Lund University Publications

Prediction of MGMT promoter methylation in glioblastoma and grade 4 astrocytoma using fluid-suppressed chemical exchange saturation transfer MRI and machine learning-based segmentation

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Salomonsson, Tim ^LU ; Zahirovic, Edin ^LU ; Knutsson, Malte ^LU ; Seidemo, Anina ^LU ; Saenz Sarda, Xavier ^LU ; Liebig, Patrick ; Casagranda, Stefano ; Lätt, Jimmy ^LU ; Rydelius, Anna ^LU and Bengzon, Johan ^LU , et al.

Abstract

INTRODUCTION: Treatment response in high grade gliomas (HGG) is influenced by O6-methylguanine-DNA methyltransferase promoter methylation (MGMTpm). Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a non-invasive approach for potential molecular tumor characterization, although previous studies have reported inconsistent results for MGMTpm. This study investigates fluid-suppressed (FS) CEST metrics and automated tumor segmentation in the preoperative prediction of MGMTpm in HGG.

METHODS: 3 T MRI including CEST imaging was performed in 44 patients with HGG (mean age 59 years, 16 female, 24 MGMTpm, 39 glioblastoma and five astrocytoma, grade 4). Contrast-enhancing tumor (ET), necrosis, and peritumoral... (More)

INTRODUCTION: Treatment response in high grade gliomas (HGG) is influenced by O6-methylguanine-DNA methyltransferase promoter methylation (MGMTpm). Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a non-invasive approach for potential molecular tumor characterization, although previous studies have reported inconsistent results for MGMTpm. This study investigates fluid-suppressed (FS) CEST metrics and automated tumor segmentation in the preoperative prediction of MGMTpm in HGG.

METHODS: 3 T MRI including CEST imaging was performed in 44 patients with HGG (mean age 59 years, 16 female, 24 MGMTpm, 39 glioblastoma and five astrocytoma, grade 4). Contrast-enhancing tumor (ET), necrosis, and peritumoral edema were segmented manually and with two machine learning (ML) based models (DeepBraTumIA and Raidionics). Maximum, minimum and percentile-based metrics were extracted from FS amide proton transfer-weighted signal at 3.5 ppm (APTw) and FS CEST signal at 2.0 ppm (CEST@2ppm), normalized with contralateral normal-appearing white matter. The APTw/CEST@2ppm ratio was calculated. Statistical analysis was performed between MGMTpm and non-MGMTpm tumors using group comparisons, Spearman correlation, and receiver operating characteristics with area under curve (AUC), corrected for multiple comparisons.

RESULTS: Significant differences were found in non-MGMTpm relative to MGMTpm tumors: higher 90th percentile and max APTw in necrosis across all segmentation methods; higher 90th percentile APTw, max APTw and max CEST@2ppm in ET with the ML-based models; higher max CEST@2ppm and max ratio in necrosis with Raidionics. The findings with APTw and CEST@2ppm remained consistent in glioblastoma patients and were further supported by significant inverse correlations between the CEST metrics and percentage of MGMTpm. The best-performing parameters for predicting MGMTpm status were the max APTw in ET and necrosis (AUC 0.76-0.82) and max CEST@2ppm in ET and necrosis (AUC 0.67-0.85), achieving higher sensitivity (69-100%) compared to specificity (59-82%).

CONCLUSION: MGMTpm status in HGG may be predicted with fluid-suppressed CEST MRI. Using publicly available ML-based segmentation tools highlights a potential reproducible workflow in the clinical setting. Future multicenter studies with optimized acquisition protocols and independent, multimodal validation are warranted.

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