Precision Diagnosis and Treatment Monitoring of Glioma via PET Radiomics.
Authors
Affiliations (4)
Affiliations (4)
- Department of Neurosurgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China (C.Z., P.J., B.G., Y.K., Z.F., L.W.); Xi'an Medical University, Xi'an, China (C.Z., B.G.). Electronic address: [email protected].
- Department of Neurosurgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China (C.Z., P.J., B.G., Y.K., Z.F., L.W.).
- Department of Neurosurgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China (C.Z., P.J., B.G., Y.K., Z.F., L.W.); Xi'an Medical University, Xi'an, China (C.Z., B.G.).
- Department of Neurosurgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China (C.Z., P.J., B.G., Y.K., Z.F., L.W.). Electronic address: [email protected].
Abstract
Glioma, the most common primary intracranial tumor, poses significant challenges to precision diagnosis and treatment due to its heterogeneity and invasiveness. With the introduction of the 2021 WHO classification standard based on molecular biomarkers, the role of imaging in non-invasive subtyping and therapeutic monitoring of gliomas has become increasingly crucial. While conventional MRI shows limitations in assessing metabolic status and differentiating tumor recurrence, positron emission tomography (PET) combined with radiomics and artificial intelligence technologies offers a novel paradigm for precise diagnosis and treatment monitoring through quantitative extraction of multimodal imaging features (e.g., intensity, texture, dynamic parameters). This review systematically summarizes the technical workflow of PET radiomics (including tracer selection, image segmentation, feature extraction, and model construction) and its applications in predicting molecular subtypes (such as IDH mutation and MGMT methylation), distinguishing recurrence from treatment-related changes, and prognostic stratification. Studies demonstrate that amino acid tracers (e.g., <sup>18</sup>F-FET, <sup>11</sup>C-MET) combined with multimodal radiomics models significantly outperform traditional parametric analysis in diagnostic efficacy. Nevertheless, current research still faces challenges including data heterogeneity, insufficient model interpretability, and lack of clinical validation. Future advancements require multicenter standardized protocols, open-source algorithm frameworks, and multi-omics integration to facilitate the transformative clinical translation of PET radiomics from research to practice.