CT Texture Analysis Parameters Research Articles

Differentiation of giant cell tumours of bone, primary aneurysmal bone cysts, and aneurysmal bone cysts secondary to giant cell tumour of bone: using whole-tumour CT texture analysis parameters as quantitative biomarkers

To determine whether computed tomography (CT) texture analysis parameters can be used as quantitative biomarkers to help differentiate giant cell tumour of bones (GCTs), primary aneurysmal bone cysts (PABCs), and aneurysmal bone cysts (ABCs) secondary to giant cell tumours of bone (GABCs). One hundred and seven patients with 63 GCTs, 31 PABCs, and 13 GABCs were analysed retrospectively. All patients underwent preoperative CT. Two radiologists independently evaluated the qualitative features of the CT images and extracted texture parameters. Patient demographics, qualitative features, and texture parameters among GCTs, PABCs, and GABCs were compared statistically. Differences in these parameters between ABCs and GCTs were also assessed. ROC curves were obtained to determine optimal parameter values. The best preoperative CT parameters to differentiate GCTs, PABCs, and GABCs included one qualitative feature (location around the knee) and four texture parameters (95th percentile, maximum intensity, skewness, and kurtosis). Age and three texture parameters (5th percentile, inhomogeneity, and kurtosis) enabled statistically significant differentiation between GCTs and ABCs. Combination of the above four parameters generated the largest area under the ROC curve (AUC) for the differentiation of GCTs and ABCs. CT texture analysis parameters can be used as quantitative biomarkers for preoperative differentiation among GCTs, PABCs, and GABCs.

Can CT texture analysis parameters be used as imaging biomarkers for prediction of malignancy in canine splenic tumors?

Splenic hemangiosarcoma has morphological similarities to benign nodular hyperplasia. Computed tomography (CT) texture analysis can analyze the texture of images that the naive human eye cannot detect. Recently, there have been attempts to incorporate CT texture analysis with artificial intelligence in human medicine. This retrospective, analytical design study aimed to assess the feasibility of CT texture analysis in splenic masses and investigate predictive biomarkers of splenic hemangiosarcoma in dogs. Parameters for dogs with hemangiosarcoma and nodular hyperplasia were compared, and an independent parameter that could differentiate between them was selected. Discriminant analysis was performed to assess the ability to discriminate the two splenic masses and compare the relative importance of the parameters. A total of 23 dogs were sampled, including 16 splenic nodular hyperplasia and seven hemangiosarcoma. In each dog, total 38 radiomic parameters were extracted from first-, second-, and higher-order matrices. Thirteen parameters had significant differences between hemangiosarcoma and nodular hyperplasia. Skewness in the first-order matrix and GLRLM_LGRE and GLZLM_ZLNU in the second, higher-order matrix were determined as independent parameters. A discriminant equation consisting of skewness, GLZLM_LGZE, and GLZLM_ZLNU was derived, and the cross-validation verification result showed an accuracy of 95.7%. Skewness was the most influential parameter for the discrimination of the two masses. The study results supported using CT texture analysis to help differentiate hemangiosarcoma from nodular hyperplasia in dogs. This new diagnostic approach can be used for developing future machine learning-based texture analysis tools.

Predicting endocrine function after total pancreatectomy and islet cell autotransplantation: A novel approach utilizing computed tomography texture analysis

BackgroundIslet cell autotransplantation is an effective method to prevent morbidity associated with type IIIc diabetes after total pancreatectomy. However, there is no valid method to predict long-term endocrine function. Our aim was to assess computed tomography texture analysis as a strategy to predict long-term endocrine function after total pancreatectomy and islet cell autotransplantation. MethodsAll patients undergoing total pancreatectomy and islet cell autotransplantation from 2007 to 2020 who had high-quality preoperative computed tomography imaging available for texture analysis were included. The primary outcome was optimal long-term endocrine function, defined as stable glycemic control with <10 units of insulin/day. ResultsSixty-three patients met inclusion criteria. Median yield was 6,111 islet equivalent/kg body weight. At a median follow-up of 64.2 months, 12.7% (n = 8) of patients were insulin independent and 39.7% (n = 25) demonstrated optimal endocrine function. Neither total islet equivalent nor islet equivalent/kg body weight alone were associated with optimal endocrine function. To improve endocrine function prediction, computed tomography texture analysis parameters were analyzed, identifying an association between kurtosis (odds ratio, 2.32; 95% confidence interval, 1.08–4.80; P = .02) and optimal endocrine function. Sensitivity analysis discovered a cutoff for kurtosis = 0.60, with optimal endocrine function seen in 66.7% with kurtosis ≥0.60, compared with only 26.2% with kurtosis <0.60 (P < .01). On multivariate logistic regression including islet equivalent yield, only kurtosis ≥0.60 (odds ratio, 5.61; 95% confidence interval, 1.56–20.19; P = .01) and fewer small islet equivalent (odds ratio, 1.00; 95% confidence interval, 1.00–1.00; P = .02) were associated with optimal endocrine function, with the whole model demonstrating excellent prediction of long-term endocrine function (area under the curve, 0.775). ConclusionComputed tomography texture analysis can provide qualitative data, that when used in combination with quantitative islet equivalent yield, can accurately predict long-term endocrine function after total pancreatectomy and islet cell autotransplantation.

Volumetric CT Texture Analysis of Intrahepatic Mass-Forming Cholangiocarcinoma for the Prediction of Postoperative Outcomes: Fully Automatic Tumor Segmentation Versus Semi-Automatic Segmentation.

ObjectiveTo determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection.Materials and MethodsThis retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38–78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters.ResultsThe time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semi-automatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601–0.997 and 0.177–0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation.ConclusionVolumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.

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.

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.

Correlation of CT imaging features and CT texture analysis parameters with pathologic risk stratification in gastric stromal tumors

To analyze the correlation of the CT imaging features and CT texture analysis (CTTA) parameters with risk stratifications of the gastric stromal tumors (GSTs). Methods: CT (plain scans with portal phase contrast enhanced scans) images from 98 GSTs patients before surgery were collected. CT features of the tumors were retrospectively analyzed and pathological risk stratifications were determined. Feature parameters of CTTA at plain and portal phase were obtained by using a MaZda software. The correlations of CT imaging features and CTTA parameters with the risk stratifications were analyzed. Results: CT imaging features including tumor size, growth pattern, shape, margin, the relationship between mass and adjacent organs, enhancement pattern, necrosis or cystic change, and the abnormal tumor vessels were associated with the risk stratifications (P<0.05). For CTTA, entropy was associated with the risk stratifications (P<0.05). Conclusion: Certain CT imaging features and CTTA parameters are associated with risk classifications in GSTs.

Radiogenomics in Clear Cell Renal Cell Carcinoma: Correlations Between Advanced CT Imaging (Texture Analysis) and MicroRNAs Expression.

Purpose:A relevant challenge for the improvement of clear cell renal cell carcinoma management could derive from the identification of novel molecular biomarkers that could greatly improve the diagnosis, prognosis, and treatment choice of these neoplasms. In this study, we investigate whether quantitative parameters obtained from computed tomography texture analysis may correlate with the expression of selected oncogenic microRNAs.Methods:In a retrospective single-center study, multiphasic computed tomography examination (with arterial, portal, and urographic phases) was performed on 20 patients with clear cell renal cell carcinoma and computed tomography texture analysis parameters such as entropy, kurtosis, skewness, mean, and standard deviation of pixel distribution were measured using multiple filter settings. These quantitative data were correlated with the expression of selected microRNAs (miR-21-5p, miR-210-3p, miR-185-5p, miR-221-3p, miR-145-5p). Both the evaluations (microRNAs and computed tomography texture analysis) were performed on matched tumor and normal corticomedullar tissues of the same patients cohort.Results:In this pilot study, we evidenced that computed tomography texture analysis has robust parameters (eg, entropy, mean, standard deviation) to distinguish normal from pathological tissues. Moreover, a higher coefficient of determination between entropy and miR-21-5p expression was evidenced in tumor versus normal tissue. Interestingly, entropy and miR-21-5p show promising correlation in clear cell renal cell carcinoma opening to a radiogenomic strategy to improve clear cell renal cell carcinoma management.Conclusion:In this pilot study, a promising correlation between microRNAs and computed tomography texture analysis has been found in clear cell renal cell carcinoma. A clear cell renal cell carcinoma can benefit from noninvasive evaluation of texture parameters in adjunction to biopsy results. In particular, a promising correlation between entropy and miR-21-5p was found.

Applying CT texture analysis to determine the prognostic value of subsolid nodules detected during low-dose CT screening

To analyse subsolid nodules (SSNs) detected during low-dose (LD) computed tomography (CT) screening and investigated whether CT texture analysis parameters can predict the malignancy and growth trends of GGNs. In this retrospective study, 89 SSNs were detected in 86 LDCT screening participants, including 42 pure ground-glass nodules (GGNs) and 47 part-solid GGNs. In these participants, 28 SSNs were diagnosed as lung cancer at histopathology, and 61 SSNs from participants who underwent at least two LDCT imaging studies. All nodules were divided into three groups: cancer group, growth group, and non-growth group. The nodule size, volume, attenuation, volume doubling time (VDT), and texture parameters (mean value, uniformity, entropy and energy) were assessed, respectively. The entropy of the cancer group was significantly higher than that of the growth and non-growth groups (pure GGNs: p=0.009, 0.001; part-solid GGNs: p=0.012, 0.004). The energy of the cancer group was significantly lower than that of the other groups (pure GGNs: p=0.043, 0.021; part-solid GGNs: p=0.001, 0.002). A good positive correlation was found between uniformity and VDT (p=0.022). Different CT texture parameters show good predictive value for SSNs detected at LDCT screening: the entropy and energy differences between malignant pulmonary nodules and others could be a helpful quantitative index to predict the malignancy of SSNs. Uniformity could be used to predict the growth probability of pure GGNs at baseline to pay more attention to these nodules. Moreover, the follow-up and treatment plan could be more targeted.

Evaluation of texture analysis for the differential diagnosis of focal nodular hyperplasia from hepatocellular adenoma on contrast-enhanced CT images.

To explore the value of CT texture analysis (CTTA) for differentiation of focal nodular hyperplasia (FNH) from hepatocellular adenoma (HCA) on contrast-enhanced CT (CECT). This is a retrospective, IRB-approved study conducted in a single institution. A search of the medical records between 2008 and 2017 revealed 48 patients with 70 HCA and 50 patients with 62 FNH. All lesions were histologically proven and with available pre-operative CECT imaging. Hepatic arterial phase (HAP) and portal venous phase (PVP) were used for CTTA. Textural features were extracted using a commercially available research software (TexRAD). The differences between textural parameters of FNH and HCA were assessed using the Mann-Whitney U test and the AUROC were calculated. CTTA parameters showing significant difference in rank sum test were used for binary logistic regression analysis. A p value < 0.05 was considered statistically significant. On HAP images, mean, mpp, and skewness were significantly higher in FNH than in HCA on unfiltered images (p ≤ 0.007); SD, entropy, and mpp on filtered analysis (p ≤ 0.006). On PVP, mean, mpp, and skewness in FNH were significantly different from HCA (p ≤ 0.001) on unfiltered images, while entropy and kurtosis were significantly higher in FNH on filtered images (p ≤ 0.018). The multivariate logistic regression analysis indicated that the mean, mpp, and entropy of medium-level and coarse-level filtered images on HAP were independent predictors for the diagnosis of HCA and a model based on all these parameters showed the largest AUROC (0.824). Multiple explored CTTA parameters are significantly different between FNH and HCA on CECT.

CT texture analysis of pancreatic cancer.

We investigated the value of CT texture analysis (CTTA) in predicting prognosis of unresectable pancreatic cancer. Sixty patients with unresectable pancreatic cancers at presentation were enrolled for post-processing with CTTA using commercially available software (TexRAD Ltd, Cambridge, UK). The largest cross-section of the tumour on axial CT was chosen to draw a region-of-interest. CTTA parameters (mean value of positive pixels (MPP), kurtosis, entropy, skewness), arterial and venous invasion, metastatic disease and tumour size were correlated with overall and progression-free survivals. The median overall and progression-free survivals of cohort were 13.3 and 7.8 months, respectively. On multivariate Cox proportional hazard regression analysis, presence of metastatic disease at presentation had the highest association with overall survival (p = 0.003-0.05) and progression-free survival (p < 0.001 to p = 0.004). MPP at medium spatial filter was significantly associated with poor overall survival (p = 0.04). On Kaplan-Meier survival analysis of CTTA parameters at medium spatial filter, MPP of more than 31.625 and kurtosis of more than 0.565 had significantly worse overall survival (p = 0.036 and 0.028, respectively). CTTA features were significantly associated with overall survival in pancreas cancer, particularly in patients with non-metastatic, locally advanced disease. • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis can determine prognosis in patients with unresectable pancreas cancer. • The best predictors of poor prognosis were high kurtosis and MPP.

CT texture analysis of histologically proven benign and malignant lung lesions.

The purpose of our study was to determine accuracy of CT texture analysis (CTTA) for differentiating benign from malignant pulmonary nodules, and well-differentiated from poorly differentiated lung cancers, with histology as the standard of reference.In this IRB-approved study, 175 adult patients (average age 66 ± 12 years; age range 27–89 years, male 82: female 93) who underwent a noncontrast chest CT examination prior to CT-guided biopsy of pulmonary nodules were included. There were 57 benign (24 tumors or tumor-like lesions; 33 inflammatory conditions) and 120 malignant (29 well-differentiated adenocarcinomas, 48 poorly differentiated adenocarcinomas, and 43 squamous cell carcinomas) diagnoses on pathology. CTTA was performed on the prebiopsy noncontrast CT images using a commercially available software (TexRAD limited, UK). The CTCA features analyzed included mean HU values, percent positive pixels (PPP), mean value of positive pixels (MPP), standard deviation (SD), normalized SD, skewness, kurtosis, and entropy.The ROC analyses showed that normalized SD [AUC: 0.63, (CI: 0.55–72), P = .003] had moderate accuracy for differentiating between benign and malignant lesions. For differentiating among well-differentiated and poorly differentiated tumors, the ROC analysis showed that except skewness all other parameters were statistically significant The AUC values of other CTTA parameters were: mean (AUC: 0.73–0.76, P = .001– < .0001).CT texture analyses can reliably predict well- and poorly differentiated lung malignancies. However, inflammatory lung lesions with tissue heterogeneity negatively affect the performance of CTTA when it comes to differentiation between benign and malignant pulmonary nodules.

Value of computed tomography texture analysis for prediction of perioperative complications during laparoscopic partial nephrectomy in patients with renal cell carcinoma.

PurposeTumorous texture is a marker for tumor tissue inhomogeneity. Based on this assumption, this study aims to evaluate the value of computed tomography texture analysis for imaging-based prediction of perioperative complications during laparoscopic partial tumor nephrectomy.MethodsA total of 106 patients with histologically confirmed renal cell carcinoma and pre-operative CT were included and volumetric texture analysis of the tumors was performed by two readers. Texture analysis parameter ratios and differences were calculated using the kidney parenchyma as reference (“reference-corrected”). Regression analysis was performed, regarding the value of the texture analysis parameters, for assessment of the tumor nuclear grade and the prediction of peri- and postoperative complications and approximated blood loss. Moreover, the inter-rater agreement in terms of the intra-class correlation coefficient (ICC) was calculated.ResultsRegarding the reference-corrected values, the predictive value of texture analysis parameters for severe perioperative complications was highest for the standard deviation of the mean attenuation (Area under curve/AUC, .615; sensitivity, 93.8%, specificity, 30.0%), followed by the uniformity (AUC, .599; sensitivity, 62.5%, specificity, 60.0%), and the uniformity of distribution of positive pixels (AUC, .597; sensitivity, 62.5%; specificity, 61.1%).Regarding the blood loss, the uniformity of positive pixel values (UPP; AUC, 0.638), uniformity (AUC, 0.635), and entropy (AUC, 0.633) yielded the best predictive values, whilst the tumor grade was a weaker predictor (AUC, 0.574).The applied texture analysis parameters did not correlate with the time of surgery or the warm ischemic time. All measured parameters were better predictors for complications than the tumor diameter alone. The inter-rater agreement was almost perfect (ICC, .982).ConclusionCT and CT texture analysis parameters are valuable for prediction of perioperative outcome before laparoscopic partial nephrectomy in patients with renal cell carcinoma.

Virtual monoenergetic imaging in rapid kVp-switching dual-energy CT (DECT) of the abdomen: impact on CT texture analysis.

To study the impact of keV levels of virtual monoenergetic images generated from rapid kVp-switching dual-energy CT (rsDECT) on CT texture analysis (CTTA). This study included 30 consecutive patients (59.3±12years; range 34-77years; 17M:13F) who underwent portal venous phase abdominal CT on a rsDECT scanner. Axial 5-mm monoenergetic images at 5 energy levels (40/50/60/70/80keV) were created and CTTA of liver was performed. CTTA comprised a filtration-histogram technique with different spatial scale filter (SSF) values (0-6). CTTA quantification at each SSF value included histogram-based statistical parameters such as mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. The values were compared using repeated measures ANOVA. Among the different CTTA metrics, mean intensity (at SSF>0), skewness, and kurtosis did not show variability whereas entropy, MPP, and SD varied with different keV levels. There was no change in skewness and kurtosis values for all 6 filters (p>0.05). Mean intensity showed no change for filters 2-6 (p>0.05). Mean intensity at SSF=0 i.e., mean attenuations were 91.2±2.9, 108.7±3.6, 136.1±4.7, 179.8±6.9, and 250.5±10.1 HU for 80, 70, 60, 50, and 40keV images, respectively demonstrating significant variability (decrease) with increasing keV levels (p<0.001). Entropy, MPP, and SD values showed a statistically significant decrease with increasing keV of monoenergetic images on all 6 filters (p<0.001). The energy levels of monoenergetic images have variable impact on the different CTTA parameters, with no significant change in skewness, kurtosis, and filtered mean intensity whereas significant decrease in mean attenuation, entropy, MPP, and SD values with increasing energy levels.

CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy.

The accurate prediction of prognosis and failure is crucial for optimizing treatment strategies for patients with cancer. The purpose of this study was to assess the performance of pretreatment CT texture analysis for the prediction of treatment failure in primary head and neck squamous cell carcinoma treated with chemoradiotherapy. This retrospective study included 62 patients diagnosed with primary head and neck squamous cell carcinoma who underwent contrast-enhanced CT examinations for staging, followed by chemoradiotherapy. CT texture features of the whole primary tumor were measured using an in-house developed Matlab-based texture analysis program. Histogram, gray-level co-occurrence matrix, gray-level run-length, gray-level gradient matrix, and Laws features were used for texture feature extraction. Receiver operating characteristic analysis was used to identify the optimal threshold of any significant texture parameter. We used multivariate Cox proportional hazards models to examine the association between the CT texture parameter and local failure, adjusting for age, sex, smoking, primary tumor stage, primary tumor volume, and human papillomavirus status. Twenty-two patients (35.5%) developed local failure, and the remaining 40 (64.5%) showed local control. Multivariate analysis revealed that 3 histogram features (geometric mean [hazard ratio = 4.68, P = .026], harmonic mean [hazard ratio = 8.61, P = .004], and fourth moment [hazard ratio = 4.56, P = .048]) and 4 gray-level run-length features (short-run emphasis [hazard ratio = 3.75, P = .044], gray-level nonuniformity [hazard ratio = 5.72, P = .004], run-length nonuniformity [hazard ratio = 4.15, P = .043], and short-run low gray-level emphasis [hazard ratio = 5.94, P = .035]) were significant predictors of outcome after adjusting for clinical variables. Independent primary tumor CT texture analysis parameters are associated with local failure in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.

Differences in Texture Analysis Parameters Between Active Alveolitis and Lung Fibrosis in Chest CT of Patients with Systemic Sclerosis: A Feasibility Study

This study aimed to determine the diagnostic aid of computed tomography (CT) features for the differentiation of active alveolitis and fibrosis using a CT texture analysis (CTTA) prototype and CT densitometry in patients with systemic sclerosis (SSc) using ancillary high-resolution computed tomography (HRCT) features and their longitudinal course as standard of reference. We retrospectively analyzed thin-slice noncontrast chest CT image data of 43 patients with SSc (18 men, mean age 51.55 ± 15.52 years; range 23-71 years). All of them had repeated noncontrast enhanced HRCT of the lung. Classification into active alveolitis or fibrosis was done on HRCT based on classical HRCT findings (active alveolitis [19; 44.2%] and fibrosis [24; 55.8%]) and their course at midterm. Results were compared to pulmonary functional tests and were followed up by CT. Ground glass opacity was considered suggestive of alveolitis, whereas coarse reticulation with parenchymal distortion, traction bronchiectasis, and honeycombing were assigned to fibrosis. Statistically significant differences in CTTA were found for first-order textural features (mean intensity, average, deviation, skewness) and second-order statistics (entropy of co-occurrence matrix, mean number of nonuniformity (NGLDM), entropy of NGLDM, entropy of heterogeneity, intensity, and average). Cut-off value for the prediction of fibrosis at baseline was significant for entropy of intensity (P value < .001) and for mean deviation (P value < .001), and for prediction of alveolitis was significant for uniformity of intensity (P value < .001) and for NGLDM (P value < .001). At pulmonary functional tests, forced expiratory volume in 1 second and single-breath diffusion capacity for carbon monoxide were significantly lower in fibrosis than in alveolitis 2.03 ± 0.78 vs. 2.61 ± 0.83, P < .016 and 4.51 ± 1.61 vs. 6.04 ± 1.75, P < .009, respectively. Differences in CT densitometry between alveolitis and fibrosis were not significant. CTTA parameters are significantly different in active alveolitis vs. fibrosis in patients with SSc and may be helpful for differentiation of these two entities.

Evaluation of Texture Analysis Parameter for Response Prediction in Patients with Hepatocellular Carcinoma Undergoing Drug-eluting Bead Transarterial Chemoembolization (DEB-TACE) Using Biphasic Contrast-enhanced CT Image Data: Correlation with Liver Perfusion CT

This study aimed to evaluate the potential role of computed tomography texture analysis (CTTA) of arterial and portal-venous enhancement phase image data for prediction and accurate assessment of response of hepatocellular carcinoma undergoing drug-eluting bead transarterial chemoembolization (TACE) by comparison to liver perfusion CT (PCT). Twenty-eight patients (27 male; mean age 67.2 ± 10.4) with 56 hepatocellular carcinoma-typical liver lesions were included. Arterial and portal-venous phase CT data obtained before and after TACE with a mean time of 39.93 ± 62.21 days between examinations were analyzed. TACE was performed within 48 hours after first contrast-enhanced CT. CTTA software was a prototype. CTTA analysis was performed blinded (for results) by two observers separately. Combined results of modified Response Evaluation Criteria In Solid Tumors (mRECIST) and PCT of the liver were used as the standard of reference. Time to progression was additionally assessed for all patients. CTTA parameters included heterogeneity, intensity, average, deviation, skewness, and entropy of co-occurrence. Each parameter was compared to those of PCT (blood flow [BF], blood volume, arterial liver perfusion [ALP], portal-venous perfusion, and hepatic perfusion index) measured before and after TACE. mRECIST + PCT yielded 28.6% complete response (CR), 42.8% partial response, and 28.6% stable disease. Significant correlations were registered in the arterial phase in CR between changes in mean heterogeneity and BF (P = .004, r = -0.815), blood volume (P = .002, r = -0.851), and ALP (P = .002, r = -0.851), respectively. In the partial response group, changes in mean heterogeneity correlated with changes in ALP (P = .003) and to a lesser degree with hepatic perfusion index (P = .027) in the arterial phase. In the stable disease group, BF correlated with entropy of nonuniformity (P = .010). In the portal-venous phase, no statistically significant correlations were registered in all groups. Receiver operating characteristic analysis of CTTA parameters yielded predictive cutoff values for CR in the arterial contrast-enhanced CT phase for uniformity of skewness (sensitivity: 90.0%; specificity: 45.8%), and in the portal-venous phase for uniformity of heterogeneity (sensitivity: 92.3%; specificity: 81.8%). Significant correlations exist between CTTA parameters and those derived from PCT both in the pre- and the post-TACE settings, and some of them have predictive value for TACE midterm outcome.

Can CT measures of tumour heterogeneity stratify risk for nodal metastasis in patients with non-small cell lung cancer?

To undertake a preliminary assessment of the potential for computed tomography (CT) measurement of tumour heterogeneity to stratify risk of nodal metastasis in patients with non-small cell lung cancer (NSCLC). Tumour heterogeneity in CT images from combined positron-emission tomography (PET)/CT examinations in 150 consecutive patients with NSCLC was assessed using CT texture analysis (CTTA). The short axis diameter of the largest mediastinal node was also measured. Forty-two patients without distant metastases subsequently had tumour nodal status confirmed at surgery (n=26) or endobronchial ultrasound (EBUS; n=16). CTTA parameters and largest nodal diameter were related to nodal status using the rank correlation and the risk ratio for each nodal stage (>N0, >N1, >N2) was compared between patients categorised as high and low risk by CTTA or nodal size. The most significant predictor of nodal status was related to overall survival using Kaplan-Meier analysis. N-stage was more significantly correlated with CTTA than nodal diameter (Rs = -0.39, p=0.011, Rs = -0.45, p=0.0025, Rs = -0.40, p=0.0091 for normalised standard deviation (SD), normalised entropy and kurtosis respectively; Rs = -0.39, p=0.042 for nodal diameter). The presence of two or more high-risk CTTA values was the greatest risk factor for mediastinal metastasis (risk ratio: 11.0, 95% confidence interval: 1.56-77.8, p=0.0014) and was associated with significantly poorer overall survival (p=0.016). CTTA in NSCLC is related to nodal status in patients without distant metastases and has the potential to inform selection of investigative strategies for the assessment of mediastinal malignancy.

Texture analysis of the liver at MDCT for assessing hepatic fibrosis.

To evaluate CT texture analysis (CTTA) for staging of hepatic fibrosis (stages F0-F4) METHODS: Quantitative texture analysis (QTA) of the liver was performed on abdominal MDCT scans using commercially available software (TexRAD), which uses a filtration-histogram statistic-based technique. Single-slice ROI measurements of the total liver, Couinaud segments IV-VIII, and segments I-III were obtained. CTTA parameters were correlated against fibrosis stage (F0-F4), with biopsy performed within one year for all cases with intermediate fibrosis (F1-F3). The study cohort consisted of 289 adults (158M/131W; mean age, 51 years), including healthy controls (F0, n=77), and patients with increasing stages of fibrosis (F1, n=42; F2 n=37; F3 n=53; F4 n=80). Mean gray-level intensity increased with fibrosis stage, demonstrating an ROC AUC of 0.78 at medium filtration for F0 vs F1-4, with sensitivity and specificity of 74% and 74% at cutoff 0.18. For significant fibrosis (≥F2), mean showed AUCs ranging from 0.71-0.73 across medium- and coarse- filtered textures with sensitivity and specificity of 71% and 68% at cutoff of 0.3, with similar performance also observed for advanced fibrosis (≥F3). Entropy showed a similar trend. Conversely, kurtosis and skewness decreased with increasing fibrosis, particularly in cirrhotic patients. For cirrhosis (≥F4), kurtosis and skewness showed AUCs of 0.86 and 0.87, respectively, at coarse-filtered scale, with skewness showing a sensitivity and specificity of 84% and 75% at cutoff of 1.3. CTTA may be helpful in detecting the presence of hepatic fibrosis and discriminating between stages of fibrosis, particularly at advanced levels.

Characterization of Portal Vein Thrombosis (Neoplastic Versus Bland) on CT Images Using Software-Based Texture Analysis and Thrombus Density (Hounsfield Units).

The purpose of this study was to investigate the role of CT texture analysis and thrombus density (measured in Hounsfield units) in distinguishing between neoplastic and bland portal vein thrombosis (PVT) on portal venous phase CT. In this retrospective study, 117 contrast-enhanced CT studies of 109 patients were included for characterization of PVT. Assessment of PVT was performed by estimation of CT textural features using CT texture analysis software and measurement of attenuation values. For CT texture analysis, filtered and unfiltered images were assessed to quantify heterogeneity using a set of predefined histogram-based texture parameters. The Mann-Whitney U test and binary logistic regression were applied for statistical significance. ROC curves were used to identify accuracy and optimal cutoff values. Of the 117 CT studies, 63 neoplastic thrombi and 54 bland thrombi were identified on the images. The two most discriminative CT texture analysis parameters to differentiate neoplastic from bland thrombus were mean value of positive pixels (without filtration, p < 0.001) and entropy (with fine filtration, p < 0.001). Mean thrombus density values could also reliably distinguish neoplastic (81.39 HU) and bland (32.88 HU) thrombi (p < 0.001). The AUCs were 0.97 for mean value of positive pixels (p < 0.001), 0.93 for entropy (p < 0.001), 0.99 for the model combining mean value of positive pixels and entropy (p < 0.001), 0.91 for thrombus density (p < 0.001), and 0.61 for the radiologist's subjective evaluation (p = 0.037). The optimal cutoffs values were 56.9 for mean value of positive pixels, 4.50 for entropy, and 54.0 HU for thrombus density. CT texture analysis and CT attenuation values allow reliable differentiation between neoplastic and bland thrombi on a single portal venous phase CT examination.