Comparison of two region-of-interest placement methods for histogram analysis of apparent diffusion coefficient maps for glioma grading

Objectives

We assessed the value of histogram analysis (HA) of apparent diffusion coefficient (ADC) maps for grading low-grade (LGG) and high-grade (HGG) gliomas.

Methods

We compared the diagnostic performance of two region-of-interest (ROI) placement methods (ROI 1: the entire tumor; ROI 2: the tumor excluding cystic and necrotic portions). We retrospectively evaluated 54 patients with supratentorial gliomas (18 LGG and 36 HGG). All subjects underwent standard 3T contrast-enhanced magnetic resonance imaging. Histogram parameters of ADC maps calculated with the two segmentation methods comprised mean, median, maxi-mum, minimum, kurtosis, skewness, entropy, standard deviation (sd), mean of positive pixels (mpp), uniformity of positive pixels, and their ratios (r) between lesion and normal white matter. They were compared using the independent t-test, chi-square test, or Mann-Whitney U test. For statistically significant results, receiver operating characteristic curves were constructed, and the optimal cutoff value, sensitivity, and specificity were determined by maximizing Youden's index.

Results

The ROI 1 method resulted in significantly higher rADC mean, rADC median, and rADC mpp for LGG than for HGG; these parameters had value for predicting the histological glioma grade with a cutoff (sensitivity, specificity) of 1.88 (77.8%, 61.1%), 2.25 (44.4%, 97.2%), and 1.88 (77.8%, 63.9%), respectively. The ROI 2 method resulted in significantly higher ADC mean, ADC median, ADC mpp, ADC sd, ADC max, rADC median, rADC mpp, rADC mean, rADC sd, and rADC max for LGG than for HGG, while skewness was lower for LGG than for HGG (0.27 [0.98] vs 0.91 [0.81], p = 0.014). In ROI 2, ADC median, ADC mpp, ADC mean, rADC median, rADC mpp, and rADC mean performed well in differentiating glioma grade with cutoffs (sensitivity, specificity) of 1.28 (77.8%, 88.9%), 1.28 (77.8%, 88.9%), 1.25 (77.8%, 91.7%), 1.81 (83.3%, 91.7%), 1.74 (83.3%, 91.7%), and 1.81 (83.3%, 91.7%), respectively.

Conclusions

HA parameters had value for grading gliomas. Ex-cluding cystic and necrotic portions of the tumor for measuring HA parameters was preferable to using the entire tumor as the ROI. In this segmentation, rADC median showed the highest performance in predicting histological glioma grade, followed by rADC mpp, rADC mean, ADC median, ADC mpp, and ADC mean.

Exploring alternatives for securing anatomical structures in capturing digital images: A comparative analysis

Teaching veterinary anatomy using digital platforms requires improved image quality, which may influence the fixation process. This study aimed to compare four embalming solutions for high-colour-quality images of different tissues compared to the original image. Four equine left pelvic limbs were cut into metameres and divided equally for application of 10% formaldehyde, 96% glycerine, 33% hypersaturated NaCl solution and modified Larssen solution, respectively, which was maintained for 3 days. After drying for 3 days at room temperature, photographs were obtained at time 0 (T0), without any fixation process (original colour); time 1 (T1), immediately after removal from the solutions; and every 24 h for 3 days (T2-T4). The image colour quality was investigated by digitally evaluating the cortical bone, tendon and bone marrow using histograms and CIEDE2000 as well as by 10 specialists in an online survey. CIEDE2000 and histograms revealed that all fixation solutions changed the original tissue colour at all the time points (p < 0.0001). According to the specialists, the 33% saline solution produced the best results compared to the original one. The modified Larssen solution demonstrated better results for the tendon, marrow and cortical bone at T3 (p = 0.0015). Considering the colour of digital images, the modified Larssen solution provided the best results; however, the visual evaluation by the specialists revealed the 33% saline solution as the best.

Development and validation of apparent diffusion coefficient histogram-based nomogram for predicting malignant transformation of sinonasal inverted papilloma

OBJECTIVES

To develop and validate a nomogram based on whole-tumour histograms of apparent diffusion coefficient (ADC) maps for predicting malignant transformation (MT) in sinonasal inverted papilloma (IP).

METHODS

This retrospective study included 209 sinonasal IPs with and without MT, which were assigned into a primary cohort (n = 140) and a validation cohort (n = 69). Eight ADC histogram features were extracted from the whole-tumour region of interest. Morphological MRI features and ADC histogram parameters were compared between the two groups (with and without MT). Stepwise logistic regression was used to identify independent predictors and to construct models. The predictive performances of variables and models were assessed using the area under the curve (AUC). The optimal model was presented as a nomogram, and its calibration was assessed.

RESULTS

Four morphological features and seven ADC histogram parameters showed significant differences between the two groups in both cohorts (all p < 0.05). Maximum diameter, loss of convoluted cerebriform pattern, ADC10th and ADCSkewness were identified as independent predictors to construct the nomogram. The nomogram showed significantly better performance than the morphological model in both the primary (AUC, 0.96 vs 0.88; p = 0.006) and validation (AUC, 0.96 vs 0.88; p = 0.015) cohorts. The nomogram showed good calibration in both cohorts. Decision curve analysis demonstrated that the nomogram is clinically useful.

CONCLUSIONS

The developed nomogram, which incorporates morphological MRI features and ADC histogram parameters, can be conveniently used to facilitate the pre-operative prediction of MT in IPs.

Centile reference curves of the ultrasound-based characteristics of the rectus femoris muscle composition in children at 4-11 years old

Quantitative diagnostic ultrasound has been proposed as a way to characterize muscle structure, but there is a lack of normative data for children. This study aims to establish age-specific normal ranges for echo-intensity (EI), cross-sectional area (CSA), muscular thickness (MT), and subcutaneous adipose thickness (SAT) values of the rectus femoris muscle in typically developing children. The study recruited 497 children (288 boys and 209 girls) aged 4-10.9 years (mean age 7.39 years), and muscle parameters were measured using 2D B-mode ultrasound. Percentile values and reference curves were calculated using the Lambda, Mu, and Sigma method (LMS). The results showed small variation between measurements for boys compared to girls, with the most significant difference in EI, CSA, and MT values. EI decreased with age, with the most pronounced curve in boys. SAT increased in both sexes, with a slightly higher increase in girls after the age of 9.0 years. This study provides the first age-specific reference norms for the rectus femoris muscle architecture in children, and further research is needed to validate these curves and determine their clinical utility.

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation

Color similarity has been a key feature for content-based image retrieval by contemporary search engines, such as Google. In this study, we compare the visual content information of images, obtained through color histograms, with their corresponding hashtag sets in the case of Instagram posts. In previous studies, we had concluded that less than 25% of Instagram hashtags are related to the actual visual content of the image they accompany. Thus, the use of Instagram images' corresponding hashtags for automatic image annotation is questionable. In this study, we are answering this question through the computational comparison of images' low-level characteristics with the semantic and syntactic information of their corresponding hashtags. The main conclusion of our study on 26 different subjects (concepts) is that color histograms and filtered hashtag sets, although related, should be better seen as a complementary source for image retrieval and automatic image annotation.

Gaussian mixture modeling of acceleration-derived signal for monitoring external physical load of tennis player

Introduction: With the widespread use of wearable sensors, various methods to evaluate external physical loads using acceleration signals measured by inertial sensors in sporting activities have been proposed. Acceleration-derived external physical loads have been evaluated as a simple indicator, such as the mean or cumulative values of the target interval. However, such a conventional simplified indicator may not adequately represent the features of the external physical load in sporting activities involving various movement intensities. Therefore, we propose a method to evaluate the external physical load of tennis player based on the histogram of acceleration-derived signal obtained from wearable inertial sensors. Methods: Twenty-eight matches of 14 male collegiate players and 55 matches of 55 male middle-aged players wore sportswear-type wearable sensors during official tennis matches. The norm of the three-dimensional acceleration signal measured using the wearable sensor was smoothed, and the rest period (less than 0.3 G of at least 5 s) was excluded. Because the histogram of the processed acceleration signal showed a bimodal distribution, for example, high- and low-intensity peaks, a Gaussian mixture model was fitted to the histogram, and the model parameters were obtained to characterize the bimodal distribution of the acceleration signal for each player. Results: Among the obtained Gaussian mixture model parameters, the linear discrimination analysis revealed that the mean and standard deviation of the high-intensity side acceleration value accurately classified collegiate and middle-aged players with 93% accuracy; however, the conventional method (only the overall mean) showed less accurate classification results (63%). Conclusion: The mean and standard deviation of the high-intensity side extracted by the Gaussian mixture modeling is found to be the effective parameter representing the external physical load of tennis players. The histogram-based feature extraction of the acceleration-derived signal that exhibit multimodal distribution may provide a novel insight into monitoring external physical load in other sporting activities.

Is histogram manipulation always beneficial when trying to improve model performance across devices? Experiments using a Meibomian gland segmentation model

Meibomian gland dysfunction (MGD) is caused by abnormalities of the meibomian glands (MG) and is one of the causes of evaporative dry eye (DED). Precise MG segmentation is crucial for MGD-related DED diagnosis because the morphological parameters of MG are of importance. Deep learning has achieved state-of-the-art performance in medical image segmentation tasks, especially when training and test data come from the same distribution. But in practice, MG images can be acquired from different devices or hospitals. When testing image data from different distributions, deep learning models that have been trained on a specific distribution are prone to poor performance. Histogram specification (HS) has been reported as an effective method for contrast enhancement and improving model performance on images of different modalities. Additionally, contrast limited adaptive histogram equalization (CLAHE) will be used as a preprocessing method to enhance the contrast of MG images. In this study, we developed and evaluated the automatic segmentation method of the eyelid area and the MG area based on CNN and automatically calculated MG loss rate. This method is evaluated in the internal and external testing sets from two meibography devices. In addition, to assess whether HS and CLAHE improve segmentation results, we trained the network model using images from one device (internal testing set) and tested on images from another device (external testing set). High DSC (0.84 for MG region, 0.92 for eyelid region) for the internal test set was obtained, while for the external testing set, lower DSC (0.69-0.71 for MG region, 0.89-0.91 for eyelid region) was obtained. Also, HS and CLAHE were reported to have no statistical improvement in the segmentation results of MG in this experiment.

Beyond mean value analysis - a voxel-based analysis of the quantitative MR biomarker water T2 in the presence of fatty infiltration in skeletal muscle tissue of patients with neuromuscular diseases

The main pathologies in the muscles of patients with neuromuscular diseases (NMD) are fatty infiltration and edema. Recently, quantitative magnetic resonance (MR) imaging for determination of the MR biomarkers proton density fat fraction (PDFF) and water T₂ (T_2w ) has been advanced. Biophysical effects or pathology can have different effects on MR biomarkers. Thus, for heterogeneously affected muscles, the routinely performed mean or median value analyses of MR biomarkers are questionable. Our work presents a voxel-based histogram analysis of PDFF and T_2w images to point out potential quantification errors. In 12 patients with NMD, chemical-shift encoding-based water-fat imaging for PDFF and T₂ mapping with spectral adiabatic inversion recovery (SPAIR) for T_2w determination was performed. Segmentation of nine thigh muscles was performed bilaterally (n = 216). PDFF and T₂ maps were coregistered. A voxel-based comparison of PDFF and T_2w showed a decreased T_2w with increasing PDFF. Mean T_2w and mean T_2w ^{without fatty voxels} (PDFF < 10%) show good agreement, whereas standard deviation (σ) T_2w and σ T_2w ^{without fatty voxels} show increasing difference with increasing values of σ. Thereby two subgroups can be observed, referring to muscles in which the exclusion of fatty voxels has a negligible influence versus muscles in which a strong dependency of the T_2w value distribution on the exclusion of fatty voxels is present. Because of the two opposite effects that influence T_2w in a voxel, namely, (i) a pathophysiologically increased water mobility leading to T_2w elevation, and (ii) a dependency of T_2w on the PDFF leading to decreased T_2w , the T_2w distribution within a muscle might be heterogenous and the routine mean or median analysis can lead to a misinterpretation of the muscle health. It was concluded that muscle T_2w mean values can wrongly suggest healthy muscle tissue. A deeper analysis of the underlying value distribution is necessary. Therefore, a quantitative analysis of T_2w histograms is a potential alternative.

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

BACKGROUND

Variations in axillary lymph node (ALN) metastatic potential between different breast cancers lead to microscopical alterations in tumor perfusion heterogeneity. This study investigated the usefulness of histogram metrics from iodine maps in the preoperative diagnosis of metastatic ALNs in patients with early-stage breast cancer.

METHODS

Between October 2020 and November 2021 enhanced spectral computed tomography (CT) was performed in female patients with breast cancer. Quantitative spectral CT parameters and histogram parameters (mean, median, maximum, minimum, 10th percentiles, 90th percentiles, kurtosis, skewness, energy, range, and variance) from iodine maps were compared between patients with metastatic and nonmetastatic ALNs. Continuous variables were compared using Student's t-test or Mann-Whitney U test. Categorical variables were compared using Pearson's chi-square tests or Fisher's exact tests. Associations between ALN status and imaging features were evaluated using Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis.

RESULTS

This study included 113 female patients (62 and 51 in the ALN-negative and ALN-positive groups, respectively). Tumor size, molecular subtypes, and location differed significantly between the ALN-negative and ALN-positive groups (P<0.05). None of the quantitative spectral CT parameters of mass between metastatic and nonmetastatic ALN groups were significantly different (P>0.05). Histogram parameters of iodine maps for breast cancers, including maximum, 10th percentile, range, and energy, were significantly higher in the metastatic ALNs group compared with the nonmetastatic ALNs group (P<0.05). Multivariable logistic regression analyses showed that tumor location and energy were independent predictors of metastatic ALNs in breast cancers. The combination of independent predictors yielded an area under the curve (AUC) of 0.824 (sensitivity 72.5%; specificity 74.2%).

CONCLUSIONS

Whole-lesion histogram parameters derived from spectral CT iodine maps may be used as a complementary noninvasive means for the preoperative identification of ALN metastases in patients with early-stage breast cancer.

Noise in ICUs: Review and Detailed Analysis of Long-Term SPL Monitoring in ICUs in Northern Spain

Intensive care units (ICUs) are busy and noisy areas where patients and professional staff can be exposed to acoustic noise for long periods of time. In many cases, noise levels significantly exceed the levels recommended by the official health organisations. This situation can affect not only patient recovery but also professional staff, making ICUs unhealthy work and treatment environments. To introduce the measures and reduce the acoustic noise in the ICU, acoustic noise levels should first be measured and then appropriately analysed. However, in most studies dealing with this problem, measurements have been performed manually over short periods, leading to limited data being collected. They are usually followed by insufficient analysis, which in turn results in inadequate measures and noise reduction. This paper reviews recent works dealing with the problem of excessively high noise levels in ICUs and proposes a more thorough analysis of measured data both in the time and frequency domains. Applied frequency domain analysis identifies the cyclic behaviour of the measured sound pressure levels (SPLs) and detects the dominant frequency components in the SPL time series. Moreover, statistical analyses are produced to depict the patterns and SPLs to which patients in ICUs are typically exposed during their stay in the ICU. It has been shown that the acoustic environment is very similar every night, while it can vary significantly during the day or evening periods. However, during most of the observed time, recorded SPLs were significantly above the prescribed values, indicating an urgent need for their control and reduction. To effectively tackle this problem, more detailed information about the nature of noise during each of the analysed periods of the day is needed. This issue will be addressed in the continuation of this project.

Glutathione peroxidase family and survival prognosis in patients with renal cell carcinoma

OBJECTIVES

Renal cell carcinoma (RCC) is a renal cortical tumor with high clinical incidence. The effect of glutathione peroxidases (GPXs) on RCC and the possible mechanism are still unclear. This study aims to explore the expression level of GPXs gene in RCC and its effect on the clinical prognosis of patients with RCC via bioinformatics analysis.

METHODS

The mRNA expressions of GPXs family genes were obtained from the public data of The Cancer Genome Atlas (TCGA) database. The Kruskal-Wails test was used to analyze the differences in mRNA expression of GPXs family genes between samples from patients with RCC and the normal population. UALAN databases were used to analyze the differences in protein expression of GPXs family genes between samples from patients with renal clear cell carcinoma and the normal population, and to evaluate the role of GPXs family genes in RCC. The Kaplan-Meier Plotter was used to analyze the correlation between different types of RCC and overall survival (OS), disease-free survival (DFS), disease-specific survival (DSS), and progression-free survival (PFS). Kaplan-Meier survival curve was drawn based on the GPX8 gene expression to study the relationship between GPX8 gene expression and prognosis of RCC patients. Based on the results of multivariate Cox regression analysis, a Nomogram scoring model for RCC prediction was established by introducing GPX8 gene.

RESULTS

The mRNA expressions of GPX1 and GPX4 were higher in the sample of renal chromophobe cell carcinoma, renal clear cell carcinoma, and renal papillary cell carcinoma than those in the normal population (all P<0.01), and GPX7 and GPX8 were significantly over-expressed in patients with renal papillary cell carcinoma and renal clear cell carcinoma (all P<0.01). Compared with the normal group, the protein expressions of GPX1, GPX2, GPX7, and GPX8 were increased significantly in renal clear cell carcinoma (all P<0.01), while GPX3 and GPX4 expressions were decreased significantly (both P<0.01). The protein expressions of GPX1, GPX2, GPX7, and GPX8 were increased significantly in patients with renal clear cell carcinoma at different tumor grades (all P<0.01), while GPX3 and GPX4 expressions were decreased significantly (both P<0.01). Survival analysis showed that OS, DFS, DSS, and PFS were all decreased in patients with clear cell carcinoma compared with patients with papillary cell carcinoma and chromophobe cell carcinoma. According to the GPX8 level, patients were assigned into the low, medium, and high expression groups. Compared with the low GPX8 level group, the OS (P<0.01), DFS (P=0.03), DSS (P<0.01), and PFS (P=3.18×10-7) were significantly decreased in the high level group. Univariate Cox proportional regression analysis showed that the high level of GPX8 was associated with poor OS of 3 different types of renal cancer. Multifactorial analysis showed that GPX8 was an independent factor affecting the OS of patients with renal papillary cell carcinoma. Race and post tumor node metastasis (pTNM) typing were independent factors influencing the OS of patients with renal clear cell carcinoma. GPX8 and pTMN were independent factors influencing the OS of patients with renal chromophobe cell carcinoma. Based on these variables, the Nomogram risk models of 3 types of cell carcinoma were established, and the discrimination and calibration of the models were evaluated using the Consistency index (C-index) and calibration curves. The C-index of the risk model of renal papillary cell carcinoma was 0.62 (95% CI 0.51 to 1.00, P=0.03). The results of receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) was 0.88. The C-index of the risk model of renal clear cell carcinoma was 0.72 (95% CI 0.52 to 1.00, P=0.03). The results of ROC curve showed that the AUC was 0.90. The C-index of the risk model of chromophobe cell carcinoma of kidney was 0.90 (95% CI 0.85 to 1.00, P<0.01). The results of ROC curve showed that the AUC was 0.59.

CONCLUSIONS

GPXs family genes, especially GPX8, are potential markers for poor prognosis of RCC, and the occurrence and development of RCC can be predicted in clinical practice based on the expressions of GPXs family genes.

The role of quantitative gray-scale ultrasound histogram in the differential diagnosis of infected and non-infected hydronephrosis

BACKGROUND

The early detection of infected hydronephrosis is critical before lithotripsy. A feasible and noninvasive diagnostic method is of considerable clinical attention.

OBJECTIVES

This retrospective study was performed to find some quantitative evaluation parameters of B-mode Gray-scale ultrasound histogram analysis that might assist the early diagnosis of infected hydronephrosis and test their diagnostic efficacy.

MATERIALS AND METHODS

The ultrasound images and clinical data of 245 patients with hydronephrosis were retrospectively analyzed. Image J software was applied to obtain the gray-scale maps and the analysis results of the signal strength. The difference in the data between the infected and non-infected groups and the diagnostic value of the parameters were calculated.

RESULTS

In this retrospective study, 70 patients with infected hydronephrosis and 175 patients with non-infected hydronephrosis were enrolled. The echogenicity of internal effusion and the echogenicity ratio of infected cases were significantly higher than those of non-infected cases (p < 0.05). The cutoff values were 23.82 (AUC = 0.859) of echogenicity of internal effusion, while 0.27 (AUC = 0.832) of echogenicity ratio.

CONCLUSION

The quantitative evaluation of gray-scale ultrasound histogram is an objective and reliable method in differentiating infected from non-infected hydronephrosis.

Which Parameter Influences Local Disease-Free Survival after Radiation Therapy Due to Osteolytic Metastasis? A Retrospective Study with Pre- and Post-Radiation Therapy MRI including Diffusion-Weighted Images

Although radiation therapy (RT) plays an important role in the palliation of localized bone metastases, there is no consensus on a reliable method for evaluating treatment response. Therefore, we retrospectively evaluated the potential of magnetic resonance imaging (MRI) using apparent diffusion coefficient (ADC) maps and conventional images in whole-tumor volumetric analysis of texture features for assessing treatment response after RT. For this purpose, 28 patients who received RT for osteolytic bone metastasis and underwent both pre- and post-RT MRI were enrolled. Volumetric ADC histograms and conventional parameters were compared. Cox regression analyses were used to determine whether the change ratio in these parameters was associated with local disease progression-free survival (LDPFS). The ADC_maximum, ADC_mean, ADC_median, ADC_SD, maximum diameter, and volume of the target lesions after RT significantly increased. Change ratios of ADC_mean < 1.41, tumor diameter ≥ 1.17, and tumor volume ≥ 1.55 were significant predictors of poor LDPFS. Whole-tumor volumetric ADC analysis might be utilized for monitoring patient response to RT and potentially useful in predicting clinical outcomes.

Whole-body diffusion-weighted MRI in lymphoma-comparison of global apparent diffusion coefficient histogram parameters for differentiation of diseased nodes of lymphoma patients from normal lymph nodes of healthy individuals

Background

Morphologic features yield low diagnostic accuracy to distinguish between diseased and normal lymph nodes. The purpose of this study was to compare diseased lymphomatous and normal lymph nodes using global apparent diffusion coefficient (gADC) histogram parameters derived from whole-body diffusion-weighted MRI (WB-DWI).

Methods

1.5 Tesla WB-DWI of 23 lymphoma patients and 20 healthy volunteers performed between 09/2010 and 07/2015 were retrospectively reviewed. All diseased nodal groups in the lymphoma cohort and all nodes visible on b900 images in healthy volunteers were segmented from neck to groin to generate a total diffusion volume (tDV). A connected component-labelling algorithm separated spatially distinct nodes. Mean, median, skewness, kurtosis, minimum, maximum, interquartile range (IQR), standard deviation (SD), 10^th and 90^th centile of the gADC distribution were derived from the tDV of each patient/volunteer and from spatially distinct nodes. gADC and regional nodal ADC parameters were compared between malignant and normal nodes using t-tests and ROC curve analyses. A P value ≤0.05 was deemed statistically significant.

Results

Mean, median, IQR, 10th and 90th centiles of gADC and regional nodal ADC values were significantly lower in diseased compared with normal lymph nodes. Skewness, kurtosis and tDV were significantly higher in lymphoma. The SD, min and max gADC showed no significant difference between the two groups (P>0.128). The diagnostic accuracies of gADC parameters by AUC from highest to lowest were: 10th centile, mean, median, 90th centile, skewness, kurtosis and IQR. A 10th centile gADC threshold of 0.68×10^-3 mm²/s identified diseased lymphomatous nodes with 91% sensitivity and 95% specificity.

Conclusions

WB-DWI derived gADC histogram parameters can distinguish between malignant lymph nodes of lymphoma patients and normal lymph nodes of healthy individuals.

Prediction of Inflammatory Breast Cancer Survival Outcomes Using Computed Tomography-Based Texture Analysis

Background: Although inflammatory breast cancer (IBC) has poor overall survival (OS), there is little information about using imaging features for predicting the prognosis. Computed tomography (CT)-based texture analysis, a non-invasive technique to quantify tumor heterogeneity, could be a potentially useful imaging biomarker. The aim of the article was to investigate the usefulness of chest CT-based texture analysis to predict OS in IBC patients.

Methods: Of the 3,130 patients with primary breast cancers between 2006 and 2016, 104 patients (3.3%) with IBC were identified. Among them, 98 patients who underwent pre-treatment contrast-enhanced chest CT scans, got treatment in our institution, and had a follow-up period of more than 2 years were finally included for CT-based texture analysis. Texture analysis was performed on CT images of 98 patients, using commercially available software by two breast radiologists. Histogram-based textural features, such as quantification of variation in CT attenuation (mean, standard deviation, mean of positive pixels [MPP], entropy, skewness, and kurtosis), were recorded. To dichotomize textural features for survival analysis, receiver operating characteristic curve analysis was used to determine cutoff points. Clinicopathologic variables, such as age, node stage, metastasis stage at the time of diagnosis, hormonal receptor positivity, human epidermal growth factor receptor 2 positivity, and molecular subtype, were assessed. A Cox proportional hazards model was used to determine the association of textural features and clinicopathologic variables with OS.

Results: During a mean follow-up period of 47.9 months, 41 of 98 patients (41.8%) died, with a median OS of 20.0 months. The textural features of lower mean attenuation, standard deviation, MPP, and entropy on CT images were significantly associated with worse OS, as was the M1 stage among clinicopathologic variables (all P-values < 0.05). In multivariate analysis, lower mean attenuation (hazard ratio [HR], 3.26; P = 0.003), lower MPP (HR, 3.03; P = 0.002), and lower entropy (HR, 2.70; P = 0.009) on chest CT images were significant factors independent from the M1 stage for predicting worse OS.

Conclusions: Lower mean attenuation, MPP, and entropy on chest CT images predicted worse OS in patients with IBC, suggesting that CT-based texture analysis provides additional predictors for OS.

Value of perfusion parameters and histogram analysis of triphasic computed tomography in pre-operative prediction of histological grade of hepatocellular carcinoma

BACKGROUND

Pre-operative non-invasive histological evaluation of hepatocellular carcinoma (HCC) remains a challenge. Tumor perfusion is significantly associated with the development and aggressiveness of HCC. The purpose of the study was to evaluate the clinical value of quantitative liver perfusion parameters and corresponding histogram parameters derived from traditional triphasic enhanced computed tomography (CT) scans in predicting histological grade of HCC.

METHODS

Totally, 52 patients with HCC were enrolled in this retrospective study and underwent triple-phase enhanced CT imaging. The blood perfusion parameters were derived from triple-phase CT scans. The relationship of liver perfusion parameters and corresponding histogram parameters with the histological grade of HCC was analyzed. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal ability of the parameters to predict the tumor histological grade.

RESULTS

The variance of arterial enhancement fraction (AEF) was significantly higher in HCCs without poorly differentiated components (NP-HCCs) than in HCCs with poorly differentiated components (P-HCCs). The difference in hepatic blood flow (HF) between total tumor and total liver flow (ΔHF = HFtumor - HFliver) and relative flow (rHF = ΔHF/HFliver) were significantly higher in NP-HCCs than in P-HCCs. The difference in portal vein blood supply perfusion (PVP) between tumor and liver tissue (ΔPVP) and the ΔPVP/liver PVP ratio (rPVP) were significantly higher in patients with NP-HCCs than in patients with P-HCCs. The area under ROC (AUC) of ΔPVP and rPVP were both 0.697 with a high sensitivity of 84.2% and specificity of only 56.2%. The ΔHF and rHF had a higher specificity of 87.5% with an AUC of 0.681 and 0.673, respectively. The combination of rHF and rPVP showed the highest AUC of 0.732 with a sensitivity of 57.9% and specificity of 93.8%. The combined parameter of ΔHF and rPVP, rHF and rPVP had the highest positive predictive value of 0.903, and that of rPVP and ΔPVP had the highest negative predictive value of 0.781.

CONCLUSION

Liver perfusion parameters and corresponding histogram parameters (including ΔHF, rHF, ΔPVP, rPVP, and AEFvariance) in patients with HCC derived from traditional triphasic CT scans may be helpful to non-invasively and pre-operatively predict the degree of the differentiation of HCC.

Value of Apparent Diffusion Coefficient Histogram Analysis in the Differential Diagnosis of Nasopharyngeal Lymphoma and Nasopharyngeal Carcinoma Based on Readout-Segmented Diffusion-Weighted Imaging

Background

This study aims to explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC) following readout-segmented echo-planar diffusion-weighted imaging (RESOLVE sequence).

Methods

Thirty-eight patients with NPL and 62 patients with NPC, who received routine head-and-neck MRI and RESOLVE (b-value: 0 and 1,000 s/mm²) examinations, were retrospectively evaluated as derivation cohort (February 2015 to August 2018); another 23 patients were analyzed as validation cohort (September 2018 to December 2019). The RESOLVE data were obtained from the MAGNETOM Skyra 3T MR system (Siemens Healthcare, Erlangen, Germany). Fifteen parameters derived from the whole-lesion histogram analysis (ADC_mean, variance, skewness, kurtosis, ADC₁, ADC₁₀, ADC₂₀, ADC₃₀, ADC₄₀, ADC₅₀, ADC₆₀, ADC₇₀, ADC₈₀, ADC₉₀, and ADC₉₉) were calculated for each patient. Then, statistical analyses were performed between the two groups to determine the statistical significance of each histogram parameter. A receiver operating characteristic curve (ROC) analysis was conducted to assess the diagnostic performance of each histogram parameter for distinguishing NPL from NPC and further tested in the validation cohort; calibration of the selected parameter was tested with Hosmer-Lemeshow test.

Results

NPL exhibited significantly lower ADC_mean, variance, ADC₁, ADC₁₀, ADC₂₀, ADC₃₀, ADC₄₀, ADC₅₀, ADC₆₀, ADC₇₀, ADC₈₀, ADC₉₀ and ADC₉₉, when compared to NPC (all, P < 0.05), while no significant differences were found on skewness and kurtosis. Furthermore, ADC₉₉ revealed the highest diagnostic efficiency, followed by ADC₁₀ and ADC₂₀. Optimal diagnostic performance (AUC = 0.790, sensitivity = 91.9%, and specificity = 63.2%) could be achieved when setting ADC₉₉ = 1,485.0 × 10^-6 mm²/s as the threshold value. The predictive performance was maintained in the validation cohort (AUC = 0.817, sensitivity = 94.6%, and specificity = 56.2%).

Conclusion

Whole-lesion ADC histograms based on RESOLVE are effective in differentiating NPC from NPL.

Whole-Tumor Histogram and Texture Imaging Features on Magnetic Resonance Imaging Combined With Epstein-Barr Virus Status to Predict Disease Progression in Patients With Nasopharyngeal Carcinoma

Purpose: We aimed to investigate whether Epstein–Barr virus (EBV) could produce differences on MRI by examining the histogram and texture imaging features. We also sought to determine the predictive value of pretreatment MRI texture analyses incorporating with EBV status for disease progression (PD) in patients with primary nasopharyngeal carcinoma (NPC).

Materials and Methods: Eighty-one patients with primary T2-T4 NPC and known EBV status who underwent contrast-enhanced MRI were included in this retrospective study. Whole-tumor-based histogram and texture features were extracted from pretreatment T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced (CE)-T1WI images. Mann–Whitney U-tests were performed to identify the differences in histogram and texture parameters between EBV DNA-positive and EBV DNA-negative NPC images. The effects of clinical variables as well as histogram and texture features were estimated by using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curve analysis was used to predict the EBV status and PD. Finally, an integrated model with the best performance was built.

Results: Of the 81 patients included, 54 had EBV DNA-positive NPC, and 27 had EBV DNA-negative NPC. Patients who were tested EBV DNA-positive had higher overall stage (P = 0.016), more lymphatic metastases (p < 0.0001), and easier distant metastases (P = 0.026) than the patients who were tested EBV DNA-negative. Tumor volume, T1WI_Skewness and T2WI_Kurtosis showed significant differences between the two groups. The combination of the three features achieved an AUC of 0.783 [95% confidence interval (CI) 0.678–0.888] with a sensitivity and specificity of 70.4 and 74.1%, respectively, in differentiating EBV DNA-positive tumors from EBV DNA-negative tumors. The combination of overall stage and tumor volume of T2WI_Kurtosis and EBV status was the most effective model for predicting PD in patients with primary NPC. The overall accuracy was 84.6%, with a sensitivity and specificity of 93.8 and 66.2%, respectively (AUC, 0.800; 95% CI 0.700–0.900).

Conclusion: This study demonstrates that MRI-based radiological features and EBV status can be used as an aid tool for the evaluation of PD, in order to develop tailored treatment targeting specific characteristics of individual patients.

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

Background/aim

The 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 methods

The 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.

Results

It 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.

Conclusion

In 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.

Using arterial spin labeling blood flow and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma from lymphoid hyperplasia

To investigate the feasibility of arterial spin labeling (ASL) blood flow (BF) and its histogram analysis to distinguish early-stage nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPLH).Sixty-three stage T1 NPC patients and benign NPLH patients underwent ASL on a 3.0-T magnetic resonance imaging system. BF histogram parameters were derived automatically, including the mean, median, maximum, minimum, kurtosis, skewness, and variance. Absolute values were obtained for skewness and kurtosis (absolute value of skewness [AVS] and absolute value of kurtosis [AVK], respectively). The Mann-Whitney U test, receiver operating characteristic curve, and multiple logistic regression models were used for statistical analysis.The mean, maximum, and variance of ASL BF values were significantly higher in early-stage NPC than in NPLH (all P < 0.0001), while the median and AVK values of early-stage NPC were also significantly higher than those of NPLH (all P < 0.001). No significant difference was found between the minimum and AVS values in early-stage NPC compared with NPLH (P = 0.125 and P = 0.084, respectively). The area under the curve (AUC) of the maximum was significantly higher than those of the mean and median (P < 0.05). The AUC of variance was significantly higher than those of the other parameters (all P < 0.05). Multivariate analysis showed that variance was the only independent predictor of outcome (P < 0.05).ASL BF and its histogram analysis could distinguish early-stage NPC from NPLH, and the variance value was a unique independent predictor.

Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models

OBJECTIVE

Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis.

METHODS

We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance.

RESULTS

The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4.

CONCLUSIONS

First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.

Quantitative brain histogram of canine epilepsy using magnetic resonance imaging

BACKGROUND

Quantitative magnetic resonance imaging (MRI) is used to study the anatomy of the brain in dogs with idiopathic epilepsy.

PURPOSE

To quantitate MRI images in terms of volumetric ratios and histogram analyses of the following regions of interest (ROI) in dogs with idiopathic epilepsy: frontal; parietal; temporal; piriform; thalamic; and hippocampal regions.

MATERIAL AND METHODS

Nine dogs with epilepsy and four healthy controls were evaluated. We examined the volumetric ratios and histogram analyses of six ROIs in all dogs.

RESULTS

MR images, in T1-weighted, T2-weighted, FLAIR, diffusion-weighted imaging, and apparent diffusion coefficient sequences detected changes in 4/9 (44%) epileptic dogs found in 5/6 regions: frontal; parietal; temporal; piriform; and hippocampal regions. However, no such changes were observed in the thalamic region. Interestingly, the frontal and piriform volumetric ratios of epileptic dogs were significantly lower than those of control dogs. The histogram analyses in 4/6 regions were significantly increased in epileptic dogs.

CONCLUSION

Our results demonstrated MRI finding abnormalities in several regions of the brain in several sequences including T1-weighted, T2-weighted, FLAIR, diffusion-weighted imaging, and apparent diffusion coefficient in epileptic dogs. In several regions of the brain, atrophy may exist, and hyperintensity may be present on MR images in epileptic dogs. These findings suggest that the diagnostic yield of MRI, which is an advanced neuroimaging technique, is high in epileptic dogs and has good reliability and sensitivity in detecting abnormal areas in patients.

One-minute whole-brain magnetization transfer ratio imaging with intrinsic B1 -correction

PURPOSE

Magnetization transfer ratio (MTR) histograms are used widely for the assessment of diffuse pathological changes in the brain. For broad clinical application, MTR scans should not only be fast, but confounding factors should also be minimized for high reproducibility. To this end, a 1-minute whole-brain spiral MTR method with intrinsic B₁ -field correction is introduced.

METHODS

A spiral multislice spoiled gradient-echo sequence with adaptable magnetization-transfer saturation pulses (angle β) is proposed. After a low-resolution single-shot spiral readout and a dummy preparation period, high-resolution images are acquired using an interleaved spiral readout. For whole-brain MTR imaging, 50 interleaved slices with three different magnetization-transfer contrasts (β = 0°, 350°, and 550°) together with an intrinsic B₁ -field map are recorded in 58.5 seconds on a clinical 3T system. From the three contrasts, two sets of MTR images are derived and used for subsequent B₁ correction, assuming a linear dependency on β. For validation, a binary spin bath model is used.

RESULTS

For the proposed B₁ -correction scheme, numerical simulations indicate for brain tissue a decrease of about a factor of 10 for the B₁ -related bias on MTR. As a result, following B₁ correction, MTR differences in gray and white matter become markedly accentuated, and the reproducibility of MTR histograms from scan-rescan experiments is improved. Furthermore, B₁ -corrected MTR histograms show a lower variability for age-matched normal-appearing brain tissue.

CONCLUSION

From its speed and offering intrinsic B₁ correction, the proposed method shows excellent prospects for clinical studies that explore magnetization-transfer effects based on MTR histogram analysis.

Non-Invasive Estimation of Glioma IDH1 Mutation and VEGF Expression by Histogram Analysis of Dynamic Contrast-Enhanced MRI

Objectives

To investigate whether glioma isocitrate dehydrogenase (IDH) 1 mutation and vascular endothelial growth factor (VEGF) expression can be estimated by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

Chinese Glioma Genome Atlas (CGGA) database was wined for differential expression of VEGF in gliomas with different IDH genotypes. The VEGF expression and IDH1 genotypes of 56 glioma samples in our hospital were assessed by immunohistochemistry. Preoperative DCE-MRI data of glioma samples were reviewed. Regions of interest (ROIs) covering tumor parenchyma were delineated. Histogram parameters of volume transfer constant (K^trans) and volume of extravascular extracellular space per unit volume of tissue (V_e) derived from DCE-MRI were obtained. Histogram parameters of K^trans, V_e and VEGF expression of IDH1 mutant type (IDH1^mut) gliomas were compared with the IDH1 wildtype (IDH1^wt) gliomas. Receiver operating characteristic (ROC) curve analysis was performed to differentiate IDH1^mut from IDH1^wt gliomas. The correlation coefficients were determined between histogram parameters of K^trans, V_e and VEGF expression in gliomas.

Results

In CGGA database, VEGF expression in IDH^mut gliomas was lower as compared to wildtype counterpart. The immunohistochemistry of glioma samples in our hospital also confirmed the results. Comparisons demonstrated statistically significant differences in histogram parameters of K^transand V_e [mean, standard deviation (SD), 50th, 75th, 90th. and 95th percentile] between IDH1^mutand IDH1^wtgliomas (P < 0.05, respectively). ROC curve analysis revealed that 50th percentile of K^trans (0.019 min⁻¹) and V_e (0.039) provided the perfect combination of sensitivity and specificity in differentiating gliomas with IDH1^mutfrom IDH1^wt. Irrespective of IDH1 mutation, histogram parameters of K^transand V_e were correlated with VEGF expression in gliomas (P < 0.05, respectively).

Conclusions

VEGF expression is significantly lower in IDH1^mut gliomas as compared to the wildtype counterpart, and it is non-invasively predictable with histogram analysis of DCE-MRI.

The Recalled Age of Initiation of Multiple Tobacco Products among 26-34 Year Olds: Findings from the Population Assessment of Tobacco and Health (PATH) Study Wave 1 (2013-2014)

This study examined the recalled age of initiation of seven different tobacco products (TPs) and explored potential influences of sex, race/ethnicity, and cigarette-smoking status on tobacco use initiation among adults 26-34 years old using the PATH study.

METHODS

Secondary analyses were conducted in the adult restricted PATH wave 1 (2013-2014) dataset. Weighted statistics are reported using the balanced repeated replication method and Fay's correction to account for PATH's complex study design. Distributions and histograms of the recalled age of initiation of seven different TPs (cigarettes, cigarillos, traditional cigars, filtered cigars, hookah, smokeless tobacco, and e-cigarettes) are reported, as well as the impact of sex and race/ethnicity using Cox proportional hazard models. The impact of cigarette-smoking status on the recalled age of initiation of each tobacco product other than cigarettes was explored.

RESULTS

The highest modes of the recalled age of initiation of cigarette use were at 14-15 and 15-16 years old. The distributions of the recalled age of initiation of cigarillos, traditional cigars, filtered cigars, hookah, and smokeless tobacco occurred later, with the highest modes at 15-16 and 17-18 years old. The distribution of the recalled age of initiation of e-cigarettes had a different shape than the other TPs, with the highest mode reported at 27-28 years old.

CONCLUSION

Due to the ever-changing tobacco marketplace, understanding when contemporary adults aged 26-34 years recall initiating TP use is important and will inform prevention researchers.