Combined High-Resolution MRSI and -FACBC PET to Improve the Presurgical Diagnostic Accuracy in Gliomas

Medical imaging is crucial for glioma management. Combined with MRI, amino acid PET may improve glioma diagnosis, biopsy targeting, and tumor delineation compared to structural MRI alone. Magnetic resonance spectroscopic imaging (MRSI) complements both structural MRI and PET by detecting metabolites such as N-acetylaspartate (NAA), creatine (Cr), and choline (Cho), which are markers for brain health and tumor malignancy, but is challenged by low spatial resolution. This study evaluates whether high-resolution MRSI enhanced by deep learning can improve diagnostic accuracy and serve as a complement or alternative to PET for glioma classification. Ten glioma patients (CNS WHO grades 2-4, ages 24-80) were included. Presurgical [18F]-FACBC PET/MRI, including proton 2D MRSI, was acquired for all patients. Thirty image-guided biopsies were sampled during surgery from the patients and classified as glioma tissue or non-tumor tissue, and according to IDH1 status. For each biopsy location, tumor-to-background ratio (TBR) and standardized uptake value (SUV) from PET, and tCho/NAA and tCho/tCr ratios from MRSI were calculated. ROC analysis was used to assess the accuracy of [18F]-FACBC PET and high-resolution MRSI, and the combinations of these in classifying glioma vs. non-tumor tissue and IDH1 status. The tCho/NAA ratio from the deep learning-based model demonstrated excellent diagnostic accuracy in classifying glioma vs. non-tumor tissue (AUC = 0.87, 95% CI: 0.66- 1.0), outperforming SUV (AUC = 0.71, 95% CI: 0.49-0.90), TBR (AUC = 0.68, 95% CI: 0.48-0.86), and tCho/tCr (AUC = 0.81, 95% CI: 0.54-1.00). Combining TBR with tCho/NAA and/or tCho/tCr improved tissue classification compared to either modality alone, where TBR + tCho/NAA + tCr/NAA showed the best results (AUC = 0.91, 95% CI: 0.71-1.0). MRSI was a poor predictor for IDH1-status (tCho/NAA: AUC = 0.67, 95% CI: 0.44-0.88 and tCho/tCr: AUC = 0.38, 95% CI: 0.17-0.60), while PET was an excellent predictor (SUV: AUC = 0.83, 95% CI: 0.66-0.85 and TBR: AUC = 0.82, 95% CI: 0.65-0.94) and the combination of SUV and tCho/tCr was an outstanding predictor (AUC = 0.96, 95% CI: 0.88-1.0). Incorporating high-resolution MRSI in combination with [18F]-FACBC PET improved the diagnostic accuracy in differentiating glioma tissue from non-tumor tissue. Significance StatementOur study highlights the importance of combining imaging methods for brain tumor characterization. MRI remains central in brain imaging but is limited, making PET a valuable molecular complement. MRSI provides insight into neurometabolic alterations associated with tumor growth, yet its clinical utility has been limited by low spatial resolution. By applying deep learning, we enhanced the resolution of MRSI and compared its performance with PET. Our findings demonstrate that High-resolution MRSI adds diagnostic value and, with PET, may enhance glioma classification and inform future clinical decision-making.