Texture Analysis Parameters Research Articles

Role of CT colonography in differentiating sigmoid cancer from chronic diverticular disease.

To evaluate the accuracy of computed tomography colonography (CTC) in differentiating chronic diverticular disease from colorectal cancer (CRC), using morphological and textural parameters. We included 95 consecutive patients with histologically proven chronic diverticular disease (n = 53) or CRC (n = 42) who underwent CTC. One radiologist, unaware of histological findings, evaluated CTC studies for the presence of potential discriminators including: maximum thickness, involved segment length, shouldering phenomenon, growth pattern, diverticula, fascia thickening, fat tissue edema, loco-regional lymph nodes, mucosal pattern. Another radiologist performed volumetric texture analysis on the involved segment. Several qualitative imaging parameters resulted to significantly correlated with colorectal cancer, including absence of diverticula in the affected segment, straightened growth pattern and shouldering phenomenon. A maximum wall thickness/involved segment length ratio < 0.1 had 98% specificity and 47% sensitivity in identifying diverticular disease. Regarding first-order texture analysis parameters, kurtosis resulted to be significantly different between the two groups. Absence of diverticula, straightened growth pattern and shouldering phenomenon are significantly associated with CRC (71-91% sensitivity; 82-91%).

Evaluating the added benefit of CT texture analysis on conventional CT analysis to differentiate benign ovarian cysts.

We aimed to evaluate the benefit of adding CT texture analysis on conventional CT features of benign adnexal cystic lesions, especially in identifying mucinous cystadenoma. This retrospective study included patients who underwent surgical removal of benign ovarian cysts (44 mucinous cystadenomas, 32 serous cystadenomas, 16 follicular/simple cysts and 43 endometriotic cysts) at our institution between January 2015 and November 2017. The CT images were independently reviewed by an abdominal radiologist (reviewer 1) and a resident (reviewer 2). Both reviewers recorded the conventional characteristics and performed texture analysis. Based on reviewer 1's results, two decision trees for differential diagnosis were developed. Reviewer 2's results were then applied to the decision trees. The diagnostic performances of each reviewer with and without the decision trees were compared. Several conventional features and texture analysis parameters showed significant differences between mucinous cystadenomas and other benign adnexal cysts. The first decision tree selected septum number and thickness as significant features, whereas the second decision tree selected septum number and the mean values at spatial scaling factor (SSF) 0. Reviewer 1's performance did not change significantly with or without the use of the decision trees. Reviewer 2's interpretations were significantly less sensitive than reviewer 1's interpretations (p = 0.001). However, when aided by the first and second decision trees, Reviewer 2's interpretations were significantly more sensitive than reviewer 1's interpretations (86.4%, p < 0.001; 72.7%, p = 0.001). This study suggests the benefit of CT texture analysis on conventional images to differentiate mucinous cystadenoma from other benign adnexal cysts, particularly for less experienced radiologists.

The use of cardiac MRI texture analysis to investigate left ventricle tissue characterisation in a healthy UK population

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Medical Research Scotland(MRS) Guerbet Group Background Cardiac MR texture analysis (TA) has the potential to distinguish subtle differences amongst myocardial diseases, but with limited evidence in a healthy population. The aim of this study was to assess the application of TA to cardiac CINE MR Images of the left ventricle to evaluate variability and consistency in a large-scale healthy population. Method A cohort of 600 healthy volunteers was recruited from the Tayside Screening for Prevention of Cardiac Events study. All subjects underwent short axis CINE CMR at 3T (including LVM), using a 2D ECG-gated breath-hold segmented steady-state gradient echo sequence with constant field-of-view and pixel size. For each subject, the mid short-axis slices of the left ventricle, at ED and ES, were extracted for image analysis. The TA parameters (n = 50) for all images were derived using Mazda v4.7 by a single observer. Three different regions-of-interest (ROI) were applied to the LV myocardium at ED and ES, as follows: 1) ‘whole wall’ (figure 1 a and d); 2) ‘septal wall’ (figure 1 b and e); and 3) ‘lateral wall’ (figure 1 c and f). Statistical comparisons were made for all texture features to establish how they varied between ED and ES, males versus females, different age ranges (40-45 years, 46-54 years, 55-63 years, and ≥ 64 years) and also between those with small (66.8 ± 5.88 g/m2), media (98.2 ± 15.28 g/m2) and large (146.2 ± 16.81 g/m2) LVM. Finally, the images of 30 volunteers were analysed by a second observer to derive test-retest inter-observer variation as an index of measurement repeatability for each TA feature. Results Of the original 50 TA features tested, the means of n = 45-49 features (number dependent on whether whole wall, septal wall or lateral wall) were significantly different when compared between ED and ES (p &amp;lt; 0.05). For comparisons with gender, the means of n = 36-43 of the original features were significantly different (p &amp;lt; 0.05). Additionally, the means of n = 15-29 features were significantly different when tested between sub-cohorts of different ages (p &amp;lt; 0.05). When these data were combined together, ‘lateral wall’ was less sensitive, but for ‘whole wall’ and ‘septal wall’, the means of n = 7 of the original TA features were able to identify statistically significant differences between (i) ED and ES, (ii) males and females, (iii) different sub-cohorts of age, and (iv) different sub-cohorts of LVM (figure 2). Further, the root mean square test-retest inter-observer coefficient of variation associated with the repeatability of these measures was &amp;lt;10%. The n = 7 most useful TA features were ‘GrMean’, ‘GrNonZeros’, ‘Average_RLNonUni’, ‘Average_LngREmph’, ‘Average_ShrtREmph’, ‘Average_Fraction’ and ‘S5_Average_Entropy’. Conclusion These findings showed the repeatability of CMR texture anaysis and capability to identify differences in a healthy population. Further work may identify whether these features are able to differentiate between different cardiovascular diseases.

Multi-detector computed tomography (MDCT) imaging: association of bone texture parameters with finite element analysis (FEA)-based failure load of single vertebrae and functional spinal units.

Osteoporosis is a systemic skeletal disease that is characterized by low bone mass and microarchitectural deterioration, predisposing affected individuals to fragility fractures. Yet, standard measurement of areal bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) as the current reference standard has limitations for correctly detecting osteoporosis and fracture risk, with opportunistic osteoporosis screening using computed tomography (CT) showing increasing importance. This study's objective is to compare finite element analysis (FEA)-based vertebral failure load with parameters of texture analysis (TA) derived from multi-detector CT (MDCT). MDCT data of seven subjects (mean age: 71.9±7.4 years) were included for FEA and TA. Manual segmentation was performed for the vertebral bodies T11, T12, L1, and L2 and the intervertebral discs (IVDs) T11/12, T12/L1, L1/2, and L2/3. Correlation analyses between FEA-derived failure loads and parameters of TA for the single vertebrae and two functional spinal units (FSUs) were calculated, defining FSU-1 as T11-IVD-T12-IVD-L1 and FSU-2 as T12-IVD-L1-IVD-L2. Furthermore, multivariate regressions were performed to identify the texture parameters that predicted the failure load best. For single vertebrae, the strongest correlations were observed for skewnessglobal, kurtosisglobal, and gray level variance (rho =-0.7668 to -0.7362; P<0.001), while for FSUs, SumAverage, long-run emphasis, long-run low gray-level emphasis, homogeneity, and energy showed the strongest correlations (rho =-0.8187 to 0.8407; P<0.05) to failure loads. SumAverage best predicted the failure load for single vertebrae (R2 adj =0.523, P<0.001). For the two FSUs, kurtosisglobal (FSU-1: R2 adj =0.611, P=0.001) and skewnessglobal (FSU-2: R2 adj =0.579, P=0.002) were the best predictors. TA using MDCT data of the spine was significantly associated with FEA-derived failure loads of both, single vertebrae and FSUs. Texture parameters predicted failure loads of FSUs as a more realistic in-vivo scenario equally well as compared to single vertebrae analyses. TA may reflect a less complex and time-consuming approach to accurately and non-invasively evaluate vertebral bone strength.

Carotid plaque vulnerability assessed by Shear Wave elastography and ultrafast Doppler compared to histology: The UF-plaque study

Ultrafast Ultrasound Imaging (UUI) provides an estimation of carotid plaque stiffness by shear wave elastography (SWE) and the quantification of wall shear stress (WSS) by ultrafast Doppler. We aimed to evaluate the combined criteria of plaque stiffness and WSS applied on the plaque as potential biomarkers of plaque vulnerability assessed by histology. We included patients for whom carotid endarterectomy had been decided by a multidisciplinary team. UUI was performed within 48 hours before surgery, and acquisitions were obtained on a carotid longitudinal view. After endarterectomy, gross examination and histological analysis were performed on each removed plaque. Forty-six plaques with SWE data and 29 with WSS data were analysed. Histological analysis revealed 29 vulnerable and 17 stable plaques. Gray Scale Median analysis by B mode, mean and standard deviation of stiffness by SWE did not differ between vulnerable and stable plaques. SWE analysis revealed that the percentage of stiffness range of [3–5] m/s was significantly increased in vulnerable plaques ( P = 0.048). WSS alone showed no difference between stable and vulnerable plaques regardless of the segment of the plaque which was analysed. A multiparametric score using maximal WSS at the peak of the plaque associated with SWE texture analysis parameters was calculated by stepwise regression, leading to a score with a sensitivity of 80% and a specificity of 78%. Area under the Receiver Operating Characteristics curve was 0.85. A multiparameter scoring system including plaque stiffness and flow analysis using UUI allows to effectively identify histologically vulnerable carotid plaques.

Value of MRI texture analysis for predicting new Gleason grade group.

To explore the potential value of multiparametric magnetic resonance imaging (mpMRI) texture analysis (TA) to predict new Gleason Grade Group (GGG). Fifty-eight lesions of fifty patients who underwent mpMRI scanning, including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters were obtained by the postprocessing software, and each lesion was assigned to its corresponding GGG. TA parameters derived from T2WI and DWI were statistically analyzed in detail. Energy, inertia, and correlation derived from apparent diffusion coefficient (ADC) maps and T2WI had a statistically significant difference among the five groups. Kurtosis, energy, inertia, correlation on ADC maps and Energy, inertia on T2WI were moderately related to the GGG trend. ADC-energy and T2-energy were significant independent predictors of the GGG trend. ADC-energy, T2WI-energy, and T2WI-correlation had a statistically significant difference between GGG1 and GGG2-5. ADC-energy were significant independent predictors of the GGG1. ADC-energy, T2WI-energy, and T2WI-correlation showed satisfactory diagnostic efficiency of GGG1 (area under the curve (AUC) 84.6, 74.3, and 83.5%, respectively), and ADC-energy showed excellent sensitivity and specificity (88.9 and 95.1%, respectively). TA parameters ADC-energy and T2-energy played an important role in predicting GGG trend. Both ADC-energy and T2-correlation produced a high diagnostic power of GGG1, and ADC-energy was perfect predictors of GGG1. TA parameters were innovatively used to predict new GGG trend, and the predictive factors of GGG1 were screen out.

Diagnostic accuracy of MRI textural analysis in the classification of breast tumors

ObjectiveTo investigate whether textural analysis (TA) of MRI heterogeneity may play a role in the clinical assessment and classification of breast tumors. Materials and methodsFor this retrospective study, patients with breast masses ≥1 cm on contrast-enhanced MRI were obtained in 69 women (mean age: 51 years; range 21–78 years) with 77 masses (38 benign, 39 malignant) from 2006 to 2018. The selected single slice sagittal peak post-contrast T1-weighted image was analyzed with commercially available TA software [TexRAD Ltd., UK]. Eight histogram TA parameters were evaluated at various spatial scaling factors (SSF) including mean pixel intensity, standard deviation of the pixel histogram (SD), entropy, mean of the positive pixels (MPP), skewness, kurtosis, sigma, and Tx_sigma. Additional statistical tests were used to determine their predictiveness. ResultsEntropy showed a significant difference between benign and malignant tumors at all textural scales (p < 0.0001) and kurtosis was significant at SSF = 0–5 (p = 0.0026–0.0241). The single best predictor was entropy at SSF = 4 with AUC = 0.80, giving a sensitivity of 95% and specificity of 53%. An AUC of 0.91 was found using a model combining entropy with sigma, which yielded better performance with a sensitivity of 92% and specificity of 79%. ConclusionTA of breast masses has the potential to assist radiologists in categorizing tumors as benign or malignant on MRI. Measurements of entropy, kurtosis, and entropy combined with sigma may provide the best predictability.

Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue

PurposeTo prospectively investigate the capabilities of texture analysis (TA) based on apparent diffusion coefficient (ADC) map of the entire tumor volume and the whole volume of peri-tumoral edema, in discriminating between high-grade glioma (HGG) and low-grade glioma (LGG). Materials and methodsA total of 33 patients with histopathological proven glioma were prospectively included. There were 20 men and 13 women with a mean age of 54.5±14.7 (standard deviation [SD]) years (range: 34–75years). TA parameters of whole tumor and peri-tumoral edema were extracted from the ADC map obtained with diffusion-weighted spin-echo echo-planar magnetic resonance imaging at 1.5–T. TA variables of HGG were compared to those of LGG. The optimum cut-off values of TA variables and their corresponding sensitivity, specificity and accuracy for differentiating between LGG and HGG were calculated using receiver operating characteristic curve analysis. ResultsMean and median tumoral ADC of HGG were significantly lower than those of LGG, at 1.23×10−3 mm2/s and 1.21×10−3 mm2/s cut-off values, yielding 70% sensitivity each (95% CI: 59–82% and 61–80%, respectively), 80% (95% CI: 79–98%) and 90% (95% CI: 82–97%) specificity, and 73% (95% CI: 66–91%) and 76% (95% CI: 72–90%) accuracy, respectively. Significant differences in tumoral and peri-tumoral kurtosis were found between HGG and LGG at 1.60 and 0.314 cut-off values yielding sensitivities of 74% (95% CI: 58–83%) and 70% (95% CI: 59–84%), specificities of 90% (95% CI: 80–95%) and 70% (95% CI: 64–83%) and accuracies of 79% (95% CI: 69–89%) and 70% (95% CI: 64–77%), respectively. ConclusionMeasurements of whole tumoral and peri-tumoral TA, based on ADC maps, provide useful information that helps distinguish between HGG and LGG.

Detecting turbulent structures on single Doppler lidar large datasets: an automated classification method for horizontal scans

Abstract. Medium-to-large fluctuations and coherent structures (mlf-cs's) can be observed using horizontal scans from single Doppler lidar or radar systems. Despite the ability to detect the structures visually on the images, this method would be time-consuming on large datasets, thus limiting the possibilities to perform studies of the structures properties over more than a few days. In order to overcome this problem, an automated classification method was developed, based on the observations recorded by a scanning Doppler lidar (Leosphere WLS100) installed atop a 75 m tower in Paris's city centre (France) during a 2-month campaign (September–October 2014). The mlf-cs's of the radial wind speed are estimated using the velocity–azimuth display method over 4577 quasi-horizontal scans. Three structure types were identified by visual examination of the wind fields: unaligned thermals, rolls and streaks. A learning ensemble of 150 mlf-cs patterns was classified manually relying on in situ and satellite data. The differences between the three types of structures were highlighted by enhancing the contrast of the images and computing four texture parameters (correlation, contrast, homogeneity and energy) that were provided to the supervised machine-learning algorithm, namely the quadratic discriminant analysis. The algorithm was able to classify successfully about 91 % of the cases based solely on the texture analysis parameters. The algorithm performed best for the streak structures with a classification error equivalent to 3.3 %. The trained algorithm applied to the whole scan ensemble detected structures on 54 % of the scans, among which 34 % were coherent structures (rolls and streaks).

CT texture analysis (CTTA) parameters as imaging biomarkers for predicting histopathological and molecular features of carcinoma

CT texture analysis (CTTA) parameters as imaging biomarkers for predicting histopathological and molecular features of carcinoma

Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success?

To explore whether multiple 3D computed tomography texture analysis (3D-CTTA) parameters can predict the therapeutic effects of holmium: YAG laser lithotripsy (LL) on ureteral calculi. The files from 94 patients (102 stones) with proximal ureteral calculi treated only by LL at a single institution were retrospectively retrieved from January 2016 to March 2019. According to intra-operative observations and postoperative reexamination, samples were divided into a completely crushed and a non-crushed group. Preoperative non-contrast-enhanced computed tomography (NCCT) images obtained by multiple CT scanners were imported to MaZda software for 3D texture analysis (TA). The CT-derived value of each target stone was measured, and 15 TA parameters were extracted by delineating volumes of interest (VOIs). Receiver operating characteristic (ROC) curves were drawn to determine the optimal critical value of each parameter based on the Youden index, and univariable and multivariable logistic regression analyses determined the significant factors for LL success. In univariable analysis, significant differences (p < 0.05) were observed among 7 parameters. In multivariable analysis, Perc.01 3D > 2062 (p = 0.03) and Z-fraction of image in runs (Z-Fraction) > 0.45570 (p = 0.009) were significant independent predictors, with odds ratios (ORs) of 24.204 and 60.329, respectively. In subgroup analysis based on the cutoff value of the CT-derived value (HU = 960), Perc.01 3D (OR = 44.154, 95% CI (2.379, 819.618), p = 0.011) and Z-Fraction (OR = 14.519, 95% CI (2.088, 100.953), p = 0.007) remained statistically significant. The combination of 3D-CTTA parameters and the CT-derived value can be used as a quantitative reference to predict whether a target stone could be completely crushed by LL. • Computed tomography texture analysis (CTTA) may be helpful in selecting suitable laser lithotripsy (LL) patients. • 3D-CTTA better predicts stone fragility than commonly used methods (such as the CT-derived value). • The combination of CTTA and the CT-derived value can be used as a preoperative quantitative reference.

Variation in textural parameters of hepatic lesions during contrast medium injection.

Textural parameters extracted using quantitative imaging techniques have been shown to have prognostic value for hepatocellular carcinoma (HCC). To evaluate whether the contrast medium timing of the image acquisition affects the reproducibility of textural parameters in HCC and hepatic tissue. This retrospective study included 17 patients with 37 HCC lesions. Perfusion computed tomography (CT) was obtained after 50 mL contrast medium injection. HCC lesions were segmented for analysis. The gray-level co-occurrence (GLCM) textural analysis parameters, homogeneity, energy, entropy, inertia, and correlation were calculated. Variation was quantified by calculating the SD of each parameter during respective perfusion series and the inter lesion variation as the SD among the lesions. The average change in texture parameters in both HCC and hepatic tissue per second after injection was 0.01% to 0.3% of the respective texture parameter. In HCC, the average variation in homogeneity, energy, and entropy within each lesion after contrast medium injection was significantly less than the variation observed among the lesions (23% to 74%, P < 0.001). Significant differences in energy, entropy, inertia, and correlation between hepatic tissue and HCC were observed. However, when considering the intra-individual variation of hepatic tissue over time, only the HCC parameter energy was significantly outside that 95% confidence interval (P < 0.02). The contrast medium timing does not affect the reproducibility of textural parameters in HCC and hepatic tissue. Thus, contrast medium timing should not be an issue at CT texture analysis of HCC.

Value of MRI texture analysis for predicting high-grade prostate cancer

PurposeTo explore the potential value of MRI texture analysis (TA) combined with prostate-related biomarkers to predict high-grade prostate cancer (HGPCa). Materials and methodsEighty-five patients who underwent MRI scanning, including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters derived from T2WI and DWI, prostate-specific antigen (PSA), and free PSA (fPSA) were compared between the HGPCa and non-high-grade prostate cancer (NHGPCa) groups using independent Student's t-test and the Mann-Whitney U test. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the predictive value for HGPCa. ResultsUnivariate analysis showed that PSA and entropy based on apparent diffusion coefficient (ADC) map differed significantly between the HGPCa and NHGPCa groups and showed higher diagnostic values for HGPCa (area under the curve (AUC) = 82.0% and 80.0%, respectively). Logistic regression and ROC curve analyses revealed that kurtosis, skewness and entropy derived from ADC maps had diagnostic power to predict HGPCa; when the three texture parameters were combined, the area under the ROC curve reached the maximum (AUC = 84.6%; 95% confidence interval (CI): 0.758, 0.935; P = 0.000). ConclusionTA parameters derived from ADC may be a valuable tool in predicting HGPCa. The combination of specific textural parameters extracted from ADC map may be additional tools to predict HGPCa.

CT texture analysis compared to Positron Emission Tomography (PET) and mutational status in resected melanoma metastases

PurposeTo evaluate the potential of CT texture analysis parameters and metabolic characteristics of melanoma metastases in 18F- FDG PET/CT to predict relevant mutations of tumour cells for targeted therapy in metastatic melanoma patients in correlation with histopathologic specimen. Material and methods66 melanoma patients, examined with contrast-enhanced 18F-FDG PET/CT before scheduled metastasectomy without any prior systemic therapy, were included in this single-centre retrospective analysis under IRB waiver. The largest, resected metastasis in each patient was assessed with CT texture analysis and semiquantitative 18F-FDG PET parameters. Correlation between imaging parameters and histopathological mutations (BRAF- and NRAS- genes) were calculated. ResultsAttenuation standard deviation (SD) within target lesion indicated a weak correlation with its SUVpeak (rho −0.292, p 0.017). However, no correlation between CT texture analysis, metabolic 18F-FDG PET parameters and tumour cell mutation could be established. ConclusionCT texture parameters cannot replace the diagnostic value of 18F- FDG PET/CT for metabolic information in melanoma patients. Discrimination between BRAF- and NRAS mutation status was not feasible with CT texture analysis in this exploratory study.

Association between Texture Analysis Parameters and Molecular Biologic KRAS Mutation in Non-Mucinous Rectal Cancer.

PurposeTo evaluate the association between magnetic resonance imaging (MRI)-based texture parameters and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in patients with non-mucinous rectal cancer.Materials and MethodsSeventy-nine patients who had pathologically confirmed rectal non-mucinous adenocarcinoma with or without KRAS-mutation and had undergone rectal MRI were divided into a training (n = 46) and validation dataset (n = 33). A texture analysis was performed on the axial T2-weighted images. The association was statistically analyzed using the Mann-Whitney U test. To extract an optimal cut-off value for the prediction of KRAS mutation, a receiver operating characteristic curve analysis was performed. The cut-off value was verified using the validation dataset.ResultsIn the training dataset, skewness in the mutant group (n = 22) was significantly higher than in the wild-type group (n = 24) (0.221 ± 0.283; -0.006 ± 0.178, respectively, p = 0.003). The area under the curve of the skewness was 0.757 (95% confidence interval, 0.606 to 0.872) with a maximum accuracy of 71%, a sensitivity of 64%, and a specificity of 78%. None of the other texture parameters were associated with KRAS mutation (p > 0.05). When a cut-off value of 0.078 was applied to the validation dataset, this had an accuracy of 76%, a sensitivity of 86%, and a specificity of 68%.ConclusionSkewness was associated with KRAS mutation in patients with non-mucinous rectal cancer.

Utility of texture analysis on T2-weighted MR for differentiating tumor deposits from mesorectal nodes in rectal cancer patients, in a retrospective cohort.

The purpose of the study was to evaluate the utility of MR texture analysis for differentiating tumor deposits from mesorectal nodes in rectal cancer. Pretreatment MRI of 40 patients performed between 2006 and 2018 with pathologically proven tumor deposits and/or malignant nodes in the setting of rectal cancer were retrospectively reviewed. In total, 25 tumor deposits (TDs) and 71 positive lymph nodes (LNs) were analyzed for morphological and first-order texture analysis features on T2-weighted axial images. MR morphological features (lesion shape, size, signal heterogeneity, contrast enhancement) were analyzed and agreed in consensus by two experienced radiologists followed by assessmentwith Fisher's exact test. Texture analysis of the lesions was performed using TexRAD, a proprietary software algorithm. First-order texture analysis features (mean, standard deviation, skewness, entropy, kurtosis, MPP) were obtained after applying spatial scaling filters (SSF; 0, 2, 3, 4, 5, 6). Univariate analysis was performed with non-parametric Mann-Whitney U test. The results of univariate analysis were reassessed with generalized estimating equations followed by multivariate analysis. Using histopathology as a gold standard, diagnostic accuracy was assessed by obtaining area under the receiver operating curve. MR morphological parameter, lesion shape was a strong discriminator between TDs and LNs with a p value of 0.02 (AUC: 0.76, 95% CI of 0.66 to 0.84, SE: 0.06) and sensitivity, specificity of 90% and 68%, respectively. Skewness extracted at fine filter (SSF-2) was the only significant texture analysis parameter for distinguishing TDs from LNs with p value of 0.03 (AUC: 0.70, 95% CI of 0.59 to 0.79, SE: 0.06) and sensitivity, specificity of 70% and 72%, respectively. When lesion shape and skewness-2 were combined into a single model, the diagnostic accuracy was improved with AUC of 0.82 (SE: 0.05, 95% CI of 0.72 to 0.88 with p value of < 0.01). This model also showed a high sensitivity of 91% with specificity of 68%. Lesion shape on MR can be a useful predictor for distinguishing TDs from positive LNs in rectal cancer patients. When interpreted along with MR texture parameter of skewness, accuracy is further improved.

MRI-based texture analysis to differentiate the most common parotid tumours

To evaluate magnetic resonance imaging (MRI) features and signal characteristics of parotid masses and investigate the added role of texture analysis (TA) in the differentiation of parotid tumours. Ninety-five patients (42 women, 53 men; mean age 51.67±14.15) were included in this study. The study group consisted of 40 pleomorphic adenoma, 45 Warthin's tumour, and 10 mucoepidermoid carcinomas. Two reviewers assessed the MRI sequences retrospectively. Fat-suppressed T2-weighted and contrast-enhanced T1-weighted axial images were used for TA. Receiver operating characteristic curve analyses were performed to evaluate the ability to make a diagnosis. Logistic regression analyses were conducted to explore the independent risk factors among the MRI features and to analyse the added value of TA to the qualitative analysis. Significant differences were found in the tumour border (p<0.001), infiltration of the surrounding tissue (p=0.003), contrast-enhancement grading (p<0.001), perineural spread (p=0.013), and pathological lymph nodes (p<0.001) between the malignant and benign tumours. Kurtosis on contrast-enhanced T1-weighted images, and skewness and kurtosis on T2-weighted images were significantly different between the three groups (p=0.020, <0.001, 0.003; respectively). A kurtosis value on T2-weighted images <2.815 along with an ill-defined border had the highest specificity (98.8%) and positive predictive value (83.3%) in the differentiation of malignant tumours. The addition of TA parameters to the MRI findings may contribute to distinguish benign from malignant parotid tumours.

Estimation of Renal Function Using Unenhanced Computed Tomography in Upper Urinary Tract Stones Patients.

Objectives: The aim of this study was to determine whether unenhanced computed tomography (CT) imaging can estimate differential renal function (DRF) in patients with chronic unilateral obstructive upper urinary tract stones.Materials and Methods: This was a single-center retrospective study of 76 patients. All the patients underwent unenhanced CT and nuclear renography (RG) at an interval of 4 to 6 weeks due to chronic unilateral obstructive urinary stones. Renal CT measurements (RCMs), including residual parenchymal volume (RPV) and volumetric CT texture analysis parameters, were obtained through a semiautomatic method. Percent RCMs were calculated and compared with renal function determined by RG.Results: The strongest Pearson coefficient between percent RCM and DRF was reflected by RPV (r = 0.957, P < 0.001). Combinations of RPV and other parameters did not significantly improve the correlation compared with RPV alone (r = 0.957 vs. r = 0.957, 0.957, 0.887, 0.815, and 0.956 for combination with Hounsfield unit, parenchymal voxel, skewness, kurtosis, and entropy, respectively; all P < 0.001). Percent RPV was subsequently introduced into linear regression, and the equation y = −2.66 + 1.07* × (P < 0.001) was derived to calculate predicted DRF. No statistically difference was found between predicted DRF using the equation and observed DRF according to RG (P = 0.959).Conclusion: Unenhanced CT imaging can estimate DRF in patients with chronic unilateral obstructive upper urinary tract stones, and RG might not be necessary as a conventional method in clinical.

Pilot study to differentiate lipoma from atypical lipomatous tumour/well-differentiated liposarcoma using MR radiomics-based texture analysis.

This pilot study aims to determine if tumour heterogeneity assessed using magnetic resonance imaging (MRI) radiomics-based texture analysis (TA) can differentiate between lipoma and atypical lipomatous tumour (ALT)/well-differentiated liposarcoma (WDL). Thirty consecutive ALT/WDLs and 30 lipomas were included in the study, cases diagnosed both histologically and with murine double minute 2 (MDM2) gene amplification by fluorescence in situ hybridisation (FISH) in excision specimens. Multiple patient, MRI and MRTA factors were assessed. Heterogeneity was evaluated using a filtration-histogram technique-based textural analysis on single axial proton density (PD) and coronal T1-W images of the most homogenously fatty component of the lesion. Thirty-three percent of the diagnoses of ALT/WDL vs lipoma were confirmed using FISH MDM2 analysis. ALT/WDLs were statistically different from lipomas in location (site in the body and depth from skin surface) and fat content, with p values of 0.021, 0.001, and 0.021 respectively. Nine of 36 (25%) texture parameters had significant differences between ALT/WDLs and lipomas on axial PD MRTA, with the most significant results at medium and coarse texture scales particularly mean intensity (p= 0.003) at SSF = 6, and kurtosis (p= 0.012) at SSF = 5. A cut-off value of < 304 for coarse-filtered texture on axial PD MRI identified ALT from lipoma with a sensitivity and specificity of 70% (AUC = 0.73, p= 0.003). Texture heterogeneity quantified at fine, medium, and coarse texture scales are significant differentiators of lipoma and ALT/WDL with the difference particularly marked in medium and coarse texture scales for two MR TA parameters: mean and kurtosis.

MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer.

This study aimed to evaluate the efficiency of imaging features and texture analysis (TA) based on baseline rectal MRI for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) and tumor recurrence in patients with locally advanced rectal cancer (LARC). Consecutive patients with LARC who underwent rectal MRI between January 2014 and December 2015 and surgical resection after completing nCRT were retrospectively enrolled. Imaging features were analyzed, and TA parameters were extracted from the tumor volume of interest (VOI) from baseline rectal MRI. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the optimal TA parameter cutoff values to stratify the patients. Logistic and Cox regression analyses were performed to assess the efficacy of each imaging feature and texture parameter in predicting tumor response and disease-free survival. In total, 78 consecutive patients were enrolled. In the logistic regression, good treatment response was associated with lower tumor location (OR = 13.284, p = 0.012), low Conv_Min (OR = 0.300, p = 0.013) and high Conv_Std (OR = 3.174, p = 0.016), Shape_Sphericity (OR = 3.170, p = 0.015), and Shape_Compacity (OR = 2.779, p = 0.032). In the Cox regression, a greater risk of tumor recurrence was related to higher cT stage (HR = 5.374, p = 0.044), pelvic side wall lymph node positivity (HR = 2.721, p = 0.013), and gray-level run length matrix_long-run low gray-level emphasis (HR = 2.268, p = 0.046). Imaging features and TA based on baseline rectal MRI could be valuable for predicting the treatment response to nCRT for rectal cancer and tumor recurrence. • Imaging features and texture parameters of T2-weighted MR images of rectal cancer can help to predict treatment response and the risk for tumor recurrence. • Tumor location as well as conventional and shape indices of texture features can help to predict treatment response for rectal cancer. • Clinical T stage, positive pelvic side wall lymph nodes, and the high-order texture parameter, GLRLM_LRLGE, can help to predict tumor recurrence for rectal cancer.