Document Type : Original Article
Authors
1
Qom University of Medical Sciences, Qom, Iran
2
Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3
Department of Radiology, Qom University of Medical Sciences, Qom, Iran
4
Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
5
Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
10.30491/ijtmgh.2023.431376.1395
Abstract
Introduction: Apparent diffusion coefficient (ADC) statistics can be valuable in distinguishing three types of brain tumors. The aim is to evaluate the capability of volume under the receiver operating characteristic (ROC) surface (VUS) for concurrent differential diagnosis of glioblastoma (GBM), lymphoma (LYM), and metastatic tumor(s) (MTTs) lesions of brain malignancies.
Methods: Investigated Magnetic Resonance Imaging (MRI) included 57 GBM, 25 LYM, and 25 MTT that were pathological diagnoses, after MR imaging. Region of interest (ROI) was taken from tumor regions (TUMOR), enhancement area (ENHANCED), and peritumoral edema (EDEM) regions. ADC maps were obtained after selecting a region of interest, and First-Order Histogram Features (FOHs) were extracted. Statistical analysis was performed by MedCalc version 15.8 for comparison of continuous variables between three groups of lesions and plotting the ROC curves. For VUS and correct classification rates (CCR) calculations the R software v2.13.1 with the DiagTest3grp package was used. The confidence interval level was 95% for significant results. Diagnostic accuracy of ADC in the differentiation of mentioned three groups was performed using ROC surface.
Results: ADCMin, ADC75 and ADC95 Percentile values in TUMOR groups of ROI, ADCMaximum, ADCMin, ADCMean, ADCMedian , ADCUniformity and ADCEntropy in ENHANCED and ADC25, ADC75, ADC95 Percentiles, ADCMean , ADCNormal Mean , ADCMedian, ADCEntropy, ADCThird Moment and ADCStandardDeviation in EDEM had significant VUS values results among GBM, LYM and MTTs .
Conclusion: VUS analysis is a helpful statistical method for categorizing types of brain tumors. Using the application of FOHs and proposed cut-off points for them by the VUS analysis, the differentiation of more than two types of brain tumors would be possible, concurrently. This will help neurologists and neurosurgeons to plan their treatment and surgery or monitor the status of patients’ therapeutic needs.
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