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.