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To investigate the value of preoperative MRI features and ADC histogram analysis for evaluating tumor-infiltrating CD8+ T cells in meningiomas. In this single-center cross-sectional study, we conducted a retrospective analysis of clinical, imaging, and pathological data from 84 patients with meningioma and performed immunohistochemical staining to quantitatively evaluate CD8+ T cells. Using X-Tile software, we divided the patients into high-and low-CD8+ T cells groups based on cut-off values. Furthermore, we compared the clinical and MRI features between the two groups and assessed the predictive value of significant parameters by plotting ROC curves. Additionally, Spearman's analysis was used to examine the association between ADC histogram parameters and CD8+ T cells. The level of tumor-infiltrating CD8+ T cells was found to have a negative correlation with recurrence in patients with meningiomas (r=-0.235, p = 0.031). No statistically significant differences were found in clinical and conventional MRI features between the two groups (all p > 0.05). Conversely, among the ADC histogram parameters, the coefficient of variation (CV), Perc.01, Perc.05, Perc.10, and Perc.25 showed statistically significant differences between the two groups (all p < 0.05) and combined ADC histogram parameters had the highest AUC (0.791; 95%CI (0.689–0.872)). Additionally, we observed a positive correlation between Perc.01, Perc.05, Perc.10 and CD8+ T cells (p < 0.05), the CV and variance was negatively correlated with the levels of CD8+ T cells (p < 0.05). ADC histogram analysis can be used as an imaging tool to preoperatively assess CD8+ T cells in patients with meningioma, and found a certain correlation between them. [ABSTRACT FROM AUTHOR]

Steganography is used to hide sensitive types of data including images, audio, text, and videos in an invisible way so that no one can detect it. Image-based steganography is a technique that uses images as a cover media for hiding and transmitting sensitive information over the internet. However, image-based steganography is a challenging task due to transparency, security, computational efficiency, tamper protection, payload, etc. Recently, different image steganography methods have been proposed but most of them have reliability issues. Therefore, to solve this issue, we propose an efficient technique based on the Least Significant Bit (LSB). The LSB substitution method minimizes the error rate in the embedding process and is used to achieve greater reliability. Our proposed image-based steganography algorithm incorporates LSB substitution with Magic Matrix, Multi-Level Encryption Algorithm (MLEA), Secret Key (SK), and transposition, flipping. We performed several experiments and the results show that our proposed technique is efficient and achieves efficient results. We tested a total of 165 different RGB images of various dimensions and sizes of hidden information, using various Quality Assessment Metrics (QAMs); A name of few are; Normalized Cross Correlation (NCC), Image Fidelity (IF), Peak Signal Noise Ratio (PSNR), Root Mean Square Error (RMSE), Quality Index (QI), Correlation Coefficient (CC), Structural Similarity Index (SSIM), Mean Square Error (MSE), Entropy, Contrast, and Homogeneity, Image Histogram (IH). We also conducted a comparative analysis with some existing methods as well as security analysis which showed better results. The achieved result demonstrates significant improvements over the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract Purpose To assess and compare the diagnostic efficiency of histogram analysis of monochromatic and iodine images derived from spectral CT in predicting Ki-67 expression in gastric gastrointestinal stromal tumors (gGIST). Methods Sixty-five patients with gGIST who underwent spectral CT were divided into a low-level Ki-67 expression group (LEG, Ki-67

Objective: Limited studies have explored the utility of whole-lesion histogram analysis in discerning the subtypes and proliferation status of non-small cell lung cancer (NSCLC), despite its potential to provide comprehensive tissue assessment through the computation of additional quantitative metrics. This study sought to assess the significance of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram parameters in discriminating between squamous cell carcinoma (SCC) and adenocarcinoma (AC), and to examine the correlation of each parameter with the proliferative marker Ki-67. Materials and methods: Patients with space-occupying lesions detected by chest CT examination and with further routine MRI, DKI and IVIM functional sequence scans were enrolled. Based on the pathological results, seventy patients with NSCLC were selected and divided into AC and SCC groups. Histogram parameters of IVIM (D, D*, f) and DKI (Dapp, Kapp) were calculated, and the Mann–Whitney U test or independent samples t test was used to analyze the differences in each histogram parameter of the SCC and AC groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the histogram parameters. The correlation coefficient between histogram parameters and Ki-67 was calculated using Spearman's or Pearson's methods. Results: The D 10th percentile, D 90th percentile, D mean, D median, Dapp10th percentile, Dapp90th percentile, Dappmean, Dappmedian, Dappskewness, DappSD of the AC groups were significantly higher than those of the SCC groups, while the Kappentropy and KappSD of the SCC groups were significantly higher than those of the AC groups. All the above differences were statistically significant (all P < 0.05). ROC curve analysis revealed that Dappmean showed the best performance for differentiating AC from SCC lesions, with an area under the ROC curve of 0.832 (95% confidence interval [CI]: 0.707-0.919). But there was no statistically significant difference in diagnostic efficacy compared to other histogram parameters (all P>0.05). Dapp90thpercentile, Dappmean, Kappskewnes showed a slight negative correlation with Ki-67 expression (r value -0.340, -0.287, -0.344, respectively; P< 0.05), while the other histogram parameters showed no significant correlation with Ki-67 (all P > 0.05). Conclusions: Our study demonstrates the utility of IVIM and DKI histogram analyses in differentiating NSCLC subtypes, particularly AC and SCC. Correlations with the Ki-67 index suggest that Dappmean, Dapp90th percentile, and Kappskewness may serve as markers of tumor aggressiveness, supporting their use in NSCLC diagnosis and treatment planning. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs). Methods: Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared. Results: The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 – p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618). Conclusions: Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances. [ABSTRACT FROM AUTHOR]

To look for links between diffusion and IVIM parameters and different molecular subtypes and prognostic factors through histogram analysis. A total of 139 patients with breast cancer who had pre-operative MRI examinations were enrolled in this retrospective study. Histograms of the diffusion and IVIM parameters were analyzed for the whole tumor, and an association was investigated between the parameters and the different molecular prognostic factors and subtypes using the nonparametric test, Spearman's rank correlation, and receiver operating characteristic (ROC) curve. The histogram metrics of the diffusion and IVIM parameters were significantly different for molecular prognostic factors such as human epidermal receptor factor-2 (HER2), progesterone receptor, estrogen receptor, and ki-67. All histogram metrics displayed a poor correlation with all groups (r = −0.28-0.29). There were significant differences in the histogram metrics for the Luminal B-HER2 (−) vs. HER2-positive (non-luminal) subtypes in the mean and 10th percentile D, with the area under the curves (AUCs) of 0.742 and 0.700, respectively, and for the Luminal A and HER2-positive (non-luminal) subtypes in the 90th percentile and entropy of D*, with AUCs of 0.769 and 0.727, respectively. The histogram metrics of IVIM parameters exhibited links with breast cancer prognosis factors and combined subtypes. [ABSTRACT FROM AUTHOR]

Abstract Background There is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC. Methods Seventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model. Results ADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P

Abstract Objective This study was based on MRI features and number of tumor-infiltrating CD8 + T cells in post-operative pathology, in predicting meningioma recurrence risk. Methods Clinical, pathological, and imaging data of 102 patients with surgically and pathologically confirmed meningiomas were retrospectively analyzed. Patients were divided into recurrence and non-recurrence groups based on follow-up. Tumor-infiltrating CD8 + T cells in tissue samples were quantitatively assessed with immunohistochemical staining. Apparent diffusion coefficient (ADC) histogram parameters from preoperative MRI were quantified in MaZda. Considering the high correlation between ADC histogram parameters, we only chose ADC histogram parameter that had the best predictive efficacy for COX regression analysis further. A visual nomogram was then constructed and the recurrence probability at 1- and 2-years was determined. Finally, subgroup analysis was performed with the nomogram. Results The risk factors for meningioma recurrence were ADCp1 (hazard ratio [HR] = 0.961, 95% confidence interval [95% CI]: 0.937 ~ 0.986, p = 0.002) and CD8 + T cells (HR = 0.026, 95%CI: 0.001 ~ 0.609, p = 0.023). The resultant nomogram had AUC values of 0.779 and 0.784 for 1- and 2-years predicted recurrence rates, respectively. The survival analysis revealed that patients with low CD8 + T cells counts or ADCp1 had higher recurrence rates than those with high CD8 + T cells counts or ADCp1. Subgroup analysis revealed that the AUC of nomogram for predicting 1-year and 2-year recurrence of WHO grade 1 and WHO grade 2 meningiomas was 0.872 (0.652) and 0.828 (0.751), respectively. Conclusions Preoperative ADC histogram parameters and tumor-infiltrating CD8 + T cells may be potential biomarkers in predicting meningioma recurrence risk. Clinical relevance statement The findings will improve prognostic accuracy for patients with meningioma and potentially allow for targeted treatment of individuals who have the recurrent form.

Objectives: Our aim is to estimate the long-term neurological sequelae and prognosis in term neonatal asphyxia treated with hypothermia via volumetric apparent diffusion coefficient (ADC) map histogram analysis (HA). Methods: Brain MRI studies of 83 term neonates with asphyxia who received whole-body hypothermia treatment and examined between postnatal (PN) fourth and sixth days were retrospectively re-evaluated by 2 radiologists. Volumetric HA was performed for the areas frequently affected in deep and superficial asphyxia (thalamus, lentiform nucleus, posterior limb of internal capsule, corpus callosum forceps major, and perirolandic cortex-subcortical white matter) on ADC map. The quantitative ADC values were obtained separately for each region. Qualitative-visual (conventional) MRI findings were also re-evaluated. Neonates were examined neurodevelopmentally according to the Revised Brunet-Lezine scale. The distinguishability of long-term neurodevelopmental outcomes was statistically investigated. Results: With HA, the adverse neurodevelopmental outcomes could only be distinguished from mild-moderated impairment and normal development at the thalamus with 10th percentile ADC (P = .02 and P = .03, respectively) and ADCmin (P = .03 and P = .04, respectively). Also with the conventional MRI findings, adverse outcome could be distinguished from mild-moderated impairment (P = .04) and normal development (P = .04) via cytotoxic oedema of the thalamus, corpus striatum, and diffuse cerebral cortical. Conclusion: The long-term adverse neurodevelopmental outcomes in newborns with asphyxia who received whole-body hypothermia treatment can be estimated similarly with volumetric ADC-HA and the conventional assessment of the ADC map. Advances in knowledge: This study compares early MRI ADC-HA with neurological sequelae in term newborns with asphyxia who received whole-body hypothermia treatment. We could not find any significant difference in predicting adverse neurological sequelae between the visual-qualitative evaluation of the ADC map and HA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

How much green space is available for individuals is a major question that city planners are generally interested in, and the present study aimed to address this issue in the context of Vellore, India, through two approaches, namely, the per capita and the geographical area approach. In existing studies, urban green space (UGS) was only calculated at the macro level, i.e., for the city as a whole. Micro-or ward-level analysis was not attempted before, and the present study carried out the same to get a clear picture of the amount of greenery available in each ward of a city. For this purpose, a two-step approach was proposed where the histograms of Google Earth (GE) images were analyzed first to check whether the green cover types such as trees, shrubs/grassland, and cropland were spectrally different. Then, classification techniques such as ISODATA, maximum likelihood, support vector machine (SVM), and objectbased methods were applied to the GE images. It was found that SVM performed well in extracting different green cover types with the highest overall accuracy of 93% and Kappa coefficient of 0.881. It was found that when considering the city as a whole, the amount of UGS available is 42% of the total area, which is more than the recommended range of 20-40%. Similarly, the available UGS per person is 97.84 m2, which is far above the recommended 12 m2/person. However, the micro-level analysis revealed that some of the wards have not satisfied the criteria of per capita and percentage area, though the city as a whole has satisfied both the criteria. Thus, the results indicate the importance of calculating the urban green space availability at the ward level rather than the city level as the former gives a closer look at the surplus and deficit areas. The results of terrestrial LiDAR survey at individual tree level revealed that if trees are located adjacent to buildings or roads, it results in fewer heat islands compared to the case where there are no trees. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the amide proton transfer (APT), tumor blood flow (TBF), and apparent diffusion coefficient (ADC) combined diagnostic value for differentiating intracranial malignant tumors (MTs) from benign tumors (BTs) in young patients, as defined by the 2021 World Health Organization classification of central nervous system tumors. Methods: Fifteen patients with intracranial MTs and 10 patients with BTs aged 0–30 years underwent MRI with APT, pseudocontinuous arterial spin labeling (pCASL), and diffusion-weighted imaging. All tumors were evaluated through the use of histogram analysis and the Mann–Whitney U test to compare 10 parameters for each sequence between the groups. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: The APT maximum, mean, 10th, 25th, 50th, 75th, and 90th percentiles were significantly higher in MTs than in BTs; the TBF minimum (min) was significantly lower in MTs than in BTs; TBF kurtosis was significantly higher in MTs than in BTs; the ADC min, 10th, and 25th percentiles were significantly lower in MTs than in BTs (all p < 0.05). The APT 50th percentile (0.900), TBF min (0.813), and ADC min (0.900) had the highest area under the curve (AUC) values of the parameters in each sequence. The AUC for the combination of these three parameters was 0.933. Conclusions: The combination of APT, TBF, and ADC evaluated through histogram analysis may be useful for differentiating intracranial MTs from BTs in young patients. [ABSTRACT FROM AUTHOR]

Determining the geometric elements required for the probabilistic calculation of flood bands for watercourses within a hydrographical basin can be achieved by extracting information from remote sensing digital recordings. Given the increasing frequency of extreme hydrological phenomena, the use of data obtained through aerial remote sensing offers the advantage of rapidly determining the geometric characteristics needed to generate profiles for the probabilistic calculation of flood bands for all watercourses within a hydrographical basin. [ABSTRACT FROM AUTHOR]

Background: The use of histogram analysis of computed tomography (CT) values is a potential method for differentiating between benign osteoblastic lesions (BOLs) and malignant osteoblastic lesions (MOLs). Purpose: To explore the diagnostic efficacy of histogram analysis in accurately distinguishing between BOLs and MOLs based on CT values. Material and Methods: A total of 25 BOLs and 25 MOLs, which were confirmed through pathology or imaging follow-up, were included in this study. FireVoxel software was used to process the lesions and obtain various histogram parameters, including mean value, standard deviation, variance, coefficient of variation, skewness, kurtosis, entropy value, and percentiles ranging from 1st to 99th. Statistical tests, such as two independent-sample t-tests and the Mann–Whitney U test with Bonferroni correction, were employed to compare the differences in histogram parameters between BOLs and MOLs. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficacy of each parameter. Results: Significant differences were observed in several histogram parameters between BOLs and MOLs, including the mean value, coefficient of variation, skewness, and various percentiles. Notably, the 25th percentile demonstrated the highest diagnostic efficacy, as indicated by the largest area under the curve in the ROC curve analysis. Conclusion: Histogram analysis of CT values provides valuable diagnostic information for accurately differentiating between BOLs and MOLs. Among the different parameters, the 25th percentile parameter proves to be the most effective in this discrimination process. [ABSTRACT FROM AUTHOR]

Preoperative prediction of meningioma consistency is of great clinical value for risk stratification and surgical approach selection. However, to date, objective quantitative criteria for predicting meningioma consistency have not been developed. This study aimed to investigate the predictive value of magnetic resonance imaging (MRI) T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) histogram parameters for meningioma consistency. We retrospectively analyzed the clinical, preoperative MRI, and pathological data of 103 patients with histopathologically confirmed meningiomas. Histogram parameters (mean, variance, skewness, kurtosis, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%) were calculated automatically on the whole tumor using MaZda software. Chi-square test, Mann–Whitney's U test, or independent samples t -test was used to compare clinical, conventional MRI features, and histogram parameters between soft and hard meningiomas. Receiver operating characteristic curve and binary logistic regression analysis were employed to assess the predictive performance of T2WI and ADC histogram parameters. Tumor enhancement was the only conventional MRI feature that was statistically different between soft and hard meningiomas. ADC mean , ADC p1 , ADC p10 , and ADC p50 among ADC histogram parameters, and T2 mean , T2 p1 , T2 p10 , T2 p50 , T2 p90 , and T2 p99 among T2WI histogram parameters showed statistically significant differences between soft and hard meningiomas (all P < 0.05). We found that all combined variables (combined all) had the best accuracy in predicting meningioma consistency, with area under the curve, sensitivity, specificity, accuracy, positive predictive, and negative predictive values of 0.873 (0.804–0.941), 88.89%, 67.50%, 80.58%, 81.20%, and 79.40%, respectively. Among them, combined T2 is the most beneficial for predicting meningioma consistency. Combined T2 demonstrated better predictive performance for meningioma consistency than combined ADC. T2WI and ADC histogram parameters may be imaging markers for predicting meningioma consistency. [ABSTRACT FROM AUTHOR]

Purpose: To study the capability of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) on subtype classification and grade differentiation for small renal cell carcinoma (RCC). Histogram analysis for apparent diffusion coefficient (ADC) was studied for comparison. Materials and methods: A total of 61 patients with small RCC (< 4 cm) were included in the retrospective study. MRI data were reviewed, including a multi-b (0–1500 s/mm2) multi-TE (51–200 ms) diffusion weighted imaging (DWI) sequence. Region of interest (ROI) was delineated manually on DWI to include solid tumor. For each patient, a D-T2 spectrum was fitted and segmented into 5 compartments, and the volume fractions VA, VB, VC, VD, VE were obtained. ADC mapping was calculated, and histogram parameters ADC 90th, 10th, median, standard deviation, skewness and kurtosis were obtained. All MRI metrices were compared between clear cell RCC (ccRCC) and non-ccRCC group, and between high-grade and low-grade group. Receiver operator curve analysis was used to assess the corresponding diagnostic performance. Results: Significantly higher ADC 90th, ADC 10th and ADC median, and significantly lower DR-CSI VB was found for ccRCC compared to non-ccRCC. Significantly lower ADC 90th, ADC median and significantly higher VB was found for high-grade RCC compared to low-grade. For identifying ccRCC from non-ccRCC, VB showed the highest area under curve (AUC, 0.861) and specificity (0.882). For differentiating high- from low-grade, ADC 90th showed the highest AUC (0.726) and specificity (0.786), while VB also displayed a moderate AUC (0.715). Conclusion: DR-CSI may offer improved accuracy in subtype identification for small RCC, while do not show better performance for small RCC grading compared to ADC histogram. [ABSTRACT FROM AUTHOR]

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption's effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm's capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm's efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics. [ABSTRACT FROM AUTHOR]

ObjectiveLimited studies have explored the utility of whole-lesion histogram analysis in discerning the subtypes and proliferation status of non-small cell lung cancer (NSCLC), despite its potential to provide comprehensive tissue assessment through the computation of additional quantitative metrics. This study sought to assess the significance of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram parameters in discriminating between squamous cell carcinoma (SCC) and adenocarcinoma (AC), and to examine the correlation of each parameter with the proliferative marker Ki-67.Materials and methodsPatients with space-occupying lesions detected by chest CT examination and with further routine MRI, DKI and IVIM functional sequence scans were enrolled. Based on the pathological results, seventy patients with NSCLC were selected and divided into AC and SCC groups. Histogram parameters of IVIM (D, D*, f) and DKI (Dapp, Kapp) were calculated, and the Mann–Whitney U test or independent samples t test was used to analyze the differences in each histogram parameter of the SCC and AC groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the histogram parameters. The correlation coefficient between histogram parameters and Ki-67 was calculated using Spearman’s or Pearson’s methods.ResultsThe D 10th percentile, D 90th percentile, D mean, D median, Dapp10th percentile, Dapp90th percentile, Dappmean, Dappmedian, Dappskewness, DappSD of the AC groups were significantly higher than those of the SCC groups, while the Kappentropy and KappSD of the SCC groups were significantly higher than those of the AC groups. All the above differences were statistically significant (all P < 0.05). ROC curve analysis revealed that Dappmean showed the best performance for differentiating AC from SCC lesions, with an area under the ROC curve of 0.832 (95% confidence interval [CI]: 0.707-0.919). But there was no statistically significant difference in diagnostic efficacy compared to other histogram parameters (all P>0.05). Dapp90thpercentile, Dappmean, Kappskewnes showed a slight negative correlation with Ki-67 expression (r value -0.340, -0.287, -0.344, respectively; P< 0.05), while the other histogram parameters showed no significant correlation with Ki-67 (all P > 0.05).ConclusionsOur study demonstrates the utility of IVIM and DKI histogram analyses in differentiating NSCLC subtypes, particularly AC and SCC. Correlations with the Ki-67 index suggest that Dappmean, Dapp90th percentile, and Kappskewness may serve as markers of tumor aggressiveness, supporting their use in NSCLC diagnosis and treatment planning.

Algal blooms, the spread of algae on the surface of water bodies, have adverse effects not only on aquatic ecosystems but also on human life. The adverse effects of harmful algal blooms (HABs) necessitate a convenient solution for detection and monitoring. Unmanned aerial vehicles (UAVs) have recently emerged as a tool for algal bloom detection, efficiently providing on-demand images at high spatiotemporal resolutions. This study developed an image processing method for algal bloom area estimation from the aerial images (obtained from the internet) captured using UAVs. As a remote sensing method of HAB detection, analysis, and monitoring, a combination of histogram and texture analyses was used to efficiently estimate the area of HABs. Statistical features like entropy (using the Kullback–Leibler method) were emphasized with the aid of a gray-level co-occurrence matrix. The results showed that the orthogonal images demonstrated fewer errors, and the morphological filter best detected algal blooms in real time, with a precision of 80%. This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.

Purpose: This study investigated the value of whole tumor apparent diffusion coefficient (ADC) histogram parameters and magnetic resonance imaging (MRI) semantic features in predicting meningioma progesterone receptor (PR) expression. Materials and methods: The imaging, pathological, and clinical data of 53 patients with PR-negative meningiomas and 52 patients with PR-positive meningiomas were retrospectively reviewed. The whole tumor was outlined using Firevoxel software, and the ADC histogram parameters were calculated. The differences in ADC histogram parameters and MRI semantic features were compared between the two groups. The predictive values of parameters for PR expression were assessed using receiver operating characteristic curves. The correlation between whole-tumor ADC histogram parameters and PR expression in meningiomas was also analyzed. Results: Grading was able to predict the PR expression in meningiomas (p = 0.012), though the semantic features of MRI were not (all p > 0.05). The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were able to predict meningioma PR expression (all p < 0.05). The predictive performance of the combined histogram parameters improved, and the combination of grade and histogram parameters provided the optimal predictive value, with an area under the curve of 0.849 (95%CI: 0.766–0.911) and sensitivity, specificity, ACC, PPV, and NPV of 73.08%, 81.13%, 77.14%, 79.20%, and 75.40%, respectively. The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were positively correlated with PR expression (all p < 0.05). Conclusion: Whole tumor ADC histogram parameters have additional clinical value in predicting PR expression in meningiomas. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the correlation between quantitative MR parameters and prognostic factors in prostate cancer (PCa). Method: A total of 186 patients with pathologically confirmed PCa who underwent preoperative multiparametric MRI (mpMRI), including synthetic MRI (SyMRI), were enrolled from two medical centers. The histogram metrics of SyMRI [T1, T2, proton density (PD)] and apparent diffusion coefficient (ADC) values were extracted. The Mann‒Whitney U test or Student's t test was employed to determine the association between these histogram features and the prognostically relevant factors. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the differentiation performance. Spearman's rank correlation coefficients were calculated to determine the correlations between histogram parameters and the International Society of Urological Pathology (ISUP) grade group as well as pathological T stage. Results: Significant correlations were found between the histogram parameters and the ISUP grade as well as pathological T stage of PCa. Among these histogram parameters, ADC_minimum had the strongest correlation with the ISUP grade (r = − 0.481, p < 0.001), and ADC_Median showed the strongest association with pathological T stage (r = − 0.285, p = 0.008). The ADC_10th percentile exhibited the highest performance in identifying clinically significant prostate cancer (csPCa) (AUC 0.833; 95% CI 0.771–0.883). When discriminating between the status of different prognostically relevant factors, a significant difference was observed between extraprostatic extension-positive and -negative cancers with regard to histogram parameters of the ADC map (10th percentile, 90th percentile, mean, median, minimum) and T1 map (minimum) (p = 0.002–0.032). Moreover, histogram parameters of the ADC map (90th percentile, maximum, mean, median), T2 map (10th percentile, median), and PD map (10th percentile, median) were significantly lower in PCa with perineural invasion (p = 0.009–0.049). The T2 values were significantly lower in patients with seminal vesicle invasion (minimum, p = 0.036) and positive surgical margin (10th percentile, 90th percentile, mean, median, and minimum, p = 0.015–0.025). Conclusion: Quantitative histogram parameters derived from synthetic MRI and ADC maps may have great potential for predicting the prognostic features of PCa. [ABSTRACT FROM AUTHOR]

To compare the diagnostic performance of different mathematical models for DWI and explore whether parameters reflecting spatial and temporal heterogeneity can demonstrate better diagnostic accuracy than the diffusion coefficient parameter in distinguishing benign and malignant breast lesions, using whole-tumor histogram analysis. This retrospective study was approved by the institutional ethics committee and included 104 malignant and 42 benign cases. All patients underwent breast magnetic resonance imaging (MRI) with a 3.0 T MR scanner using the simultaneous multi-slice (SMS) readout-segment ed echo-planar imaging (rs-EPI). Histogram metrics of Mono- apparent diffusion coefficient (ADC), CTRW, and FROC-derived parameters were compared between benign and malignant breast lesions, and the diagnostic performance of each diffusion parameter was evaluated. Statistical analysis was performed using Mann-Whitney U test and receiver operating characteristic (ROC) curve. The D FROC -median exhibited the highest AUC for distinguishing benign and malignant breast lesions (AUC = 0.965). The temporal heterogeneity parameter α CTRW -median generated a statistically higher AUC compared to the spatial heterogeneity parameter β CTRW -median (AUC = 0.850 and 0.741, respectively; p = 0.047). Finally, the combination of median values of CTRW parameters displayed a slightly higher AUC than that of FROC parameters, with no significant difference however (AUC = 0.971 and 0.965, respectively; p = 0.172). The diffusion coefficient parameter exhibited superior diagnostic performance in distinguishing breast lesions when compared to the temporal and spatial heterogeneity parameters. [ABSTRACT FROM AUTHOR]

Objectives To compare the diagnostic value of histogram features of multiple diffusion metrics in predicting early renal impairment in chronic kidney disease (CKD). Methods A total of 77 patients with CKD (mild group, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2) and 30 healthy controls (HCs) were enrolled. Diffusion-weighted imaging was performed by using single-shot echo planar sequence with 13 b values (0, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, 2000, and 2500 s/mm2). Diffusion models including mono-exponential (Mono), intravoxel incoherent motion (IVIM), stretched-exponential (SEM), and kurtosis (DKI) were calculated, and their histogram features were analysed. All diffusion models for predicting early renal impairment in CKD were established using logistic regression analysis, and diagnostic efficiency was compared among the models. Results All diffusion models had high differential diagnosis efficiency between the mild group and HCs. The areas under the curve (AUCs) of Mono, IVIM, SEM, DKI, and the combined diffusion model for predicting early renal impairment in CKD were 0.829, 0.809, 0.760, 0.825, and 0.861, respectively. There were no significant differences in AUCs except SEM and combined model, SEM, and DKI model. There were significant correlations between eGFR/serum creatinine and some of histogram features. Conclusions Histogram analysis based on multiple diffusion metrics was practicable for the non-invasive assessment of early renal impairment in CKD. Advances in knowledge Advanced diffusion models provided microstructural information. Histogram analysis further reflected histological characteristics and heterogeneity. Histogram analysis based on multiple diffusion models could provide an accurate and non-invasive method to evaluate the early renal damage of CKD. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the value of histogram analysis of postcontrast T1-weighted (T1C) and apparent diffusion coefficient (ADC) images in predicting the grade and proliferative activity of adult intracranial ependymomas. Methods: Forty-seven adult intracranial ependymomas were enrolled and underwent histogram parameters extraction (including minimum, maximum, mean, 1st percentile (Perc.01), Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, Perc.99, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, and entropy of T1C and ADC) using FireVoxel software. Differences in histogram parameters between grade 2 and grade 3 adult intracranial ependymomas were compared. Receiver operating characteristic curves and logistic regression analyses were conducted to evaluate the diagnostic performance. Spearman's correlation analysis was used to evaluate the relationship between histogram parameters and Ki-67 proliferation index. Results: Grade 3 intracranial ependymomas group showed significantly higher Perc.95, Perc.99, SD, variance, CV, and entropy of T1C; lower minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50 of ADC; and higher CV and entropy of ADC than grade 2 intracranial ependymomas group (all p < 0.05). Entropy (T1C) and Perc.10 (ADC) had a higher diagnostic performance with AUCs of 0.805 and 0.827 among the histogram parameters of T1C and ADC, respectively. The diagnostic performance was improved by combining entropy (T1C) and Perc.10 (ADC), with an AUC of 0.857. Significant correlations were observed between significant histogram parameters of T1C (r = 0.296–0.417, p = 0.001–0.044) and ADC (r = -0.428–0.395, p = 0.003–0.038). Conclusion: Whole-tumor histogram analysis of T1C and ADC may be a promising approach for predicting the grade and proliferative activity of adult intracranial ependymomas. [ABSTRACT FROM AUTHOR]

Objective: To investigate the application of whole‐tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA). Methods: Preoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion‐weighted imaging scans. Risk stratification was based on ESMO‐ESTRO‐ESP guidelines: low‐, intermediate‐, high‐intermediate‐, and high‐risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation. Results: A decreasing tendency in multiple ADC values was observed from the low‐ to high‐intermediate‐risk EEAs. The (low + intermediate)‐risk EEAs and low‐risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high‐intermediate + high)‐risk EEAs and non‐low‐risk EEAs (all P < 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)‐ and the low‐risk EEAs from the other EEAs, respectively. Conclusion: Whole‐tumor ADC histogram metrics might be helpful for preoperatively identifying low‐ and (low + intermediate)‐risk EEAs, facilitating personalized therapeutic planning. synopsis: The whole‐tumor ADC histogram metrics can be potential imaging biomarkers for preoperative risk stratification of endometrial endometrioid adenocarcinomas. [ABSTRACT FROM AUTHOR]

The article presents a new information technology for processing medical indicators, which is based on the method of histogram analysis. Histogram analysis, known as a key tool in medical statistics, allows visually and quantitatively evaluate the distribution of data, which becomes important for detecting asymmetry, deviations and other characteristics of the indicator base. The analysis of the existing algorithms for presenting and processing medical information shows that they do not fully satisfy the requirements for solving problems that require complex logical conclusions, taking into account the incompleteness and inconsistencies of the input data. To solve this problem, it is necessary to look for new technologies for processing medical data. It is proposed to use a histogram analysis of each indicator for the studied groups: a group of healthy patients (M1), a group of sick patients who received DASH treatment (M2), and a group of sick patients who were treated by the standard method (M3). The purpose of the article is to develop an information technology for the analysis of the probability of changes in the values of medical indicators in the course of the chosen method of treatment. The mathematical justification and algorithm of information technology for calculating medical indicators using the histogram method have been developed, fragments of normalized histograms and the obtained results are given. Information technology for processing medical indicators based on the method of histogram analysis is presented. Histogram analysis is an important tool in medical statistics and research. This method allows visually and quantitatively evaluate the distribution of data, which is useful for detecting deviations, asymmetry and other characteristics of distributions. Analysis of the indicators obtained by two different treatment methods showed both positive and negative changes, which require further research. A feature of the proposed information technology is that it allows assessing the state of the indicator as a whole, without reference to a specific patient. [ABSTRACT FROM AUTHOR]

How much green space is available for individuals is a major question that city planners are generally interested in, and the present study aimed to address this issue in the context of Vellore, India, through two approaches, namely, the per capita and the geographical area approach. In existing studies, urban green space (UGS) was only calculated at the macro level, i.e., for the city as a whole. Micro-or ward-level analysis was not attempted before, and the present study carried out the same to get a clear picture of the amount of greenery available in each ward of a city. For this purpose, a two-step approach was proposed where the histograms of Google Earth (GE) images were analyzed first to check whether the green cover types such as trees, shrubs/grassland, and cropland were spectrally different. Then, classification techniques such as ISODATA, maximum likelihood, support vector machine (SVM), and object-based methods were applied to the GE images. It was found that SVM performed well in extracting different green cover types with the highest overall accuracy of 93% and Kappa coefficient of 0.881. It was found that when considering the city as a whole, the amount of UGS available is 42% of the total area, which is more than the recommended range of 20–40%. Similarly, the available UGS per person is 97.84 m2, which is far above the recommended 12 m2/person. However, the micro-level analysis revealed that some of the wards have not satisfied the criteria of per capita and percentage area, though the city as a whole has satisfied both the criteria. Thus, the results indicate the importance of calculating the urban green space availability at the ward level rather than the city level as the former gives a closer look at the surplus and deficit areas. The results of terrestrial LiDAR survey at individual tree level revealed that if trees are located adjacent to buildings or roads, it results in fewer heat islands compared to the case where there are no trees.

Aim: The objective of the present study is to analyze whether the optic nerve is affected using histogram analysis on conventional MRI images in patients with Behçet's disease. Materials and Methods: Gender and age matched patients between the ages of 21 and 75 and a healthy control group were included in the study. Right and left optic nerves of all patients were evaluated by a neuroradiologist using T2-weighted MR images. The optic nerve examination data of the patients were evaluated by histogram analysis. The workstation was a 27 inch iMac computer manufactured by Apple Inc. Cupertino, 88 California, USA. Results: There was no statistically significant difference between the two groups in terms of age and gender (p:0.927, p:0.753, respectively). Histogram analysis mean and median values in Behçet's patients were statistically significantly higher than in the healthy control group (p : 0.015, p : 0.006, respectively). The histogram analysis values above the 90th percentile were found to be significantly lower in Behçet's patients with visual symptoms compared to those without visual symptoms. Conclusion: As a result, this study shows that non-macroscopic optic nerve changes can be detected with histogram data analysis of MRI in diseases that can affect the optic nerve such as Behçet's disease. Therefore, we recommend evaluating conventional MR images together with histogram analysis in diseases where optic nerve damage is expected. [ABSTRACT FROM AUTHOR]

This project aims to investigate the diagnostic performance of multiple overlapping-echo detachment imaging (MOLED) technique-derived transverse relaxation time (T 2) maps in predicting progesterone receptor (PR) and S100 expression in meningiomas. 63 meningioma patients were enrolled from October 2021 to August 2022, who underwent a complete routine magnetic resonance imaging and T 2 MOLED, which can characterize the whole brain transverse relaxation time within 32 seconds in a single scan. After the surgical resection of meningiomas, the expression levels of PR and S100 were determined by an experienced pathologist using immunohistochemistry techniques. Histogram analysis was performed in tumor parenchyma based on the parametric maps. Independent t test and Mann-Whitney U test were applied for the comparison of histogram parameters between different groups, with a significance level of P <.05. Logistic regression and receiver operating characteristic (ROC) analysis with 95% confidence interval were conducted for the diagnostic efficiency evaluation. PR-positive group had significantly elevated T 2 histogram parameters (P =.001-.049) compared to the PR-negative group. The multivariate logistic regression model with T 2 showed the highest area under the ROC curve (AUC) for predicting PR expression (AUC = 0.818). Additionally, the multivariate model also had the best diagnostic performance for predicting meningioma S100 expression (AUC = 0.768). The MOLED technique-derived T 2 maps can distinguish PR and S100 status in meningiomas preoperatively. [ABSTRACT FROM AUTHOR]

Objective: To investigate the predictive value of a model combining conventional MRI features and apparent diffusion coefficient (ADC) histogram parameters for meningioma recurrence. Materials and Methods: Seventy-two meningioma patients confirmed by surgical and pathological findings in our hospital (January 2017–June 2020) were retrospectively and divided into the recurrence and non-recurrence group. MaZda software was used to delineate the region of interest at the largest tumor level and generate histogram parameters. Univariate and multivariate logistic regression analysis were used to construct the nomogram for predicting recurrence. The predictive efficacy and diagnostic of this model were assessed by calibration and decision curve analysis, and receiver operating characteristic curve, respectively. Results: Maximum diameter, necrosis, enhancement uniformity, age, Simpson, tumor shape, and ADC first percentile (ADCp1) were significantly different between the two groups (p < 0.05), with the latter four being independent risk factors for recurrence. The model constructed combining the four factors had the best predictive efficacy, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.965(0.892–0.994), 90.3%, 92.6%, 88.9%, 83.3%, and 95.2%, respectively. The calibration curve showed good agreement between the model-predicted and actual probabilities of recurrence. The decision curve analysis indicated good clinical availability of the model. Conclusion: This model based on conventional MRI features and ADC histogram parameters can directly and reliably predict meningioma recurrence, providing a guiding basis for selecting treatment options and individualized treatment. [ABSTRACT FROM AUTHOR]

Consistency in the petrophysical and elastic properties is very critical for the characterization in low to intermediate (tight) porosity sandstone reservoirs. In this case study, we have applied an iterative and integrated workflow that provided consistency between the petrophysical and elastic properties using rock physics modeling scheme for the quantitative characterization of the low to intermediate porosity reservoir of Cretaceous (Pab) sandstone reservoir of the mature field in middle Indus basin of Pakistan. Before petrophysics and rock physics modeling (RPM), the well logs data quality is assessed and conditioned. We employed an inclusion-based rock physics model to estimate elastic (P-wave, S-wave, and density) properties by accounting for the effect of mineralogy using pore geometry (pore aspect ratio) variation. The RPM provided consistent elastic and petrophysical properties when compared with measured logs and improved lithofacies understanding in the tight gas reservoir. Finally, modeled elastic properties and lithofacies are assessed and characterized in a rock physics template (RPT) using an effective medium theory. The successful application of the integrated workflow exterminated the well log interpretation uncertainty by providing a consistency between the petrophysics and RPM, which can be extended for improved reservoir characterization and prospect evaluation across other areas with similar geological trends and reservoir distribution. [ABSTRACT FROM AUTHOR]

Background: Preoperative differentiation of atypical meningioma (AtM) from transitional meningioma (TrM) is critical to clinical treatment. Purpose: To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating AtM from TrM and its correlation with the Ki-67 proliferation index (PI). Methods: Clinical, imaging, and pathological data of 78 AtM and 80 TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile [ADCp1], 10th percentile [ADCp10], 50th percentile [ADCp50], 90th percentile [ADCp90], and 99th percentile [ADCp99]) were generated. The Mann–Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 PI was analyzed. Results: All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). Combined ADC histogram parameters (variance, skewness, kurtosis, ADCp90, and ADCp99) achieved the best diagnostic performance for distinguishing AtM from TrM. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.800%, 76.25%, 67.95%, 70.15%, 70.93%, and 73.61%, respectively. All histogram parameters were negatively correlated with Ki-67 PI (r = −0.012 to −0.293). Conclusion: ADC histogram analysis is a potential tool for non-invasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 PI. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the value of histogram analysis based on iodine-based material decomposition (IMD) images obtained through dualenergy computed tomography (DECT) to differentiate gastric schwannoma (GS) from gastric stromal tumor (GST) (≤ cm) preoperatively. Methods: From January 2015 to January 2023, 15 patients with GS and 30 patients with GST (≤ cm) who underwent biphasic contrast-enhanced scans using DECT were enrolled in this study. For each tumor, we reconstructed IMD images at the arterial phase (AP) and venous phase (VP). Nine histogram parameters were automatically extracted and selected using MaZda software based on the IMD of AP and VP, respectively, including mean, 1st, 10th, 50th, 90th, and 99th percentile of the iodine concentration value (Perc.01, Perc.10, Perc.50, Perc.90, and Perc.99), variance, skewness, and kurtosis. The extracted IMD histogram parameters were compared using the Mann-Whitney U-test. The optimal IMD histogram parameters were selected using receiver operating characteristic (ROC) curves. Results: Among the IMD histogram parameters of AP, the mean, Perc.50, Perc.90, Perc.99, variance, and skewness of the GS group were lower than that of the GST group (all P < 0.05). Among the IMD histogram parameters of VP, Perc.90, Perc.99, and the variance of the GS group was lower than those of the GST group (all P < 0.05). The ROC analysis showed that Perc.99 (AP) generated the best diagnostic performance with the area under the curve, sensitivity, and specificity being 0.960, 86.67%, and 93.33%, respectively, when using 71.00 as the optimal threshold. Conclusion: Histogram analysis based on IMD images obtained through DECT holds promise as a valuable tool for the preoperative distinction between GS and GST (≤ cm). [ABSTRACT FROM AUTHOR]

Abstract Background To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients. Methods This retrospective study included preoperative images of 194 rectal cancer patients at our hospital between May 2019 and April 2022. The postoperative histopathological examination served as the reference standard. The mean values of DCE-MRI quantitative perfusion parameters (K trans , K ep and V e ) and other HA features calculated from these parameters were compared between the pathological EMVI-positive and EMVI-negative groups. Multivariate logistic regression analysis was performed to establish the prediction model for pathological EMVI-positive status. Diagnostic performance was assessed and compared using the receiver operating characteristic (ROC) curve. The clinical usefulness of the best prediction model was further measured with patients with indeterminate MRI-defined EMVI (mrEMVI) score 2(possibly negative) and score 3 (probably positive). Results The mean values of K trans and V e in the EMVI-positive group were significantly higher than those in the EMVI-negative group (P = 0.013 and 0.025, respectively). Significant differences in K trans skewness, K trans entropy, K trans kurtosis, and V e maximum were observed between the two groups (P = 0.001,0.002, 0.000, and 0.033, respectively). The K trans kurtosis and K trans entropy were identified as independent predictors for pathological EMVI. The combined prediction model had the highest area under the curve (AUC) at 0.926 for predicting pathological EMVI status and further reached the AUC of 0.867 in subpopulations with indeterminate mrEMVI scores. Conclusions Histogram Analysis of DCE-MRI K trans maps may be useful in preoperative identification of EMVI in rectal cancer, particularly in patients with indeterminate mrEMVI scores.

Objective: To use quantitative MRI to assess gender differences in lateral pterygoid muscle (LPM) characteristics in patients with anterior disk displacement (ADD). Methods: Lateral pterygoid muscle of 51 patients diagnosed with temporomandibular joint disorders (TMD) who underwent T1‐weighted Dixon and T1‐mapping sequences were retrospectively analyzed. There were 34 female patients (10 with bilateral normal position disk [NP]; 24 with bilateral ADD) and 17 male patients (eight with bilateral NP; nine with bilateral ADD) among them. After controlling for age, differences in fat fraction, T1 value, volume and histogram features related to gender and disk status were tested with 2‐way ANCOVA or Quade ANCOVA with Bonferroni correction. Results: Volume of LPM in NP was significantly smaller than that of ADD (p < 0.001). Fat fraction of LPM in females with NP was significantly higher than males with NP (p < 0.05). Females with ADD showed a significantly higher T1 value (p < 0.05), and higher intramuscular heterogeneity than males with ADD. Conclusions: Lateral pterygoid muscle in female TMD patients presented more fatty infiltration in the NP stage and might present more fibrosis in the ADD stage compared with males. Together, this leads to more serious intramuscular heterogeneity during the pathogenesis of ADD in females. [ABSTRACT FROM AUTHOR]

Background: Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge. Purpose: To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging. Material and Methods: This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t -test. The ability of APTw for differential diagnosis was evaluated using logistic regression. Results: Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2–48 years) and 12 patients with wild-type tumor (aged 3–53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80. Conclusion: APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs. [ABSTRACT FROM AUTHOR]

A higher b-value Diffusion-weighted imaging (DWI) would improve the contrast between cancerous and noncancerous tissue. Apparent diffusion coefficient (ADC)-histogram analysis is a method that can provide statistical data and quantitative information on tumor heterogeneity. This study aimed to compare two high b-values (1000 and 2000 sec/mm2) DWI in tumor detection and diagnostic performance in identifying early-stage tumor rectal cancer. This blinded and blinded retrospective study involved 56 patients with rectal cancer and 45 patients. Two radiologists evaluated the qualitative detection parameters and quantitative parameters of the ADC evaluated histogram and compared them between two DWI sequences (b-value for 1000 sec/mm2 and 2000 sec/mm2). The characteristic curves were used to assess diagnostic administration for the ADC histogram in discriminating early-stage tumors. The b-value for 2000 sec/mm2 DWI significantly improved AUCs, sensitivity, specificity, and precision and decreased false-positive rate for detection compared to the b-value for 1000 sec/mm2 (p < 0.05). The mean and fifth percentile ADC value for stage I using the b-value for 1000 sec/mm2 DWI was significantly higher than stage ≥ II (p = 0.036II and 0.016 respectively), as the well as fifth, 10th, mean ADC of the fifth, 10th, and 25th ADC percentile at b-value for 2000 sec/mm2 (p = 0.031, 0.014, 0.035 and 0.025 respectively). The AUCs of the fifth percentile ADC at b-value for 2000 sec/mm2 DWI in both readers in differentiating the stage Ⅰ tumor were the highest (0.732 and 0.751). The b-value for 2000 sec/mm2 DWI could improve the accurate detection of rectal cancer. The fifth percentile ADC at b-value for 2000 sec/mm2 sec/mm2 DWI was more useful for discriminating early stage than the b-value for 1000 sec/mm2 DWI. [ABSTRACT FROM AUTHOR]

Background: We aimed to assess the role of histogram analysis of DCE-MRI parameters for accurately distinguishing renal clear cell carcinoma from renal hamartoma with minimal fat. Methods: Patients with renal tumors were enrolled from January 2013 to December 2015, including renal clear cell carcinoma (n = 39) and renal hamartoma (n = 10). Preoperative DCE-MR Imaging was performed, and whole-tumor regions of interest were drawn to obtain the corresponding histogram parameters, including skewness, kurtosis, frequency size, energy, quartile, etc. Histogram parameters differences between renal clear cell carcinoma and renal hamartoma with minimal fat were compared. The diagnostic value of each significant parameter in predicting malignant tumors was determined. Results: Histogram parameters of the DCE map contributed to differentiating the benign from malignant renal tumor groups. Histogram analysis of DCE maps could effectively present the heterogeneity of renal tumors and aid in differentiating benign and malignant tumors. ROC analysis results indicated that when frequency size = 1,732 was set as the threshold value, favorable diagnostic performance in predicting malignant tumors was achieved (AUC - 0.964; sensitivity - 84.6%; specificity - 100%), followed by skewness, Energy, Entropy, Uniformity, quartile 5, quartile 50, and kurtosis. Conclusions: Histogram analysis of DCE-MRI shows promise for differentiating benign and malignant renal tumors. Frequency size was the most significant parameter for predicting renal clear cell carcinoma. [ABSTRACT FROM AUTHOR]

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann–Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model's predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753–0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model's probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM. [ABSTRACT FROM AUTHOR]

Objective: In this study, the utility of histogram parameters derived from diffusion-weighted imaging to differentiate renal cell carcinoma (RCC) from renal minimal fat angiomyolipoma (MFAML) was investigated. Methods: In this retrospective study, 98 patients who were histopathologically diagnosed with RCC and MFAML and who underwent magnetic resonance imaging (MRI) examinations between 2015 and 2022 were included. Demographic data, preoperative MRI findings, MRI apparent diffusion coefficient (ADC) histogram analyses, operation types, and postoperative histopathological data of the patients were recorded. The mean, minimum (min), maximum (max), 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles as well as skewness, kurtosis, and variance of ADC values were calculated. Results: The study included 61 males and 37 females. Eighty eight of the patients had RCC and 10 had AML. In terms of age and gender, there was no significant difference between the two groups. The AML group's ADCmin, ADCmedian, ADCmean, ADCmax, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles were all lower than those of the RCC group. ADCmax value (p<0.001), as well as ADCmedian and the 50th, 75th, 90th, and 95th percentiles of ADC values (p<0.05), demonstrated a statistically significant difference. However, there was no statistical significance between ADCmin, ADCmean, and the 5th, 10th, and 25th percentiles of ADC values (p>0.05). The area under the curve, sensitivity, and specificity of the ADCmax value were 0.795, 62.4%, and 88.9%, respectively. Conclusion: A whole tumor histogram and textural analysis of ADC values could be useful in distinguishing MFAML from RCC. [ABSTRACT FROM AUTHOR]

To investigate the possibility of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating microcystic meningioma (MM) from intracranial solitary fibrous tumor (SFT). Eighteen patients with MM and 23 patients with SFT were enrolled in this retrospective study. Conventional magnetic resonance imaging (MRI) features and 9 ADC histogram parameters (including mean, first (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentiles ADC, as well as variance, skewness, and kurtosis) between MM and SFT were compared. The diagnostic performance of the optimal parameter was determined by the receiver operating characteristic analysis. SFT showed a significantly lower mean, ADC1, ADC10, ADC50, ADC90, and ADC99 than MM (all P < 0.05), while no significant difference was found in conventional MRI features or other ADC histogram parameters (all P > 0.05). ADC1 was identified as the optimal parameter in differentiating between MM and SFT, which achieved an area under the curve of 0.861, with sensitivity, specificity, and accuracy of 78.26%, 88.89%, and 82.93%, respectively. MM and SFT show overlapping conventional MRI features. ADC histogram analysis helps to differentiate between MM and SFT, with ADC1 being the optimal parameter with the best discrimination performance. [ABSTRACT FROM AUTHOR]