Work

Multiparametric radiomics signature for predicting molecular genotypes in adult-type diffuse gliomas utilizing 18F-FET PET/MRI

Year: 2025

Type: article

Abstract: This study aimed to investigate the utility of radiomic features derived from multiparametric O-(2- 18 F-fluoroethyl)-L-tyrosine ( 18 F-FET) positron emission tomography (PET)/ magnetic resonance imaging (MRI) for the prediction of molecular genotypes in adult-type diffuse gliomas. This retrospective study analyzed 97 adult-type diffuse glioma patients, divided into 70% training and 30% testing cohorts. Each participant underwent hybrid PET/MRI scans, including FLAIR, 3D T1-CE, apparent diffusion coefficient (ADC), and 18 F-FET PET. After the multimodal images were spatially aligned, tumor segmentation was performed on the 18 F-FET PET and then applied to other MRI sequences. A total of 994 radiomic features were extracted from these specified modalities. The Naive Bayesian algorithm with five-fold validation was trained to develop prediction models for the IDH, TERT, and MGMT genotypes and to calculate the radiomics score (Rad-Score). The predictive performance of these models was evaluated via receiver operating characteristic (ROC) curves and decision curve analysis (DCA). The combined model demonstrated superior performance compared to single-modality and MRI (FLAIR + T1-CE + ADC) models in predicting certain genotype statuses in the testing cohort (IDH AUC = 0.97, MGMT AUC = 0.86, TERT AUC = 0.90). The comparisons of the Rad-Score in multimodal models for identifying IDH, TERT, and MGMT showed significant differences (all P < 0.001). Performance of the radiomics signature surpassed that of clinical and conventional radiological factors. DCA indicated that all multimodal models provided good net clinical benefits. Multiparametric 18 F-FET PET/MRI comprehensively analyzes the structural, proliferative, and metabolic information of adult-type diffuse gliomas, enabling precise preoperative diagnosis of molecular genotypes. This has the potential to aid in the development of personalized clinical treatment plans. Not applicable.

Source: BMC Medical Imaging

DOI: 10.1186/s12880-025-01729-7

Authors: Jie Bai, Bixiao Cui, Fengqi Li, Xin Han, Hongwei Yang, Jie Lu

Institutions: Capital Medical University, Xuan Wu Hospital of the Capital Medical University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,090