Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence
Motivation: There are few studies on the prediction of multiple genotype status in glioma by method of multiparameter 18F-FET PET/MR radiomics.
Goal(s): To explore the value of radiomics features based on multiparametric O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) PET/MRI in predicting the molecular biomarker status of glioma.
Approach: Retrospective radiomics analysis of 97 glioma patients' multiparametric PET/MRI data, employing Bayesian modeling and Receiver Operating Characteristic (ROC) curves to assess predictive performance for IDH, TERTp, and MGMT status.
Results: Multi-modal radiomics models demonstrated superior performance in the testing cohorts (IDH AUC=0.97, TERTp AUC=0.86, MGMT AUC=0.90) compared to single-modal models across all types of molecular biomarkers.
Impact: The radiomics models, which incorporate structural, proliferative, and metabolic information based on multiparameter 18F-FET PET/MRI, demonstrated effective predictive performance for the molecular biomarker status of glioma and had the potential to optimize diagnostic processes.
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