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.

The role of histogram analysis of grayscale sonograms to differentiate thyroid nodules identified by 18F-FDG PET-CT

The role of histogram based on ultrasound (US) images for thyroid nodules found in fluorine-18 fluorodeoxyglucose (18F-FDG) Positron Emission Tomography/Computed Tomography (PET-CT) is unknown. We aimed to assess whether histogram analysis using gray scale US could differentiate thyroid nodules detected by PET-CT.In this study, 71 thyroid nodules ≥1 cm were identified in 71 patients by conducting 18F-FDG PET-CT, from January 2010 to June 2013. Subsequently, either grayscale US-guided fine needle aspirations or core needle biopsies were performed on each patient. Each grayscale US feature was categorized according to the Korean Thyroid Imaging Reporting and Data System (K-TIRADS). Histogram parameters (skewness, kurtosis, intensity, uniformity, and entropy) were extracted from the grayscale US images followed by statistical analysis using the Chi-Squared or Mann-Whitney U tests.The 71 nodules comprised 30 (42.3%) benign nodules, 30 (42.3%) primary thyroid malignancies, and 11 (15.4%) metastatic lesions. Tumor size, US findings, and histogram parameters were significantly different between the benign and malignant thyroid nodules (P = .011, P = .000, and P < .02, respectively). A comparison showed that parallel orientation and an absence of calcifications were found more frequently in metastatic thyroid nodules than in primary thyroid malignancies (P = .04, P < .000, respectively). However, histogram parameters and K-TIRADS were not significantly different between primary thyroid malignancies and metastatic lesions.There is a limit to replacing cytopathological confirmation with texture analysis for the differentiation of thyroid nodules detected by PET-CT. Therefore, cytopathological confirmation of nodules appearing malignant on US images cannot be avoided for an ultimate diagnosis of metastasis.

Statistical Research of Stainless Austenitic Steel Grain Size Distribution after Screw Rolling

Screw rolling of austenitic stainless-steel billets was conducted in two- and three-high mills. Statistical research results showed that, compared to heated but not rolled conditions, both screw rolling techniques provided a decrease of grain size, variance, grain size distribution asymmetry, and excess in the billet cross-section at the stationary stage of screw rolling. At that stage, grain size distribution after two-high screw rolling is closer to normal in terms of asymmetry and excess values compared to grain-size distribution after three-high screw rolling. A strong negative correlation between strain effective values and grain-size values for the cross-section of the rolled billets at the stationary stage was revealed for both two- and three-high screw rolling.

Oxygenation Instability Assessed by Oxygen Saturation Histograms during Supine vs Prone Position in Very Low Birthweight Infants Receiving Noninvasive Respiratory Support

OBJECTIVE

To evaluate the effect of prone vs supine position on the oxygenation instability among very low birth weight (VLBW) infants receiving noninvasive respiratory support, as assessed by the average oxygen saturation (SpO₂) histograms.

STUDY DESIGN

Sixty-nine histograms from 23 VLBW infants were studied prospectively. Each infant was studied during 3 consecutive 3-hour periods of alternating positions; 12 infants started the study while prone and 11 infants started supine, by random order. Histogram classification system was used to quantify oxygenation stability and time spent in different SpO₂ ranges.

RESULTS

The fraction of inspired oxygen values were similar in both positions. Unstable histograms were more common in supine vs prone position (20/34 [59%] vs 10/35 [29%]; P = .02, respectively). Analyzing oxygenation stability as per position change revealed that a change from prone to supine increased oxygenation instability, and supine to prone decreased instability (P = .02). In the supine vs prone position, percent of time spent in SpO₂ ≤80% and <90% was higher (5.0 ± 4.2 vs 2.4 ± 3.4 [P < .001] and 24.1 ± 13.7 vs 13.2 ± 10.0 [P < .001], respectively), and percent of time in SpO₂ >94% was lower (39.7 ± 26.0 vs 52.4 ± 23.4 [P = .04]).

CONCLUSIONS

Prone positioning decreased oxygenation instability and resulted in higher oxygenation among VLBW premature infants on noninvasive respiratory support. SpO₂ histograms allow easy bedside assessment of oxygenation instability, and quantification of the time spent at different SpO₂ ranges.

[Relationship of ADC histogram parameters with pathological grade and lymph node metastasis of prostate cancer]

OBJECTIVE

To investigate the relationship between the apparent diffusion coefficient (ADC) histogram parameters based on the whole tumor and the pathological grade and lymph node metastasis (LNM) of PCa.

METHODS

This retrospective study included 82 cases of PCa confirmed pathologically and subjected to MRI preoperatively. We obtained a series of ADC histogram parameters, such as ADCmean, ADCmedian, ADC25%, ADC75%, entropy, and histogram width, by processing the ADC images via the Firevoxel Post-Processing and the SPSS24 software. We compared the parameters between the high-risk and low- or moderate-risk groups as well as between the LNM-positive and LNM-negative groups of the patients, and analyzed the diagnostic performance of the parameters with statistically significant differences.

RESULTS

The high-risk group, compared with the low- or moderate-risk one, showed a significantly lower ADCmean (［590 ± 120］ vs ［837 ± 142］ ×10－6 mm2/s, P < 0.01), ADCmedian (［560 ± 117］ vs ［804 ± 139］ ×10－6 mm2/s, P < 0.01), ADC25% (［446.5 ± 98］ vs ［717 ± 118］ ×10－6 mm2/, P < 0.01) and ADC75% (［667 ± 132］ vs ［931 ± 167］ ×10－6 mm2/s, P < 0.01). The ADCmean manifested the highest diagnostic performance, with an AUC of 0.907, a sensitivity of 0.933 and a specificity of 0.796. No statistically significant difference was found between the high-risk and the low- or moderate-risk one in entropy (3.58 ± 0.39 vs 3.63 ± 0.42, P = 0.238) or the histogram width (［540 ± 73］ vs ［520 ± 65］ ×10－6 mm2/s, P = 0.086). Both entropy and the histogram width were remarkably higher in the LNM-positive than in the LNM-negative group (3.95 ± 0.41 vs 3.12 ± 0.45, P < 0.01; ［578 ± 59］ vs ［455 ± 68］ ×10－6 mm2/s, P < 0.01), and the former had an even higher diagnostic performance, with an AUC of 0.836, a sensitivity of 0.887 and a specificity of 0.781. There were no statistically significant differences between the LNM-positive and LNM-negative groups in the ADCmean (［768 ± 135］ vs ［790±128］ ×10－6 mm2/s, P = 0.402), ADCmedian (［759 ± 110］ vs ［775 ± 121］ ×10－6 mm2/s, P = 0.225), ADC25% (［643 ± 91］ vs ［657 ± 89］ ×10－6 mm2/s, P = 0.654) or ADC75% (［895 ± 127］ vs ［872 ± 129］ ×10－6 mm2/s, P = 0.926).

CONCLUSIONS

ADC histogram parameters are related to pathological grade and LNM of PCa, and the analysis of the ADC histogram based on the whole tumor has an important value for preoperative evaluation and prognostic estimation of the malignancy.

[Apparent Diffusion Coefficient Histogram Analysis:Differentiation of Genetic Subtypes of Diffuse Lower-grade Gliomas]

Objective To explore the utility of apparent diffusion coefficient(ADC)histogram analysis for differentiating genetic subtypes of diffuse lower-grade gliomas. Methods A total of 55 patients with WHO grade Ⅱ/Ⅲ diffuse lower-grade gliomas who underwent preoperative routine brain magnetic resonance imaging and diffusion weighted imaging in our center were retrospectively evaluated.Among whom there were 14 patients with isocitrate dehydrogenase(IDH)wild-type gliomas(IDH ^wt group),19 patients with IDH-mutant 1p19q intact gliomas(IDH ^mut1p19q ^int group),and 22 patients with IDH-mutant 1p19q co-deleted gliomas(IDH ^mut1p19q ^del group).The whole-lesion ADC values derived from histogram analysis(including ADC_mean,ADC_minimum,ADC5%,ADC10%,ADC25%,ADC50%,ADC75%,ADC90%,ADC95%,ADC_maximum,mode,range,skewness,kurtosis,standard deviation,inhomogeneity,and entrophy)were measured for each patient.All parameters between the different genetic subtypes were compared by using the Student's t test or Mann-Whitney U test.Receiver operating curve(ROC)analysis was used to assess the diagnostic performance of ADC histogram in distinguishing the different genetic subtypes. Results Compared with IDH ^wt group,the ADC75%(P=0.021),ADC90%(P=0.015),ADC95%(P=0.014),ADC_maximum (P=0.035),range(P=0.009),standard deviation(P=0.001)and inhomogeneity(P=0.001)were significantly lower in IDH ^mut group;in contrast,the ADC_minimum (P=0.031)and kurtosis(P=0.020)of IDH ^mut group were significantly higher than those in IDH ^wt group.The ADC_mean(P=0.010),ADC5%(P=0.016),ADC10%(P=0.012),ADC25%(P=0.007),ADC50%(P=0.005),ADC75%(P=0.015),and mode(P=0.002)were significantly higher in IDH ^mut1p19q ^int group than in IDH ^mut1p19q ^del group.Inhomogeneity achieved the highest area under ROC(AUC)(0.811)in differentiating IDH ^mut gliomas and IDH ^wt gliomas,with a cutoff value of 0.229;the sensitivity and specificity were 85.7% and 73.2%.The mode achieved the highest AUC(0.744)in differentiating IDH ^mut1p19q ^int gliomas and IDH ^mut1p19q ^del gliomas,with a cutoff value was 1448.75×10 ^-6 mm ²/s;the sensitivity and specificity were 57.9% and 90.9%.Conclusion ADC histograms analysis may be helpful to differentiate genetic subtypes in lower-grade gliomas.

Whole solid tumor volume histogram parameters for predicting the recurrence in patients with epithelial ovarian carcinoma: a feasibility study on quantitative DCE-MRI

BACKGROUND

Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers.

PURPOSE

To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC.

MATERIAL AND METHODS

We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann-Whitney U test, Pearson's Chi-squared test, or Fisher's exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

All histogram parameters of K^trans, k_ep, and v_e were not significantly different between EOC patients with and without recurrence (P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of K^trans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and k_ep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all P<0.05). ROC curves showed that the 5th percentile of K^trans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively.

CONCLUSION

Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.

The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions

Background

The present study analyzed whole-lesion histogram parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to explore the clinical value of IVIM histogram features in the differentiation of liver lesions.

Methods

In this retrospective study, 33 cases of hepatic hemangioma (HH), 22 cases of hepatic cysts (HC), and 34 cases of hepatocellular carcinoma (HCC) were underwent IVIM-DWI (b =0–600 s/mm²), which were confirmed pathologically and clinically. The data were processed by IVIM model to obtain the following quantitative indicators: perfusion fraction (f), slow diffusion coefficient (D), and pseudo-diffusion coefficient (or fast diffusion coefficient, D*). The region of interest in the largest solid part of the lesion was delineated for histogram analysis of the correlation between tissue image and lesion type. The relevant histogram parameters were obtained and statistically analyzed. The characteristic histogram parameters for HH, HC, and HCC were compared to find significantly different parameters. The diagnostic efficacies of these parameters for HH, liver cysts, and HCC were assessed using the receiver operating characteristic (ROC) curves.

Results

There were significant differences in the maximum diameter, maximum value, minimum value, mean, median, standard deviation, uniformity, skewness, kurtosis, volume, 10th percentile (P10) of D, and 90th percentile (P90) of D between the three groups (P<0.05). The maximum diameter, minimum value, entropy, and volume of D* differed significantly between the three groups (P<0.05). The maximum diameter, minimum value, mean, median, skewness, kurtosis, volume, P10, and P90 of f differed significantly between the three groups (P<0.05). The largest area under the ROC curve (AUC) for both D* and f was that of volume (AUC =0.883 for both). When 1438.802 was used as the volume cut-off, the sensitivity and specificity of volume in differentiating between HH and HC were 87.88 and 77.27, respectively, and the sensitivity and specificity of volume in differentiating between HC and HCC were 77.27 and 85.29.

Conclusions

A multiparametric histogram from IVIM-DWI magnetic resonance imaging (MRI) is an effective means of identifying HH, HC, and HCC that provides valuable reference information for clinical diagnosis.

Differential diagnosis of central lymphoma and high-grade glioma: dynamic contrast-enhanced histogram

BACKGROUND

In clinical diagnosis, some central nervous system lymphomas (CNSL) are difficult to distinguish from high-grade gliomas (HGG).

PURPOSE

To evaluate the diagnostic efficacy of the histogram analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in the identification of CNSL and HGG.

MATERIAL AND METHODS

In all, 43 patients diagnosed with HGG (n = 28) and CNSL (n = 15) by histopathology underwent DCE-MRI scanning. Differences in histogram parameters based on DCE-MRI between HGG and CNSL were analyzed by Mann-Whitney U test. In addition, receiver operating characteristic (ROC) analysis was performed. Short-term follow-up of patients was performed using Kaplan-Meier analysis to explore the survival rates of HGG and CNSL.

RESULTS

For the ROC curve analysis, we demonstrate that the 10th percentile of K^trans (area under the curve [AUC] = 0.912, sensitivity = 86.7%, specificity = 92.9%), K_ep (AUC = 0.940, sensitivity = 93.3%, specificity = 79.6%), V_e (AUC = 0.907, sensitivity = 86.7%, specificity = 89.3%), and AUC (AUC = 0.904, sensitivity = 86.7%, specificity = 92.9%) were significantly different between the CNSL and HGG groups (P < 0.001), with high diagnostic efficiency. Table 2 shows that the histogram features based on AUC maps (10th, 25th, median, 75th, 90th, and mean) were always significantly higher in the CNSL group than in the HGG group (P < 0.001). There was no significant difference in V_p or in the 75th, 90th and mean of K^trans, K_ep, and Ve between the CNSL and HGG groups (P > 0.05).

CONCLUSION

A histogram analysis of DCE-MRI identified significant differences between HGG and CNSL, and this will help in the clinical differential diagnosis of these conditions.

Quantification of heterogeneity to classify benign parotid tumors: a feasibility study on most frequent histotypes

Aim: To differentiate Warthin tumors (WTs) and pleomorphic adenomas (PAs) measuring heterogeneity of intravoxel incoherent motion (IVIM) and dynamic-contrast enhanced-magnetic resonance imaging biomarkers. Methods: Volumes of interest were traced on 18 WT and 18 PA in 25 patients. For each IVIM and dynamic-contrast enhanced biomarker, histogram parameters were calculated and then compared using the Wilcoxon-signed-rank test. Receiver operating characteristic curves and multivariate analysis were employed to identify the parameters and their pairs with the best accuracy. Results: Most of the biomarkers exhibited significant difference (p < 0.05) between PA and WT for histogram parameters. Time to peak median and skewness, and D* median and entropy showed the highest area under the curve. No meaningful improvement of accuracy was obtained using two features. Conclusion: IVIM and dynamic-contrast enhanced histogram descriptors may help in the classification of WT and PA.

CT Morphological Features Integrated With Whole-Lesion Histogram Parameters to Predict Lung Metastasis for Colorectal Cancer Patients With Pulmonary Nodules

Purpose: To retrospectively identify the relationships between both CT morphological features and histogram parameters with pulmonary metastasis in patients with colorectal cancer (CRC) and compare the efficacy of single-slice and whole-lesion histogram analysis. Methods: Our study enrolled 196 CRC patients with pulmonary nodules (136 in the training dataset and 60 in the validation dataset). Twenty morphological features of contrast-enhanced chest CT were evaluated. The regions of interests were delineated in single-slice and whole-tumor lesions, and 22 histogram parameters were extracted. Stepwise logistic regression analyses were applied to choose the independent factors of lung metastasis in the morphological features model, the single-slice histogram model and whole-lesion histogram model. The areas under the curve (AUC) was applied to quantify the predictive accuracy of each model. Finally, we built a morphological-histogram nomogram for pulmonary metastasis prediction. Results: The whole-lesion histogram analysis (AUC of 0.888 and 0.865 in the training and validation datasets, respectively) outperformed the single-slice histogram analysis (AUC of 0.872 and 0.819 in the training and validation datasets, respectively) and the CT morphological features model (AUC of 0.869 and 0.845 in the training and validation datasets, respectively). The morphological-histogram model, developed with significant morphological features and whole-lesion histogram parameters, achieved favorable discrimination in both the training dataset (AUC = 0.919) and validation dataset (AUC = 0.895), and good calibration. Conclusions: CT morphological features in combination with whole-lesion histogram parameters can be used to prognosticate pulmonary metastasis for patients with colorectal cancer.

Whole-Liver Apparent Diffusion Coefficient Histogram Analysis for the Diagnosis and Staging of Liver Fibrosis

BACKGROUND

Conventional diffusion-weighted imaging is limited in the quantitative evaluation of liver fibrosis, and whole-liver apparent diffusion coefficient (ADC) histogram analysis might contribute to the diagnosis and staging of liver fibrosis.

PURPOSE

To explore the value of whole-liver ADC histogram parameters in the diagnosis and staging of liver fibrosis.

STUDY TYPE

Retrospective.

POPULATION

Twenty individuals with no liver disease and 86 patients with liver fibrosis, including 30 with chronic viral hepatitis, 29 with autoimmune hepatitis, and 27 with unexplained liver fibrosis patients.

FIELD STRENGTH/SEQUENCE

3.0T/T₁ -weighted, T₂ -weighted, and diffusion-weighted images.

ASSESSMENT

A region of interest (ROI) was drawn in each slice of the diffusion-weighted images. Whole-liver histogram parameters were obtained with dedicated software by accumulating all ROIs. The effectiveness of the parameters in differentiating stage 1 or greater (≥F1), stage 2 or greater (≥F2), and stage 3 or greater (≥F3) liver fibrosis was assessed.

STATISTICAL TESTS

Mann-Whitney U-test and receiver operating characteristic curve analysis.

RESULTS

Kurtosis, entropy, skewness, mode, and 90^th and 75^th percentiles exhibited significant differences among the pathological fibrosis stages (P < 0.05). Kurtosis was found to be the most meaningful parameter in differentiating fibrosis stages of the viral hepatitis, autoimmune hepatitis, and unexplained liver fibrosis group (area under the curve) (AUC = 0.793, 0.771, 0.798, respectively). In the combined liver fibrosis group, kurtosis achieved the highest AUC (0.801; 95% confidence interval [CI]: 0.702-0.900; sensitivity: 0.750; specificity: 0.850; positive likelihood ratio: 4.953; negative likelihood ratio: 0.302; positive predictive value: 0.946; negative predictive value: 0.486), with a cutoff value of 1.817, in differentiating fibrosis stage ≥F1.

DATA CONCLUSION

Kurtosis, entropy, skewness, mode, and 90^th and 75^th percentiles may contribute to the diagnosis and staging of liver fibrosis, especially kurtosis.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1745-1754.

IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation

Automated image analysis tools for Ki67 breast cancer digital pathology images would have significant value if integrated into diagnostic pathology workflows. Such tools would reduce the workload of pathologists, while improving efficiency, and accuracy. Developing tools that are robust and reliable to multicentre data is challenging, however, differences in staining protocols, digitization equipment, staining compounds, and slide preparation can create variabilities in image quality and color across digital pathology datasets. In this work, a novel unsupervised color separation framework based on the IHC color histogram (IHCCH) is proposed for the robust analysis of Ki67 and hematoxylin stained images in multicentre datasets. An "overstaining" threshold is implemented to adjust for background overstaining, and an automated nuclei radius estimator is designed to improve nuclei detection. Proliferation index and F1 scores were compared between the proposed method and manually labeled ground truth data for 30 TMA cores that have ground truths for Ki67+ and Ki67- nuclei. The method accurately quantified the PI over the dataset, with an average proliferation index difference of 3.25%. To ensure the method generalizes to new, diverse datasets, 50 Ki67 TMAs from the Protein Atlas were used to test the validated approach. As the ground truth for this dataset is PI ranges, the automated result was compared to the PI range. The proposed method correctly classified 74 out of 80 TMA images, resulting in a 92.5% accuracy. In addition to these validations experiments, performance was compared to two color-deconvolution based methods, and to six machine learning classifiers. In all cases, the proposed work maintained more consistent (reproducible) results, and higher PI quantification accuracy.

Apparent diffusion coefficient (ADC) histogram analysis: differentiation of benign from malignant parotid gland tumors using readout-segmented diffusion-weighted imaging

OBJECTIVES

To explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating parotid gland tumors following readout-segmented diffusion-weighted imaging (RESOLVE).

METHODS

80 patients (40 with pleomorphic adenomas, 14 with Warthin tumors, and 26 with malignant parotid gland tumors) who underwent routine head-and-neck MRI and RESOLVE examinations, were retrospectively evaluated. RESOLVE data were acquired from a MAGNETOM Skyra 3T MR system. Eleven whole-lesion histogram parameters derived from histogram analysis (ADC_mean, ADC_minimum, ADC_maximum, ADC_1th, ADC_10th, ADC_50th, ADC_90th, ADC_99th, skewness, variance and kurtosis) were calculated for each patient using MaZda. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of the ADC for distinguishing among the three groups.

RESULTS

In total, nine parameters (ADC_minimum, ADC_maximum, ADC_mean, ADC_10th, ADC_50th, ADC_90th, ADC_99th, variance, skewness) were statistically significant (all p < 0.05) for all three groups, in the comparison of pleomorphic adenomas to Warthin tumors; the ADC_mean, ADC_50th, and skewness revealed high diagnostic efficiency with areas under the receiver operating characteristic curve of 0.976, 0.970, and 0.970, respectively. In the comparison of pleomorphic adenomas to malignant parotid gland tumors, these nine parameters were also found to be statistically different (all p < 0.05); the ADC_mean, ADC_10th and ADC_50th revealed high diagnostic efficiency with area under the curve of 0.851, 0.866, and 0.841, respectively. However, in the comparison of Warthin tumors to malignant parotid gland tumors, only three parameters (ADC_mean, ADC_50th, skewness) were statistically significant (all p < 0.05).

CONCLUSIONS

Whole-lesion ADC histograms are effective in differentiating common parotid gland tumors.

Repeatability analysis of ADC histogram metrics of the uterus

BACKGROUND

Recently, histogram analysis based on voxel-wise apparent diffusion coefficient (ADC) value distribution has been increasingly performed. However, few studies have been reported regarding its repeatability.

PURPOSE

To evaluate the repeatability of ADC histogram metrics of the uterus in clinical magnetic resonance imaging (MRI).

MATERIAL AND METHODS

Thirty-three female patients who underwent pelvic MRI including diffusion-weighted imaging (DWI) were prospectively included after providing informed consent. Two sequential DWI acquisitions with identical parameters and position were obtained. Regions of interest (ROIs) for histologically confirmed uterine lesions (five cervical and three endometrial cancers, and one endometrial hyperplasia) and normal appearing tissues (21 endometrium and 33 myometrium) were assigned on the first DWI dataset and then pasted onto the second DWI dataset. ADC histogram metrics within the ROIs were calculated and repeatability was evaluated by calculating within-subject coefficient of variance (%) (wCV (%)) and Bland-Altman plot (%).

RESULTS

ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy showed high repeatability (wCV (%) < 7, 95% limit of agreement in Bland-Altman plot (%) < ±20), followed by ADC minimum (wCV (%) = 8.12, 95% limit of agreement in Bland-Altman plot (%) < ±30). However, ADC skewness and kurtosis showed very low repeatability in all evaluations.

CONCLUSION

ADC histogram metrics like ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy are robust biomarkers and could be applicable to clinical use. However, ADC skewness and kurtosis lack robustness. Radiologists should keep these characteristics and limitations in mind when interpreting quantitative DWI.

Magnetic resonance texture analysis utility in differentiating intraparenchymal neurosarcoidosis from primary central nervous system lymphoma: a preliminary analysis

PURPOSE

Neurosarcoidosis and primary central nervous system lymphomas, although distinct disease entities, can both have overlapping neuroimaging findings. The purpose of our preliminary study was to assess if magnetic resonance texture analysis can differentiate parenchymal mass-like neurosarcoidosis granulomas from primary central nervous system lymphomas.

METHODS

A total of nine patients was evaluated, four with parenchymal neurosarcoidosis granulomas and five with primary central nervous system lymphomas. Magnetic resonance texture analysis was performed with commercial software using a filtration histogram technique. Texture features of different sizes and variations in signal intensity were extracted at six different spatial scale filters, followed by feature quantification using statistical and histogram parameters and 36 features were analysed for each sequence (T1-weighted, T2-weighted, fluid-attenuated inversion recovery, diffusion-weighted, apparent diffusion coefficient, T1-post contrast). The non-parametric Mann-Whitney test was used to evaluate the differences between different texture parameters.

RESULTS

The differences in distribution of entropy on T2-weighted imaging, apparent diffusion coefficient and T1-weighted post-contrast images were statistically significant on all spatial scale filters. Magnetic resonance texture analysis using medium and coarse spatial scale filters was especially useful in discriminating neurosarcoidosis from primary central nervous system lymphomas for mean, mean positive pixels, kurtosis, and skewness on diffusion-weighted imaging ( P < 0.004-0.030). At spatial scale filter 5, entropy on T2-weighted imaging ( P = 0.001) was the most useful discriminator with a cut-off value of 6.12 ( P = 0.001, area under the curve (AUC)-1, sensitivity (Sn)-100%, specificity (Sp)-100%), followed by kurtosis and skewness on diffusion-weighted imaging with a cut-off value of -0.565 ( P = 0.011, AUC-0.97, Sn-100%, Sp-83%) and-0.365 ( P = 0.008, AUC-0.98, Sn-100%, Sp-100%) respectively.

CONCLUSION

Filtration histogram-based magnetic resonance texture analysis appears to be a promising modality to distinguish parenchymal neurosarcoidosis granulomas from primary central nervous system lymphomas.

The features of cerebral permeability and perfusion detected by dynamic contrast-enhanced magnetic resonance imaging with Patlak model in relapsing-remitting multiple sclerosis

Objective

To investigate the features of cerebral permeability and perfusion detected by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with Patlak model in relapsing–remitting multiple sclerosis (RRMS) and their correlations with Expanded Disability Status Scale (EDSS) scores and disease duration.

Patients and methods

Twenty-seven RRMS patients underwent conventional MRI and DCE-MRI with 3.0 T magnetic resonance scanner were enrolled in the study. A Patlak model was used to quantitatively measure MRI biomarkers, including volume transfer constant (K^trans), fractional plasma volume (V_p), cerebral blood flow (CBF), and cerebral blood volume (CBV). The correlations of MRI biomarkers with EDSS scores and disease duration were analyzed.

Results

The MRI biomarkers K^trans, V_p, CBF, and CBV of contrast-enhancing (CE) lesions were significantly higher (P<0.05) than those of non-enhancing (NE) lesions and normal-appearing white matter (NAWM) regions. The skewness and kurtosis of K^trans values in CE lesions were significantly higher (P<0.05) than that of NE lesions. No significant correlation was found among the biomarkers with EDSS scores and disease duration (P>0.05).

Conclusion

Our study demonstrated the abnormalities of permeability and perfusion characteristics in multiple sclerosis (MS) lesions and NAWM regions by DCE-MRI with Patlak model. The K^trans, V_p, CBF, and CBV of CE lesions were significantly higher than that of NE lesions, but these MRI biomarkers did not associate with the severity and duration of the disease. The skewness and kurtosis of K^trans value in CE lesions were significantly higher than that in NE lesions, indicating that these parameters of K^trans histogram can be used to distinguish the pathology of MS lesions.

Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings

BACKGROUND

Although histological examination is the standard method for assessing genetic status, the development of a noninvasive method, which can display the heterogeneity of the whole tumor to supplement genotype analysis, might be important for personalized treatment strategies.

PURPOSE

To evaluate the potential role of diffusion kurtosis imaging (DKI)-derived parameters using histogram analysis derived from whole-tumor volumes for prediction of the status of KRAS mutations in patients with rectal adenocarcinoma.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 148 consecutive patients with histologically confirmed rectal adenocarcinoma who were treated at our institution.

SEQUENCE

DKI was performed with a 3.0 T MRI system using a single-shot echo-planar imaging sequence with b values of 0, 700, 1400, and 2100 sec/mm² .

ASSESSMENT

D, K, and apparent diffusion coefficient (ADC) values were measured using whole-tumor volume histogram analysis and were compared between different KRAS mutations status.

STATISTICAL TESTS

Student's t-test or Mann-Whitney U-test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

All the percentile metrics of ADC and D values were significantly lower in the mutated group than those in the wildtype group (all P < 0.05), except for the minimum value of ADC and D (both P > 0.05), while K-related percentile metrics were higher in the mutated group compared with those in the wildtype group (all P < 0.05). Regarding the comparison of the diagnostic performance of all the histogram metrics, K_75th showed the highest AUC value of 0.871, and the corresponding values for sensitivity, specificity, positive predictive value, and negative predictive value were 81.43%, 78.21%, 77.03%, and 82.43%, respectively.

DATA CONCLUSION

DKI metrics with whole-tumor volume histogram analysis is associated with KRAS mutations, and thus may be useful for predicting the KRAS status of rectal cancers for guiding targeted therapy.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:930-939.

A Robust Parameter-free Thresholding Method for Image Segmentation

In this work we presented a new parameter-free thresholding method for image segmentation. In separating an image into two classes, the method employs an objective function that not only maximizes the between-class variance but also the distance between the mean of each class and the global mean of the image. The design of the objective function aims to circumvent the challenge that many existing techniques encounter when the underlying two classes have very different sizes or variances. Advantages of the new method are two-fold. First, it is parameter-free, meaning that it can generate consistent results. Second, the new method has a simple form that makes it easy to adapt to different applications. We tested and compared the new method with the standard Otsu method, the maximum entropy method, and the 2D Otsu method on simulated and real biomedical and photographic images and found the new method can achieve a more accurate and robust performance.

A Radiodensity Histogram Study of the Brain in Multiple Sclerosis

Multiple sclerosis (MS) is a progressive neurodegenerative disease, affecting 1 million Americans and 2.5 million people globally. Although the diagnosis is made clinically, imaging plays a major role in diagnosing and monitoring disease progression and treatment response. Magnetic resonance imaging (MRI) has proven sensitive in imaging MS lesions, but the characterization offered by routine clinical MRI remains qualitative and with discrepancies between imaging and clinical findings. We investigated the ability of digital analysis of noncontrast head computed tomography (CT) images to detect global brain changes of MS. All routine diagnostic head CTs obtained on patients with known MS obtained from 1 of 2 scan platforms from 6/1/2011 to 6/1/2015 were reviewed. Head CT images from 54 patients with MS met inclusion criteria. Head CT images were processed and histogram metrics were compared to age- and gender- matched control subjects from the same CT scanners during the same time interval. Histogram metrics were correlated with plaque burden as seen on MRI studies. Compared with control subjects, patients had increased total brain radiodensity (P < .0001), further characterized as an increased histogram modal radiodensity (P < .0001) with decrease in histogram skewness (P < .0001). Radiodensity decreased with increasing plaque burden. Similar findings were seen in the patients with only mild plaque burden sub- group. Radiodensity is a unique tissue metric that is not measured by other imaging techniques. Our study finds that brain radiodensity histogram metrics highly correlate with MS, even in cases with minimal plaque burden.

Relative radiographic bone density measurement in revision hip arthroplasty and its correlation with qualitative subjective assessment by experienced surgeons

BACKGROUND

Conventional radiography (CR) is the imaging method of choice in monitoring bone remodelling and other stability parameters after total hip arthroplasty (THA). Quantitative roentgen- or computed-tomography-based methods to determine bone density are prone to metal artifacts and often very costly, which is why they are not used as standard in a clinical setting. Since subjective assessment of bone remodelling in CR also has a certain susceptibility to errors, semi-quantitative methods have been developed to help approximate periprosthetic bone density development via CR to open up an additional tool for documentation of radiographic THA follow-up.

OBJECTIVE

Proof-of-principle of a newly designed imaging-software-aided method to measure relative bone density around the femoral stem in a series of conventional radiographs following THA.

METHODS

Eighty-six patients with hip modular tapered, fluted titanium stems were selected from the clinical database and series of baseline and postoperative follow-up radiographs were obtained after 24 and 48 weeks. Relative bone densities were measured per Gruen zones G1-7 with the use of an open-source image analysis package (ImageJ) by means of greyscale histograms. In addition, subjective evaluation of selected cases was performed by three independent, blinded orthopedic surgeons. Besides descriptive and nonparametric analyses, intra-class correlation (ICC) was performed and objective and subjective results were compared by linear regression analysis.

RESULTS

Two individual cases are presented as a proof-of-principle. Increase or decrease of bone density could be measured correctly over time in each case. In a collective analysis there were no significant differences in mean relative bone densities between groups after 24 and 48 weeks, although a positive tendency was visible towards increased bone formation over time. Individual analyses by Gruen zones revealed that some zones, namely the proximal ones (e.g. G6), exhibit a broader scattering than others over time. This could be explained by the design of the evaluated tapered revision stem that achieves distal fixation and allows for proximal micromotion. Correlation analysis with subjective ratings (inter-rater reliability ICC = 0.71) showed a positive correlation with objective results, suggesting a feasibility of the method for clinical use.

CONCLUSIONS

In conclusion the presented method is an easy and accessible tool to quantify relative bone density changes during THA follow-up. It shows a positive correlation to established subjective assessment of bone remodelling and may therefore serve as a quantitative supplement in clinical documentation.

Volumetric voxelwise apparent diffusion coefficient histogram analysis for differentiation of the fourth ventricular tumors

PURPOSE

We applied voxelwise apparent diffusion coefficient (ADC) histogram analysis in addition to structural magnetic resonance imaging (MRI) findings and patients' age for differentiation of intraaxial posterior fossa tumors involving the fourth ventricle.

PARTICIPANTS AND METHODS

Pretreatment MRIs of 74 patients with intraaxial brain neoplasm involving the fourth ventricle, from January 1, 2004 to December 31, 2015, were reviewed. The tumor solid components were segmented and voxelwise ADC histogram variables were determined. Histogram-driven variables, structural MRI findings, and patient age were combined to devise a differential diagnosis algorithm.

RESULTS

The most common neoplasms were ependymomas ( n = 21), medulloblastoma ( n = 17), and pilocytic astrocytomas ( n = 13). Medulloblastomas followed by atypical teratoid/rhabdoid tumors had the lowest ADC histogram percentile values; whereas pilocytic astrocytomas and choroid plexus papillomas had the highest ADC histogram percentile values. In a multivariable multinominal regression analysis, the ADC 10th percentile value from voxelwise histogram was the only independent predictor of tumor type ( p < 0.001). In separate binary logistic regression analyses, the 10th percentile ADC value, tumor morphology, enhancement pattern, extension into Luschka/Magendie foramina, and patient age were predictors of different tumor types. Combining these variables, we devised a stepwise diagnostic model yielding 71% to 82% sensitivity, 91% to 95% specificity, 75% to 78% positive predictive value, and 89% to 95% negative predictive value for differentiation of ependymoma, medulloblastoma, and pilocytic astrocytoma.

CONCLUSION

We have shown how the addition of quantitative voxelwise ADC histogram analysis of the tumor solid component to structural findings and patient age can help with accurate differentiation of intraaxial posterior fossa neoplasms involving the fourth ventricle based on pretreatment MRI.

A Maximum-Entropy Method to Estimate Discrete Distributions from Samples Ensuring Nonzero Probabilities

When constructing discrete (binned) distributions from samples of a data set, applications exist where it is desirable to assure that all bins of the sample distribution have nonzero probability. For example, if the sample distribution is part of a predictive model for which we require returning a response for the entire codomain, or if we use Kullback–Leibler divergence to measure the (dis-)agreement of the sample distribution and the original distribution of the variable, which, in the described case, is inconveniently infinite. Several sample-based distribution estimators exist which assure nonzero bin probability, such as adding one counter to each zero-probability bin of the sample histogram, adding a small probability to the sample pdf, smoothing methods such as Kernel-density smoothing, or Bayesian approaches based on the Dirichlet and Multinomial distribution. Here, we suggest and test an approach based on the Clopper–Pearson method, which makes use of the binominal distribution. Based on the sample distribution, confidence intervals for bin-occupation probability are calculated. The mean of each confidence interval is a strictly positive estimator of the true bin-occupation probability and is convergent with increasing sample size. For small samples, it converges towards a uniform distribution, i.e., the method effectively applies a maximum entropy approach. We apply this nonzero method and four alternative sample-based distribution estimators to a range of typical distributions (uniform, Dirac, normal, multimodal, and irregular) and measure the effect with Kullback–Leibler divergence. While the performance of each method strongly depends on the distribution type it is applied to, on average, and especially for small sample sizes, the nonzero, the simple “add one counter”, and the Bayesian Dirichlet-multinomial model show very similar behavior and perform best. We conclude that, when estimating distributions without an a priori idea of their shape, applying one of these methods is favorable.

Measurement of the permeability, perfusion, and histogram characteristics in relapsing-remitting multiple sclerosis using dynamic contrast-enhanced MRI with extended Tofts linear model

Objective

To investigate the application value of using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with extended Tofts linear model for relapsing-remitting multiple sclerosis (RRMS) and its correlation with expanded disability status scale (EDSS) scores and disease duration.

Materials and Methods

Thirty patients with multiple sclerosis (MS) underwent conventional magnetic resonance imaging (MRI) and DCE-MRI with a 3.0 Tesla MR scanner. An extended Tofts linear model was used to quantitatively measure MR imaging biomarkers. The histogram parameters and correlation among imaging biomarkers, EDSS scores, and disease duration were also analyzed.

Results

The MR imaging biomarkers volume transfer constant (K^trans), volume of the extravascular extracellular space per unit volume of tissue (Ve), fractional plasma volume (V_p), cerebral blood flow (CBF), and cerebral blood volume (CBV) of contrast-enhancing (CE) lesions were significantly higher (P < 0.05) than those of nonenhancing (NE) lesions and normal-appearing white matter (NAWM) regions. The skewness of Ve value in CE lesions was more close to normal distribution. There was no significant correlation among the biomarkers with the EDSS scores and disease duration (P > 0.05).

Conclusions

Our study demonstrates that the DCE-MRI with the extended Tofts linear model can measure the permeability and perfusion characteristic in MS lesions and in NAWM regions. The K^trans, Ve, Vp, CBF, and CBV of CE lesions were significantly higher than that of NE lesions. The skewness of Ve value in CE lesions was more close to normal distribution, indicating that the histogram can be helpful to distinguish the pathology of MS lesions.

Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps

Background

The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions.

Methods

In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADC_mean), median ADC (ADC_median), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC₁₀, ADC₂₅, ADC₇₅, ADC₉₀) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model.

Results

Multivariate logistic regression showed ADC₁₀ (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC₁₀ and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%.

Conclusions

Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC₁₀ are valuable in differential diagnosis of orbital lymphoma and IOIP.

Histogram analysis of diffusion kurtosis imaging of nasopharyngeal carcinoma: Correlation between quantitative parameters and clinical stage

Purpose

To evaluate the correlation between histogram parameters derived from diffusion-kurtosis (DK) imaging and the clinical stage of nasopharyngeal carcinoma (NPC).

Results

High T-stage (T3/4) NPC showed significantly higher K_app-mean (P = 0.018), K_app-median (P = 0.029) and K_app-90th (P = 0.003) than low T-stage (T1/2) NPC. High N-stage NPC (N2/3) showed significantly lower Dapp-mean (P = 0.002), Dapp-median (P = 0.002) and Dapp-10th (P < 0.001) than low N-stage NPC (N0/1). High AJCC-stage NPC (III/IV) showed significantly lower D_app-10th (P = 0.038) than low AJCC-stage NPC (I/II). ROC analyses indicated that Kapp-90th was optimal for predicting high T-stage (AUC, 0.759; sensitivity, 0.842; specificity, 0.607), while Dapp-10th was best for predicting high N- and AJCC-stage (N-stage, AUC, 0.841; sensitivity, 0.875; specificity, 0.807; AJCC-stage, AUC, 0.671; sensitivity, 0.800; specificity, 0.588).

Materials and Methods

DK imaging data of forty-seven consecutive NPC patients were retrospectively analyzed. Apparent diffusion for Gaussian distribution (D_app) and apparent kurtosis coefficient (K_app) were generated using diffusion-kurtosis model. Histogram parameters, including mean, median, 10th, 90th percentiles, skewness and kurtosis of D_app and K_app were calculated. Patients were divided into low and high T, N and clinical stage based on American Joint Committee on Cancer (AJCC) staging system. Differences of histogram parameters between low and high T, N and AJCC stages were compared using t test. Multiple receiver operating characteristic (ROC) curves were used to determine and compare the value of significant parameters in predicting high T, N and AJCC stage, respectively.

Conclusions

DK imaging-derived parameters correlated well with clinical stage of NPC, therefore could serve as an adjunctive imaging technique for evaluating NPC.