Histogram Analysis Of Magnetic Resonance Imaging Research Articles

Prediction of O(6)-methylguanine-DNA methyltransferase promoter methylation status in IDH-wildtype glioblastoma using MRI histogram analysis.

To evaluate the utility of magnetic resonance imaging (MRI) histogram parameters in predicting O(6)-methylguanine-DNA methyltransferase promoter (pMGMT) methylation status in IDH-wildtype glioblastoma (GBM). From November 2021 to July 2023, forty-six IDH-wildtype GBM patients with known pMGMT methylation status (25 unmethylated and 21 methylated) were enrolled in this retrospective study. Conventional MRI signs (including location, across the midline, margin, necrosis/cystic changes, hemorrhage, and enhancement pattern) were assessed and recorded. Histogram parameters were extracted and calculated by Firevoxel software based on contrast-enhanced T1-weighted images (CET1). Differences and diagnostic performance of conventional MRI signs and histogram parameters between the pMGMT-unmethylated and pMGMT-methylated groups were analyzed and compared. No differences were observed in the conventional MRI signs between pMGMT-unmethylated and pMGMT-methylated groups (all p > 0.05). Compared with the pMGMT-methylated group, pMGMT-unmethylated showed a higher minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50, and coefficient of variation (CV) (all p < 0.05). Among all significant CET1 histogram parameters, minimum achieved the best distinguishing performance, with an area under the curve of 0.836. CET1 histogram parameters could provide additional value in predicting pMGMT methylation status in patients with IDH-wildtype GBM, with minimum being the most promising parameter.

Automatic lung segmentation of magnetic resonance images: A new approach applied to healthy volunteers undergoing enhanced Deep-Inspiration-Breath-Hold for motion-mitigated 4D proton therapy of lung tumors

Background and purposeRespiratory suppression techniques represent an effective motion mitigation strategy for 4D-irradiation of lung tumors with protons. A magnetic resonance imaging (MRI)-based study applied and analyzed methods for this purpose, including enhanced Deep-Inspiration-Breath-Hold (eDIBH). Twenty-one healthy volunteers (41–58 years) underwent thoracic MR scans in four imaging sessions containing two eDIBH-guided MRIs per session to simulate motion-dependent irradiation conditions. The automated MRI segmentation algorithm presented here was critical in determining the lung volumes (LVs) achieved during eDIBH. Materials and methodsThe study included 168 MRIs acquired under eDIBH conditions. The lung segmentation algorithm consisted of four analysis steps: (i) image preprocessing, (ii) MRI histogram analysis with thresholding, (iii) automatic segmentation, (iv) 3D-clustering. To validate the algorithm, 46 eDIBH-MRIs were manually contoured. Sørensen-Dice similarity coefficients (DSCs) and relative deviations of LVs were determined as similarity measures. Assessment of intrasessional and intersessional LV variations and their differences provided estimates of statistical and systematic errors. ResultsLung segmentation time for 100 2D-MRI planes was ∼ 10 s. Compared to manual lung contouring, the median DSC was 0.94 with a lower 95 % confidence level (CL) of 0.92. The relative volume deviations yielded a median value of 0.059 and 95 % CLs of −0.013 and 0.13. Artifact-based volume errors, mainly of the trachea, were estimated. Estimated statistical and systematic errors ranged between 6 and 8 %. ConclusionsThe presented analytical algorithm is fast, precise, and readily available. The results are comparable to time-consuming, manual segmentations and other automatic segmentation approaches. Post-processing to remove image artifacts is under development.

MRI histogram analysis of tumor-infiltrating CD8+ T cell levels in patients with glioblastoma

ObjectiveTo investigate the utility of preoperative magnetic resonance imaging histogram analysis for evaluating tumor-infiltrating CD8+ T cells in patients with glioblastoma (GBM). MethodsWe retrospectively analyzed the pathological and imaging data of 61 patients with GBM confirmed by surgery and pathology. Moreover, the levels of tumor-infiltrating CD8+ T cells in tumor tissue samples obtained from the patients were quantified through immunohistochemical staining and evaluated with respect to overall survival. The patients were divided into the high and low CD8 expression groups. Preoperative T1-weighted contrast-enhanced (T1C) histogram parameters of patients with GBM were extracted using Firevoxel software. We investigated the correlation between the histogram feature parameters and CD8+ T cells. We performed statistical analyses of the T1C histogram parameters in both groups and identified characteristic parameters with significant between-group differences. Additionally, we performed a receiver operating characteristic curve (ROC) analysis to determine the predictive utility of these parameters. ResultsThe levels of tumor-infiltrating CD8+ T cells were positively associated with overall survival in patients with GBM (P = 0.0156). Among the T1C histogram features, the mean, 5th, 10th, 25th, and 50th percentiles were negatively correlated with the levels of CD8+ T cells. Moreover, the coefficient of variation (CV) was positively correlated with the levels of CD8+ T cells (all P < 0.05). There was a significant between-group difference in the CV, 1st, 5th, 10th, 25th, and 50th percentiles (all p < 0.05). The ROC curve analysis revealed that the CV had the highest AUC value (0.783; 95% confidence interval: 0.658–0.878), with sensitivity and specificity values of 0.784 and 0.750, respectively, for distinguishing between the groups. ConclusionsThe preoperative T1C histogram have additional value for the levels of tumor-infiltrating CD8+ T cells in patients with GBM.

Differentiating renal cell carcinoma and oncocytoma with volumetric MRI histogram analysis.

In this study, the utility of histogram parameters derived from diffusion-weighted imaging for differentiate renal cell carcinoma (RCC) from oncocytoma was investigated. This research tracked 126 individuals who were diagnosed with RCC and oncocytoma through histopathological analysis, using magnetic resonance imaging (MRI) assessments from 2015 to 2023. We observed various attributes of these patients, including demographic details, surgical records, pre-surgery MRI results, MRI apparent diffusion coefficient (ADC) histogram analysis, and post-surgery histopathological outcomes. Calculations of ADC measurements such as mean, minimum, and maximum in conjunction with the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantile points were made. In addition, we also noted the skewness, kurtosis, and variance of these data points. The focus group for this investigation consisted of 75 male and 51 female patients. Out of these, 82 were diagnosed with RCC and 44 with oncocytoma. All ADC parameters including ADCmin, ADCmedian, ADCmean, and ADCmax, including the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantile divisions among the oncocytoma cohort were observed to be higher than the corresponding ones in the RCC group. A statistically meaningful difference was discovered between the minimum ADC value along with the 5th ranking of ADC measurements (p<0.001), in addition to mean of ADC (p=0.050), and the 10th (p=0.002) and 25th (p=0.015) quantiles of ADC data. When considering the region below the curve (AUC) in ROC analysis, the value of ADCmin was recorded as 0.739, with a sensitivity of 75.0%, and specificity of 68.2%. To distinguish oncocytoma from RCC, it may be useful to conduct a whole-tumor histogram and textural analysis of ADC values.

Evaluation of the corpus callosum using magnetic resonance imaging histogram analysis in autism spectrum disorder.

Histogram analysis is a texture analysis method that can be used in medical images. Quantitative values of the intensity of images can be obtained with histogram analysis. It aimed to evaluate corpus callosum in magnetic resonance images (MRIs) using histogram analysis of pediatric patients with autism spectrum disorder (ASD) to compare them with healthy controls. This study included 29 children with ASD and 29 healthy children with normal brain MRI. High-resolution three-dimensional turbo field echo images were obtained with a 1.5T scanner device for brain magnetic resonance imaging. On the corpus callosum in the sagittal T1-weighted images obtained, mean gray level density (mean), the standard deviation, median, minimum, maximum, entropy, variance, skewness, kurtosis, uniformity, size % L, size % M, size % U, and percentile parameters were measured. In ASD patients, mean, standard deviation, maximum, median, variance, entropy, 25%, 75%, 90%, 97%, and 99% values were found to be lower than the control group, and size % U value was higher. In addition, the corpus callosum area was significantly lower in the ASD compared to the controls. According to our study, corpus callosum of patients with ASD showed differences compared to healthy controls by histogram analysis, even though they were seen as normal in brain MRI. We think that histogram analysis can be used to evaluate possibly affected areas of brain in ASD patients.

Magnetic resonance imaging histogram analysis of amygdala in functional neurological disorder: Histogram Analysis of Amygdala in Functional Neurological Disorder

Magnetic resonance imaging histogram analysis of amygdala in functional neurological disorder: Histogram Analysis of Amygdala in Functional Neurological Disorder

Magnetic resonance imaging histogram analysis of corpus callosum in a functional neurological disorder

Background/aimThe aim of the present study was to examine and compare the corpus callosum (CC) via histogram analysis (HA) on T1-weighted MR images of patients diagnosed with Functional Neurological Disorder (FND) and healthy controls.Materials and methodsThe study group included 19 female patients diagnosed with FND, and the control group included 20 healthy subjects. All participants were scanned with a 1.5 T MR scanner. A high-resolution structural image of the entire brain was obtained with sagittal 3D spiral fast spin echo T1-weighted images. Gray level intensity, standard deviation of the histogram, entropy, uniformity, skewness, and kurtosis values were determined with texture analysis. A student’s t-test was used to compare the group data. P < 0.05 was accepted as statistically significant.ResultsIt was determined that the mean gray level intensity, standard deviation of the histogram, entropy calculated by the maximum, median and variance and size M percentage values were higher in patients with FND. Kurtosis and size U percentages values were lower in patients with FND.ConclusionIn the present study, analysis of CC with T1-weighted MR image HA demonstrated significant differences between FND patients and healthy controls. The study findings indicated that HA is a beneficial technique for demonstrating textural variations between the CCs of patients with FND and healthy controls using MR images.

Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma.

Conventional magnetic resonance imaging (MRI) technics are insufficient in the differentiation of tumor progression from post-treatment changes in patients with treated glioblastoma. Previous studies have suggested that histogram analysis is a useful tool in the assessment of treatment response in different cancer types. The aim of the study was to to evaluate the effectiveness of MRI histogram analysis in the differentiation of tumor progression from pseudoprogression in patients with treated glioblastoma. Forty-six patients with glioblastoma who newly developed enhancing lesions following chemoradiation treatment were included in this retrospective study. Histogram analysis was performed from new enhancing lesions on T1-weighted contrast-enhanced MRI. Histogram analysis findings of patients with progression (23) and pseudoprogression (23) were compared. Mean, minimum, median, maximum, standard deviation, variance, entropy, skewness, uniformity values were found to be significantly higher in progressive disease (p < 0.05). A receiver-operating characteristic (ROC) curve analysis was performed for mean value, and area under the curve (AUC) was found as 0.975. When the threshold value was selected as 528.86, two groups could be differentiated with 95.7% sensitivity and 87.0% specificity. MRI histogram analysis can be used for the differentiation of progressive disease from pseudoprogression.

Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI.

To evaluate the potential value of machine learning (ML)-based histogram analysis (or first-order texture analysis) on T2-weighted magnetic resonance imaging (MRI) for predicting consistency of pituitary macroadenomas (PMA) and to compare it with that of signal intensity ratio (SIR) evaluation. Fifty-five patients with 13 hard and 42 soft PMAs were included in this retrospective study. Histogram features were extracted from coronal T2-weighted original, filtered and transformed MRI images by manual segmentation. To achieve balanced classes (38 hard vs 42 soft), multiple samples were obtained from different slices of the PMAs with hard consistency. Dimension reduction was done with reproducibility analysis, collinearity analysis and feature selection. ML classifier was artificial neural network (ANN). Reference standard for the classifications was based on surgical and histopathological findings. Predictive performance of histogram analysis was compared with that of SIR evaluation. The main metric for comparisons was the area under the receiver operating characteristic curve (AUC). Only 137 of 162 features had excellent reproducibility. Collinearity analysis yielded 20 features. Feature selection algorithm provided six texture features. For histogram analysis, the ANN correctly classified 72.5% of the PMAs regarding consistency with an AUC value of 0.710. For SIR evaluation, accuracy and AUC values were 74.5% and 0.551, respectively. Considering AUC values, ML-based histogram analysis performed better than SIR evaluation (z = 2.312, p = 0.021). ML-based T2-weighted MRI histogram analysis might be a useful technique in predicting the consistency of PMAs, with a better predictive performance than that of SIR evaluation.

An evaluation of magnetic resonance imaging with histogram analysis in patients with idiopathic subjective tinnitus.

OBJECTIVE:Tinnitus is defined as the perception of sound in the ears without any acoustic stimulus. It has still not been determined whether tinnitus originates peripherally, centrally, or from several locations. The aim of the study was to determine any possible cochlear pathology in patients with normal hearing and tinnitus of unspecified etiology.METHODS:The study included 22 patients who presented at the clinic with the complaint of idiopathic subjective tinnitus that had been ongoing for at least 1 year. A total of 44 ears were included; 28 with tinnitus and 16 healthy ears. Unilateral tinnitus was determined in 16 patients and bilateral in six patients. The temporal magnetic resonance imaging (MRI) and audiogram results of the patients were retrieved from the hospital records. The MRI histogram measurement was made automatically and calculated for each ear separately.RESULTS:Entropy was found to be significantly higher in the control group and distribution was lower (p>0.01). In the tinnitus group, distribution was found to be broader, and kurtosis was statistically significantly greater (p<0.001).CONCLUSION:MRI histogram analysis can be considered as a method to be used in the clinical evaluation of the determination of possible cochlear pathology in ears with tinnitus.