenges, and future directions, summarizes the limitations of existing studies, and outlines key areas for future research. The aim is to provide a scientific reference for the precise diagnosis and treatment of adult-type diffuse gliomas.
[Keywords] adult-type diffuse gliomas；molecular subtyping；artificial intelligence；radiomics；deep learning；magnetic resonance imaging

LUO Pan1, 2, 3, 4, 5   CHEN Chunhui1, 2, 3, 4, 5   ZHANG Bin1, 2, 3, 4, 5   HAN Tao1, 2, 3, 4, 5   SUN Jiachen1, 2, 3, 4, 5   ZHOU Junlin1, 3, 4, 5*  

1 Department of Radiology, the Second Hospital of Lanzhou University, Lanzhou 730000, China

2 Second Clinical School of Lanzhou University, Lanzhou 730000, China

3 Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China

4 Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China

5 Gansu Medical Imaging Science Data Center, Lanzhou 730000, China

Corresponding author: ZHOU J L, E-mail: ery_zhoujl@lzu.edu.cn

Conflicts of interest   None.

Received  2026-01-14
Accepted  2026-04-17
DOI: 10.12015/issn.1674-8034.2026.05.027
DOI:10.12015/issn.1674-8034.2026.05.027.

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