AI Article Synopsis
- - This study investigates how histogram features from arterial spin labeling (ASL) MRI can help distinguish between different types of gliomas, specifically IDH-mut versus IDH-wt and lower-grade gliomas versus glioblastomas
- - Researchers analyzed data from 131 patients and used computed cerebral blood flow maps to extract key histogram features, finding significant correlations with tumor grades and genotypes, along with various statistical tests to validate their findings
- - Results indicate that combining certain CBF percentiles with the patient's age can improve diagnostic accuracy for differentiating glioma subtypes, achieving notable sensitivity and specificity rates in the models tested
Article Abstract
Aim: To explore the use of histogram features on noninvasive arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) in differentiating isocitrate dehydrogenase mutant-type (IDH-mut) from isocitrate dehydrogenase wild-type (IDH-wt) gliomas, and lower-grade gliomas (LGGs) from glioblastomas.
Material And Methods: This retrospective study included 131 patients who underwent ASL MRI and anatomic MRI. Cerebral blood flow (CBF) maps were calculated, from which 10 histogram features describing the CBF distribution were extracted within the tumor region. Correlation analysis was performed to determine the correlations between histogram features as well as tumor grades and IDH genotypes. The independent t-test and Fisher's exact test were used to determine differences in the extracted histogram features, age at diagnosis, and sex in different glioma subtypes. Multivariate binary logistic regression analysis was performed, and diagnostic performances were evaluated with the receiver operating characteristic curves.
Results: CBF histogram features were significantly correlated with tumor grades and IDH genotypes. These features can effectively differentiate LGGs from glioblastomas, and IDH-mut from IDH-wt gliomas. The area under the receiving operating characteristic curve of the model calculated using combined CBF 30th percentile and age at diagnosis in differentiating LGGs from glioblastomas was 0.73. Integrating age at diagnosis and CBF 10th percentile could be more effective in differentiating IDH-mut from IDH-wt gliomas. Furthermore, the combined model had a better area under the receiving operating characteristic curve at 0.856 (sensitivity: 84.4%, specificity: 82.9%).
Conclusion: The histogram features on ASL were significantly correlated with tumor grade and IDH genotypes. Moreover, the use of these features could effectively differentiate glioma subtypes. The combined application of age at diagnosis and perfusion histogram features resulted in a more comprehensive identification of tumor subtypes. Therefore, ASL can be a noninvasive tool for the pre-surgical evaluation of gliomas.
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| http://dx.doi.org/10.5137/1019-5149.JTN.42484-22.3 | DOI Listing |
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