Jie Bai, Bixiao Cui, Fengqi Li, Xin Han, Hongwei Yang, Jie Lu
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引用次数: 0
Abstract
Purpose: This study aimed to investigate the utility of radiomic features derived from multiparametric O-(2-18 F-fluoroethyl)-L-tyrosine (18F-FET) positron emission tomography (PET)/ magnetic resonance imaging (MRI) for the prediction of molecular genotypes in adult-type diffuse gliomas.
Methods: 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 18F-FET PET. After the multimodal images were spatially aligned, tumor segmentation was performed on the 18F-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).
Results: 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.
Conclusions: Multiparametric 18F-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.
期刊介绍：
BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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