196 Accurate Tumor Delineation of Glioma by FET-PET Compared to Contrast-Enhanced MRI: Radiomics and Genomics Analysis of the Mismatch Area
INTRODUCTION: Glioma treatment planning requires precise tumor delineation, which is typically performed with contrast-enhanced (CE) MRI. However, CE MRI fails to reflect the entire extent of glioma. O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) PET may detect tumor volumes missed by CE MRI. However, the histological and molecular information of the “mismatched” area between PET and CE-MRI remains unknown. METHODS: Conventional MRI and 18F-FET PET were performed simultaneously on a hybrid PET/MR in 33 patients with histopathologically confirmed glioma. Additionally, several stereotactic biopsy samples were taken from “matched” or “mismatched” FET-PET and CE MRI regions, histological, molecular and single-cell information were then analyzed, marking the first-ever reported study of its kind. RESULTS: Of the 21 biopsy samples obtained from regions with increased FET uptake, all were histopathologically confirmed as glioma tissue or tumor infiltration, whereas only 13 showed enhancement on CE MRI indicating more accurate tumor boundary delineation by FET compared to CE MRI. The nomogram combing Rad-score and clinical features had the greatest performance in predicting TRFET over MR (AUC = 0.90/0.87, training/testing). CONCLUSIONS: The metabolically active biodistribution of gliomas delineated with FET-PETsignificantly exceeds tumor volume on CE MRI, and histopathology confirms these findings. Our preliminary results indicate that combining the anatomic and molecular information obtained from conventional MRI and FET-PET would reveal a more accurate glioma extent, the nomogram based on MRI radiomics would predict gliomas tumor residuals caused by the absence. The above information is critical for individualized treatment planning.
Authors
- Ye Cheng
Publication Details
- Journal
- Neurosurgery
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1227/neu.0000000000003360_196
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00