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Histogram analysis of diffusion weighted magnetic resonance imaging for predicting isocitrate dehydrogenase 1 mutation status in brain gliomas

Articles in Press

Document Type : Original Article

Authors

1 Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

2 Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

3 Department of Neurosurgery, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran

4 Department of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

5 Department of Pathology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

6 Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran Department of Medical Physics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Background: Gliomas are a major type of central nervous system (CNS) tumor. Accurate diagnosis of glioma grade and molecular subtype such as isocitrate dehydrogenase 1 (IDH1) mutation status remains a challenge as required invasive biopsy, which is limited by sampling bias and procedural risks. Quantitative analysis of functional magnetic resonance imaging (MRI), particularly apparent diffusion coefficient (ADC) maps, can serve as a non-invasive diagnostic tool for gliomas. However, using ADC values from different tumor regions may not accurately reflect the tumors’ heterogeneous nature. This study aims to investigate the diagnostic accuracy of histogram features of ADC maps across the entire tumor volume in differentiating between low-grade gliomas (LGGs) and high-grade gliomas (HGGs), as well as IDH1-wildtype from IDH1-mutated tumors.
Methods: This cross-sectional study included 30 patients with glioma who were assessed prior to undergoing surgical resection. 
The whole tumor histogram parameters, including mean, minimum, median, maximum, 10th, 25th, 75th, and 90th percentiles, mode, standard deviation (SD), kurtosis, inhomogeneity, skewness, and entropy, were obtained from the ADC maps. Statistical analysis was conducted to clarify associations between ADC histogram parameters, grade, and IDH1 mutation status. The sensitivity was determined to evaluate the performance of each parameter.
Results: The analysis revealed that 10th percentile ADC (ADC10th) had the highest sensitivity (87.5%, P = 0.0423) for discriminating between glioma grades and IDH1 mutation status, respectively.
Conclusion: The whole-tumor ADC histogram-profiling indicates potential value for predicting glioma grades and IDH1 molecular subtypes. However, further validation is required before clinical adoption.

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References
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Current Journal of Neurology

Articles in Press, Accepted Manuscript
Available Online from 18 May 2025
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