A biopsy-validated study of combined DL-upscaled MRSI and (18F)-FACBC PET for presurgical glioma diagnosis
4 affiliations
- 1Department of Diagnostic Imaging, Akershus University Hospital , ,
- 2Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU) , ,
- 3Department of Neurosurgery, Ophthalmology and Otorhinolaryngology, University Hospital of North Norway , ,
- 4Department of Neuroscience, Norwegian University of Science and Technology , ,
Abstract
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 upscaled MRSI enhanced by deep learning can improve diagnostic accuracy and serve as a complement or alternative to PET for glioma classification. Ten glioma patients (ages 24-80) were included. Presurgical [18F]-FACBC PET/MRI, including proton 2D MRSI, was acquired for all patients. Thirty-two 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 upscaled MRSI, and the combinations of these in classifying glioma vs non-tumor tissue and IDH1 status. Among the individual biomarkers evaluated, the tCho/NAA ratio from the deep learning-based model demonstrated the highest apparent AUC for classifying glioma vs. non-tumor tissue (AUC = 0.87, 95% CI: 0.66-1.0), compared with tCho/tCr (AUC = 0.81, 95% CI: 0.54-1:00), SUV (AUC = 0.71, 95% CI: 0.49-0.90), and TBR (AUC = 0.68, 95% CI: 0.48-0.86). Combining TBR with tCho/NAA and/or tCho/tCr improved tissue classification compared to either modality alone, where TBR+tCho/NAA+tCho/tCr 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: 0.66-0.95 and TBR: AUC = 0.82, 95% CI: 0.65-0.94) and the combination of SUV, tCho/NAA, and tCho/tCr was a best predictor (AUC = 0.96, 95% CI: 0.88-1.0). Combining DL-upscaled MRSI with [18F]-FACBC PET appeared to enhance discrimination between glioma and non-tumor tissue, a finding that warrants validation in larger, prospective studies.
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BibTeX
@article{bjorkeli2026biopsyvalidated,
title = {{A biopsy-validated study of combined DL-upscaled MRSI and (18F)-FACBC PET for presurgical glioma diagnosis}},
author = {Erin Beate Bjørkeli and Anna Maria Karlberg and Benedikte Emilie Vindstad and Lars Kjelsberg Pedersen and Ole Skeidsvoll Solheim and Jonn Terje Geitung and Morteza Esmaeili and Live Eikenes},
journal = {Frontiers in oncology},
year = {2026},
month = sep,
doi = {10.3389/fonc.2026.1968798},
url = {https://europepmc.org/article/PMC/PMC13661501},
note = {CC BY},
}
RIS
TY - JOUR
TI - A biopsy-validated study of combined DL-upscaled MRSI and (18F)-FACBC PET for presurgical glioma diagnosis
AU - Erin Beate Bjørkeli
AU - Anna Maria Karlberg
AU - Benedikte Emilie Vindstad
AU - Lars Kjelsberg Pedersen
AU - Ole Skeidsvoll Solheim
AU - Jonn Terje Geitung
AU - Morteza Esmaeili
AU - Live Eikenes
PY - 2026
DA - 2026/09/30
JO - Frontiers in oncology
DO - 10.3389/fonc.2026.1968798
UR - https://europepmc.org/article/PMC/PMC13661501
AB - 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 upscaled MRSI enhanced by deep learning can improve diagnostic accuracy and serve as a complement or alternative to PET for glioma classification. Ten glioma patients (ages 24-80) were included. Presurgical [18F]-FACBC PET/MRI, including proton 2D MRSI, was acquired for all patients. Thirty-two 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 upscaled MRSI, and the combinations of these in classifying glioma vs non-tumor tissue and IDH1 status. Among the individual biomarkers evaluated, the tCho/NAA ratio from the deep learning-based model demonstrated the highest apparent AUC for classifying glioma vs. non-tumor tissue (AUC = 0.87, 95% CI: 0.66-1.0), compared with tCho/tCr (AUC = 0.81, 95% CI: 0.54-1:00), SUV (AUC = 0.71, 95% CI: 0.49-0.90), and TBR (AUC = 0.68, 95% CI: 0.48-0.86). Combining TBR with tCho/NAA and/or tCho/tCr improved tissue classification compared to either modality alone, where TBR+tCho/NAA+tCho/tCr 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: 0.66-0.95 and TBR: AUC = 0.82, 95% CI: 0.65-0.94) and the combination of SUV, tCho/NAA, and tCho/tCr was a best predictor (AUC = 0.96, 95% CI: 0.88-1.0). Combining DL-upscaled MRSI with [18F]-FACBC PET appeared to enhance discrimination between glioma and non-tumor tissue, a finding that warrants validation in larger, prospective studies.
SN - 2234-943X
ER -