Abstract
BACKGROUND: Limited amino acid availability for positron emission tomography (PET) imaging hinders therapeutic decision-making for gliomas without typical high-grade imaging features. To address this gap, we evaluated a generative artificial intelligence (AI) approach for creating synthetic O-(2-(18)F-fluoroethyl)-l-tyrosine ([(18)F]FET)-PET and predicting high [(18)F]FET uptake from magnetic resonance imaging (MRI). METHODS: We trained a deep learning (DL)-based model to segment tumors in MRI, extracted radiomic features using the Python PyRadiomics package, and utilized a Random Forest classifier to predict high [(18)F]FET uptake. To generate [(18)F]FET-PET images, we employed a generative adversarial network framework and utilized a split-input fusion module for processing different MRI sequences through feature extraction, concatenation, and self-attention. RESULTS: We included magnetic resonance imaging (MRI) and PET images from 215 studies for the hotspot classification and 211 studies for the synthetic PET generation task. The top-performing radiomic features achieved 80% accuracy for hotspot prediction. From the synthetic [(18)F]FET-PET, 85% were classified as clinically useful by senior physicians. Peak signal-to-noise ratio analysis indicated high signal fidelity with a peak at 40 dB, while structural similarity index values showed structural congruence. Root mean square error analysis demonstrated lower values below 5.6. Most visual information fidelity scores ranged between 0.6 and 0.7. This indicates that synthetic PET images retain the essential information required for clinical assessment and diagnosis. CONCLUSION: For the first time, we demonstrate that predicting high [(18)F]FET uptake and generating synthetic PET images from preoperative MRI in lower-grade and high-grade glioma are feasible. Advanced MRI modalities and other generative AI models will be used to improve the algorithm further in future studies.