Diffuse glioma molecular profiling with arterial spin labeling and dynamic susceptibility contrast perfusion MRI: A comparative study

Abstract

Background: Evaluation of molecular markers (IDH, pTERT, 1p/19q codeletion, and MGMT) in adult diffuse gliomas is crucial for accurate diagnosis and optimal treatment planning. Dynamic Susceptibility Contrast (DSC) and Arterial Spin Labeling (ASL) perfusion MRI techniques have both shown good performance in classifying molecular markers, however, their performance has not been compared side-by-side.

Methods: Pretreatment MRI data from 90 patients diagnosed with diffuse glioma (54 men/36 female, 53.1 ± 15.5 years, grades 2-4) were retrospectively analyzed. DSC-derived normalized cerebral blood flow/volume (nCBF/nCBV) and ASL-derived nCBF in tumor and perifocal edema were analyzed in patients with available IDH-mutation (n = 67), pTERT-mutation (n = 39), 1p/19q codeletion (n = 33), and MGMT promoter methylation (n = 31) status. Cross-validated uni- and multivariate logistic regression models assessed perfusion parameters' performance in molecular marker detection.

Results: ASL and DSC perfusion parameters in tumor and edema distinguished IDH-wildtype (wt) and pTERT-wt tumors from mutated ones. Univariate classification performance was comparable for ASL-nCBF and DSC-nCBV in IDH (maximum AUROCC 0.82 and 0.83, respectively) and pTERT (maximum AUROCC 0.70 and 0.81, respectively) status differentiation. The multivariate approach improved IDH (DSC-nCBV AUROCC 0.89) and pTERT (ASL-nCBF AUROCC 0.8 and DSC-nCBV AUROCC 0.86) classification. However, ASL and DSC parameters could not differentiate 1p/19q codeletion or MGMT promoter methylation status. Positive correlations were found between ASL-nCBF and DSC-nCBV/-nCBF in tumor and edema.

Conclusions: ASL is a viable gadolinium-free replacement for DSC for molecular characterization of adult diffuse gliomas.

Keywords: ASL; DSC; IDH; glioma; pTERT.

Figure 1.

(A) 50-year-old female with glioblastoma, grade 4 (IDH-wildtype, 1p/19q noncodeleted, MGMT-methylated, and pTERT-mutant). Structural segmentation resulting from the nnU-Net model shows necrosis (inner rim, yellow), enhancing tumor (middle rim, red), and edema (outer rim, green). (B) Examples of the ASL quality control assessment. Good—the perfusion signal is well-distributed in gray matter, and there are no visible motion or labeling artifacts; acceptable—minor motion or macrovascular artifacts resulting in regional loss of signal, but the acceptable overall quality, especially around the tumors; macrovascular—prominent macrovascular-signal artifact caused by delayed arterial arrival time; unusable—significant signal distortions, motion artifacts, failed labeling, or too high arrival time.

Figure 2.

Flowchart of the present study.

Figure 3.

IDH and pTERT status prediction performance of ASL-nCBF and DSC-nCBV univariate models. (A) IDH prediction performance metrics. Highlighted in bold are AUROCC ≥ 0.7. (B) Receiver operating characteristic curves of IDH-classifying perfusion descriptors with the highest AUROCC. (C) pTERT prediction performance metrics. Highlighted in bold are AUROCC ≥ 0.7. (D) Receiver operating characteristic curves of pTERT classifying perfusion descriptors with the highest AUROCC.

Figure 4.

Predictive performance of multivariate logistic regression models built on ASL- and DSC-perfusion parameters. (A) ROC curves of IDH and pTERT classification models. (B) Performance metrics of IDH- and pTERT-classification models.

Figure 5.

Scatterplots of ASL-nCBF and DSC-nCBV parameters in IDH-wildtype/mutant and pTERT-wildtype/mutant. TP—true positive, TN—true negative, FP—false positive, FN—false negative. Black horizontal lines indicate thresholds of univariate predictive models. Here, IDH-wildtype/mutant and pTERT-mutant/wildtype represent positive/negative cases respectively, according to the signal distribution analysis.