Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI: Hepatic Fibrosis on Proton Density MRI

Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review

Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.

Diagnostic performance of texture analysis in the differential diagnosis of perianal fistulising Crohn’s disease and glandular anal fistula

BACKGROUND

Perianal fistulising Crohn's disease (PFCD) and glandular anal fistula have many similarities on conventional magnetic resonance imaging. However, many patients with PFCD show concomitant active proctitis, but only few patients with glandular anal fistula have active proctitis.

AIM

To explore the value of differential diagnosis of PFCD and glandular anal fistula by comparing the textural feature parameters of the rectum and anal canal in fat suppression T2-weighted imaging (FS-T2WI).

METHODS

Patients with rectal water sac implantation were screened from the first part of this study (48 patients with PFCD and 22 patients with glandular anal fistula). Open-source software ITK-SNAP (Version 3.6.0, http://www.itksnap.org/) was used to delineate the region of interest (ROI) of the entire rectum and anal canal wall on every axial section, and then the ROIs were input in the Analysis Kit software (version V3.0.0.R, GE Healthcare) to calculate the textural feature parameters. Textural feature parameter differences of the rectum and anal canal wall between the PFCD group vs the glandular anal fistula group were analyzed using Mann-Whitney U test. The redundant textural parameters were screened by bivariate Spearman correlation analysis, and binary logistic regression analysis was used to establish the model of textural feature parameters. Finally, diagnostic accuracy was assessed by receiver operating characteristic-area under the curve (AUC) analysis.

RESULTS

In all, 385 textural parameters were obtained, including 37 parameters with statistically significant differences between the PFCD and glandular anal fistula groups. Then, 16 texture feature parameters remained after bivariate Spearman correlation analysis, including one histogram parameter (Histogram energy); four grey level co-occurrence matrix (GLCM) parameters (GLCM energy_all direction_offset1_SD, GLCM entropy_all direction_ offset4_SD, GLCM entropy_all direction_offset7_SD, and Haralick correlation_all direction_ offset7_SD); four texture parameters (Correlation_all direction_offset1_SD, cluster prominence _angle 90_offset4, Inertia_all direction_offset7_SD, and cluster shade_angle 45_offset7); five grey level run-length matrix parameters (grey level nonuniformity_angle 90_offset1, grey level nonuniformity_all direction_offset4_SD, long run high grey level emphasis_all direction_offset1_SD, long run emphasis_all direction_ offset4_ SD, and long run high grey level emphasis_all direction_offset4_SD); and two form factor parameters (surface area and maximum 3D diameter). The AUC, sensitivity, and specificity of the model of textural feature parameters were 0.917, 85.42%, and 86.36%, respectively.

CONCLUSION

The model of textural feature parameters showed good diagnostic performance for PFCD. The texture feature parameters of the rectum and anal canal in FS-T2WI are helpful to distinguish PFCD from glandular anal fistula.

Simultaneous liver steatosis, fibrosis and iron deposition quantification with mDixon quant based on radiomics analysis in a rabbit model

PURPOSE

To evaluate the feasibility of simultaneous quantification of liver fibrosis, liver steatosis and abnormal iron deposition using mDixon Quant based on radiomics analysis, and to eliminate the interference among different histopathologic features.

METHODS

One hundred and twenty rabbits that were administered CCl4 for 4-16 weeks and a cholesterol rich diet for the initial 4 weeks in the experimental group and 20 rabbits in the control group were examined using mDixon. Radiomics features of the whole liver were extracted from PDFF and R2* and radiomics models for discriminating steatosis: S0-S1 vs. S2-S4, fibrosis: F0-F2 vs. F3-F4 and iron deposition: normal vs. abnormal were constructed respectively and evaluated using receiver operating characteristic (ROC) curves with the histopathological results as reference standard. Combined corrected models merging the radscore and the other two histopathologic features were evaluated using multiple logistic regression analyses and compared with radiomics models.

RESULTS

The area under the ROC curve (AUC) of the radiomics model with PDFF features was 0.886 and 0.843 in the training and the test set, respectively, for the diagnosis of liver steatosis grade S0-1 and S2-S4. The radiomics model based on R2* features were 0.815 and 0.801 for distinguishing F0-F2 and F3-F4 and 0.831 and 0.738 for discriminating abnormal iron deposition in the training and test set, respectively. The corrected model for liver steatosis and fibrosis (0.944 and 0.912 in the test set) outperformed the radiomics models by eliminating the interference of histopathologic features(P < 0.05), but had comparable diagnostic performance for abnormal iron deposition(P > 0.05).

CONCLUSIONS

It is feasible for mDixon to simultaneously quantify whole liver steatosis, fibrosis and iron deposition based on radiomics analysis. It is valuable to minimize the interference of different pathological features for the assessment of liver steatosis and fibrosis.

A Toolbox to Investigate the Impact of Impaired Oxygen Delivery in Experimental Disease Models

Impaired oxygen utilization is the underlying pathophysiological process in different shock states. Clinically most important are septic and hemorrhagic shock, which comprise more than 75% of all clinical cases of shock. Both forms lead to severe dysfunction of the microcirculation and the mitochondria that can cause or further aggravate tissue damage and inflammation. However, the detailed mechanisms of acute and long-term effects of impaired oxygen utilization are still elusive. Importantly, a defective oxygen exploitation can impact multiple organs simultaneously and organ damage can be aggravated due to intense organ cross-talk or the presence of a systemic inflammatory response. Complexity is further increased through a large heterogeneity in the human population, differences in genetics, age and gender, comorbidities or disease history. To gain a deeper understanding of the principles, mechanisms, interconnections and consequences of impaired oxygen delivery and utilization, interdisciplinary preclinical as well as clinical research is required. In this review, we provide a "tool-box" that covers widely used animal disease models for septic and hemorrhagic shock and methods to determine the structure and function of the microcirculation as well as mitochondrial function. Furthermore, we suggest magnetic resonance imaging as a multimodal imaging platform to noninvasively assess the consequences of impaired oxygen delivery on organ function, cell metabolism, alterations in tissue textures or inflammation. Combining structural and functional analyses of oxygen delivery and utilization in animal models with additional data obtained by multiparametric MRI-based techniques can help to unravel mechanisms underlying immediate effects as well as long-term consequences of impaired oxygen delivery on multiple organs and may narrow the gap between experimental preclinical research and the human patient.

Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI

With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 70–75% (95% CI 0.48–0.89), and specificity of 71–79% (95% CI 0.52–0.90) on manual optimization, and an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 65–75% (95% CI 0.43–0.89) and specificity of 75–79% (95% CI 0.56–0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.

Computed Tomography-Based Radiomic Analysis for Preoperatively Predicting the Macrovesicular Steatosis Grade in Cadaveric Donor Liver Transplantation

This study is aimed at determining the ability of computed tomography- (CT-) based radiomic analysis to distinguish between grade 0/1 and grade 2/3 macrovesicular steatosis (MaS) in cadaveric donor liver transplantation cases. Preoperative noncontrast-enhanced CT images of 150 patients with biopsy-confirmed MaS were analyzed retrospectively; these patients were classified into the low-grade MaS (n = 100, grade 0 or 1) and high-grade MaS (n = 50, grade 2 or 3) groups. Three-dimensional spherical regions of interest of 40 pixel (2.5 cm) in diameter were placed in the right anterior and left lateral segments of the liver. Thereafter, 300 regions of interest (ROIs) were segmented and randomly assigned to the training and testing groups at a ratio of 7 : 3. A total of 402 radiomic features were extracted from each ROI. For MaS classification, a radiomic model was established using multivariate logistic regression analysis. Clinical data, including age, sex, and liver function, were collected to establish the clinical model at the patient level. The performances of the radiomic and clinical models, i.e., the diagnostic discrimination, calibration, and clinical utilities, were evaluated. The radiomic model, with seven selected features, depicted a good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.907 (95% confidence interval (CI): 0.869–0.940) in the training cohort and 0.906 (95% CI: 0.843–0.959) in the testing cohort. The calibration curve revealed good agreement between the predicted and observed probabilities in the training and testing cohorts (both P > 0.05 in the H-L test). Decision curve analysis revealed that the radiomic model was more beneficial than the treat-all or treat-none schemes for predicting the MaS grade. Alanine transaminase and gamma-glutamyl transferase were used for building the clinical model, and the AUC was 0.784 in the total cohort. The CT-based radiomic model outperforming the conventional clinical model could provide an important reference for MaS grading in cadaveric liver donors.

Assessment of renal function using magnetic resonance quantitative histogram analysis based on spatial labeling with multiple inversion pulses

Background

The incidence of chronic kidney disease (CKD) is high, and is easy to develop into end-stage renal disease (ESRD), which requires kidney dialysis or kidney transplantation. Therefore, we want to explore the clinical value of magnetic resonance quantitative histogram analysis based on spatial labeling with multiple inversion pulses (SLEEK) in assessing renal function in the early stage.

Methods

One hundred and twenty-nine patients underwent abdominal MRI examination, including a coronal SLEEK sequence. The patients were divided into the control group [CG, 47 cases, estimated glomerular filtration rate (eGFR) >90], the mild renal function impairment (mRI) group (48 cases, eGFR =60–90), and the moderate to severe renal function impairment (m-sRI) group (34 cases, eGFR <60). Two experienced radiologists delineated cortex and medulla regions of interest (ROIs) on SLEEK images to obtain cortex and medulla quantitative histogram parameters [Mean, Median, Percentiles (5^th, 10^th, 25^th, 75^th, and 90^th), Skewness, Kurtosis, and Entropy] using FireVoxel. These histogram parameters were compared by proper statistical methods such as one-way analysis of variance, the χ² test, and receiver operating characteristic (ROC) curve analysis.

Results

Four histogram parameters (Inhomogeneity_cortex, Skewness_cortex, Kurtosis_medulla, and Entropy_medulla) differed significantly between the CG and the mRI group. One medulla (Entropy_medulla) and nine cortex (Mean_cortex, Median_cortex, Kurtosis_cortex, Entropy_cortex, and 5^th, 10^th, 25^th, 75^th, and 90^th Percentile_cortex) histogram parameters were significantly different between the m-RI and m-sRI groups. The most relevant parameter to eGFR was Inhomogenity_cortex (r=−0.450, P<0.001). Inhomogeneity_cortex had the largest area under the curve (AUC) for differentiating the mRI group from the CG (AUC =0.718; 95% CI: 0.616–0.806), while 25^th Percentile_cortex generated the largest AUC (AUC =0.786; 95% CI: 0.681–0.869) for differentiating the mRI and m-sRI groups.

Conclusions

Quantitative histogram parameters based on a SLEEK sequence can be used to supplement renal dysfunction assessment. Cortex histogram parameters are more valuable for evaluating renal function than medulla histogram parameters.

Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm

Background

Accurate characterization of small nodules in a cirrhotic liver is challenging. We aimed to determine the additive value of MRI-based radiomics analysis to Liver Imaging Reporting and Data System version 2018 (LI-RADS v 2018) algorithm in differentiating small (≤ 3 cm) hepatocellular carcinomas (HCCs) from benign nodules in cirrhotic liver.

Methods

In this retrospective study, 150 cirrhosis patients with histopathologically confirmed small liver nodules (HCC, 112; benign nodules, 44) were evaluated from January 2013 to October 2018. Based on the LI-RADS algorithm, a LI-RADS category was assigned for each lesion. A radiomics signature was generated based on texture features extracted from T1-weighted, T2W, and apparent diffusion coefficient (ADC) images by using the least absolute shrinkage and selection operator regression model. A nomogram model was developed for the combined diagnosis. Diagnostic performance was assessed using receiver operating characteristic curve (ROC) analysis.

Results

A radiomics signature consisting of eight features was significantly associated with the differentiation of HCCs from benign nodules. Both LI-RADS algorithm (area under ROC [A_z] = 0.898) and the MRI-Based radiomics signature (A_z = 0.917) demonstrated good discrimination, and the nomogram model showed a superior classification performance (A_z = 0.975). Compared with LI-RADS alone, the combined approach significantly improved the specificity (97.7% vs 81.8%, p = 0.030) and positive predictive value (99.1% vs 92.9%, p = 0.031) and afforded comparable sensitivity (97.3% vs 93.8%, p = 0.215) and negative predictive value (93.5% vs 83.7%, p = 0.188).

Conclusions

MRI-based radiomics analysis showed additive value to the LI-RADS v 2018 algorithm for differentiating small HCCs from benign nodules in the cirrhotic liver.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01710-y.

Recent advances in medical image processing for the evaluation of chronic kidney disease

Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.

Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?

Background

Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas.

Methods

A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve.

Results

Two radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model).

Conclusions

The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

Deep residual nets model for staging liver fibrosis on plain CT images

PURPOSE

The early diagnosis of liver fibrosis is crucial for the prevention of liver cirrhosis and liver cancer. As gold standard for staging liver fibrosis, liver biopsy is an invasive procedure that carries the risk of serious complications. The aim of this study was to evaluate the performance of the residual neural network (ResNet), a non-invasive methods, for staging liver fibrosis using plain CT images.

METHODS

This retrospective study involved 347 patients subjected to liver CT scanning and liver biopsy. For each patient, we selected three axial images adjacent to the puncture location in the eighth or ninth inter-space on the right side. After processing and enhancement (rotation, translation, and amplification), these images were used as input data for the ResNet model. The model used a fivefold cross-validation method. In each fold, the images of approximately 80% of the total sample size (278 patients) were used for training the ResNet model, the other 20% (69 patients) were used for testing the trained network, with the liver biopsy pathology results as gold standard. The proportion of patients in each fibrosis stage was equal for training and test groups. The final result was the mean of the fivefold cross-validation in the test group. The performance of the ResNet model was evaluated for the test group by receiver operating characteristic (ROC) analysis.

RESULTS

For the ResNet model, the area under the ROC curve (AUC) for assessing cirrhosis (F4), advanced fibrosis (F3 or higher), significant fibrosis (F2 or higher), and mild fibrosis (F1 or higher) was 0.97, 0.94, 0.90, and 0.91, respectively.

CONCLUSIONS

The ResNet model analysis of plain CT images exhibited high diagnostic efficiency for liver fibrosis staging. As a convenient, fast, and economical non-invasive diagnostic method, the ResNet model can be used to assist radiologists and clinicians in liver fibrosis evaluations.

Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology

OBJECTIVES

To compare the diagnostic accuracy of texture analysis (TA)-derived parameters combined with machine learning (ML) of non-contrast-enhanced T1w and T2w fat-saturated (fs) images with MR elastography (MRE) for liver fibrosis quantification.

METHODS

In this IRB-approved prospective study, liver MRIs of participants with suspected chronic liver disease who underwent liver biopsy between August 2015 and May 2018 were analyzed. Two readers blinded to clinical and histopathological findings performed TA. The participants were categorized into no or low-stage (0-2) and high-stage (3-4) fibrosis groups. Confusion matrices were calculated using a support vector machine combined with principal component analysis. The diagnostic accuracy of ML-based TA of liver fibrosis and MRE was assessed by area under the receiver operating characteristic curves (AUC). Histopathology served as reference standard.

RESULTS

A total of 62 consecutive participants (40 men; mean age ± standard deviation, 48 ± 13 years) were included. The accuracy of TA and ML on T1w was 85.7% (95% confidence interval [CI] 63.7-97.0) and 61.9% (95% CI 38.4-81.9) on T2w fs for classification of liver fibrosis into low-stage and high-stage fibrosis. The AUC for TA on T1w was similar to MRE (0.82 [95% CI 0.59-0.95] vs. 0.92 [95% CI 0.71-0.99], p = 0.41), while the AUC for T2w fs was significantly lower compared to MRE (0.57 [95% CI 0.34-0.78] vs. 0.92 [95% CI 0.71-0.99], p = 0.008).

CONCLUSION

Our results suggest that liver fibrosis can be quantified with TA-derived parameters of T1w when combined with a ML algorithm with similar accuracy compared to MRE.

KEY POINTS

• Liver fibrosis can be categorized into low-stage fibrosis (0-2) and high-stage fibrosis (3-4) using texture analysis-derived parameters of T1-weighted images with a machine learning approach. • For the differentiation of low-stage fibrosis and high-stage fibrosis, the diagnostic accuracy of texture analysis on T1-weighted images combined with a machine learning algorithm is similar compared to MR elastography.

Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions

RESULTS

The 34 nodules comprised 14 benign nodules and 20 malignant nodules. Iodine content and Hounsfield unit curve slopes did not differ significantly between benign and malignant thyroid nodules (P = 0.480-0.670). However, significant differences in the texture features of monochromatic images were observed between benign and malignant nodules: histogram mean and median, co-occurrence matrix contrast, gray-level gradient matrix (GLGM) skewness, and mean gradients and variance of gradients for GLGM at 80 keV (P = 0.014-0.044). The highest AUC was 0.77, for the histogram mean and median of images acquired at 80 keV.

CONCLUSIONS

Texture features extracted from monochromatic images using DECT, specifically acquired at high keV, may be a promising diagnostic approach for thyroid nodules. A further large study for incidental thyroid nodules using DECT texture analysis is required to validate our results.

Using CT texture analysis to differentiate cystic and cystic-appearing odontogenic lesions

PURPOSE

Cystic and cystic-appearing odontogenic lesions of the jaw may appear similar on CT imaging. Accurate diagnosis is often difficult although the relationship of the lesion to the tooth root or crown may offer a clue to the etiology. The purpose of this study was to evaluate CT texture analysis as an aid in differentiating cystic and cystic-appearing odontogenic lesions of the jaw.

METHODS

This was an IRB-approved retrospective study including 42 pathology-proven dentigerous cysts, 37 odontogenic keratocysts, and 19 ameloblastomas. Each lesion was manually segmented on axial CT images, and textural features were analyzed using an in-house-developed Matlab-based texture analysis program that extracted 47 texture features from each segmented volume. Statistical analysis was performed comparing all pairs of the three types of lesions.

RESULTS

Pairwise analysis revealed that nine histogram features, one GLCM feature, three GLRL features, two Laws features, four GLGM features and two Chi-square features showed significant differences between dentigerous cysts and odontogenic keratocysts. Four histogram features and one Chi-square feature showed significant differences between odontogenic keratocysts and ameloblastomas. Two histogram features showed significant differences between dentigerous cysts and ameloblastomas.

CONCLUSIONS

CT texture analysis may be useful as a noninvasive method to obtain additional quantitative information to differentiate cystic and cystic-appearing odontogenic lesions of the jaw.

Quantification of Degree of Liver Fibrosis Using Fibrosis Area Fraction Based on Statistical Chi-Square Analysis of Heterogeneity of Liver Tissue Texture on Routine Ultrasound Images

RATIONALE AND OBJECTIVES

We present a novel method to quantify the degree of liver fibrosis using fibrosis area fraction based on statistical chi-square analysis of heterogeneity of echo texture within liver on routine ultrasound images. We demonstrate, in a clinical study, that fibrosis area fraction derived this way has the potential to become a noninvasive, quantitative radiometric discriminator of normal or low-grade liver fibrosis (Ishak fibrosis score range = F0-3) and advanced liver fibrosis or cirrhosis (Ishak fibrosis score range = F4-6) on routine ultrasound images.

MATERIALS AND METHODS

This retrospective patient study was institutional review board approved. Ultrasound images of 100 patients (61 males, 39 females; 18-81 years) who underwent nontargeted ultrasound-guided biopsy were randomly divided into two groups: a training group consisted of 31 cases, and a validation group that contained the rest cases. An investigator manually selected a primary region of interest (ROI; approximately 4-6 cm²) in the liver tissue while avoiding nonhepatic parenchyma. The primary ROI contained a large number of secondary ROIs (25 × 25 pixels) to maintain the precision of statistical analysis. Sample variance σ² of image gradient (a texture feature related to the amount of edge structures) was calculated in secondary ROIs in a roster scan fashion. A theoretical derivation was presented to estimate population variance [Formula: see text] of image gradient across the primary ROI from mean gradient µ of secondary ROIs. The χ² (χ² = σ²/ [Formula: see text] ) was computed at each secondary ROI, forming a χ² map of liver tissue heterogeneity. A cut-off value was optimized from datasets in the training group by comparing to the fibrosis grades determined by biopsy. This cut-off value was then applied to the datasets in the validation group to convert the χ² maps into binary images, from which fibrosis area fractions (fraction of fibrosis area to the total area of the primary ROI) were calculated and entered in a statistical analysis.

RESULTS

In the training group, the optimal setting was found to be [Formula: see text] = 6.0, which resulted a maximum discrimination of F0-3 vs F4-6: p < 0.0001, area under curve = 0.985, sensitivity = 93.7%, specificity = 93.3%. When this setting was applied to the datasets in the validation group, a distinct separation was seen between the two classes (p < 0.0001). F0-3 class had an average fibrosis area fraction of 4.7% (1.7%-11.4%), whereas the F4-6 class had an average fibrosis area fraction of 17.3% (9.8%-29.6%). A strong correlation was demonstrated between the fibrosis area fraction and histological fibrosis grade determined by biopsy (area under curve = 0.89, sensitivity = 87.9%, specificity = 90.3%).

CONCLUSION

The presented method is a promising noninvasive tool for distinguishing normal or low-grade liver fibrosis (F0-3) and advanced liver fibrosis or cirrhosis (F4-6) from routine ultrasound images. These findings support the further development of texture heterogeneity analysis, particularly fibrosis area fraction, as a quantitative biomarker for distinguishing various liver fibrosis grades.

Accuracy of ultrasonography in the assessment of liver fat compared with MRI

AIM

To investigate the accuracy of ultrasonography in the assessment of hepatic steatosis using magnetic resonance imaging (MRI) as standard of reference and to explore the influence of additional hepatic iron overload.

MATERIAL AND METHODS

A total of 2,783 volunteers (1,442 women, 1,341 men; mean age, 52.3±13.8 years) underwent confounder-corrected chemical-shift-encoded MRI of the liver at 1.5 T. Proton-density fat fraction (PDFF) and transverse relaxation rate (R2*) were calculated to estimate hepatic steatosis and liver iron overload, respectively. In addition, the presence of hepatic steatosis was assessed by B-mode ultrasonography. The sensitivity, specificity, and accuracy of hepatic ultrasonography were determined for different degrees of hepatic steatosis and different amounts of liver iron.

RESULTS

MRI revealed hepatic steatosis in 40% of participants (n=1,112), which was mild in 68.9% (n=766), moderate in 26.7% (n=297), and severe in 4.4% (n=49) of patients. Ultrasonography detected hepatic steatosis in 37.8% (n=1,052), corresponding to 74.5% sensitivity and 86.6% specificity. The sensitivity of ultrasound increased with the amount of hepatic fat present and was 65.1%, 95%, and 96% for low, moderate, and high fat content; whereas the specificity was constantly high at 86.6%. The diagnostic accuracy of ultrasound for detection of hepatic steatosis did not vary significantly with the amount of liver iron present.

CONCLUSION

Ultrasonography is an excellent tool to assess hepatic steatosis in the clinical setting with some limitations in patients with a low liver fat content. The detection of hepatic steatosis by ultrasonography is not influenced by liver iron.

CT Texture Analysis of Cervical Lymph Nodes on Contrast-Enhanced [18F] FDG-PET/CT Images to Differentiate Nodal Metastases from Reactive Lymphadenopathy in HIV-Positive Patients with Head and Neck Squamous Cell Carcinoma

BACKGROUND AND PURPOSE

Differentiating nodal metastases from reactive adenopathy in HIV-infected patients with [¹⁸F] FDG-PET/CT can be challenging because lymph nodes in HIV-positive patients often show increased [¹⁸F] FDG uptake. The purpose of this study was to assess CT textural analysis characteristics of HIV-positive and HIV-negative lymph nodes on [¹⁸F] FDG-PET/CT to differentiate nodal metastases from disease-specific nodal reactivity.

MATERIALS AND METHODS

Nine HIV-positive patients with head and neck squamous cell carcinoma (7 men, 2 women; 29-62 years of age; median age, 48 years) with 22 lymph nodes (≥1 cm) who underwent contrast-enhanced CT with [¹⁸F] FDG-PET followed by pathologic evaluation of cervical lymph nodes were retrospectively reviewed. Twenty-six HIV-negative patients with head and neck squamous cell carcinoma with 61 lymph nodes were evaluated as a control group. Each lymph node was manually segmented, and an in-house-developed Matlab-based texture analysis program extracted 41 texture features from each segmented volume. A mixed linear regression model was used to compare the pathologically proved malignant lymph nodes with benign nodes in the 2 enrolled groups.

RESULTS

Thirteen (59%) lymph nodes in the HIV-positive group and 22 (36%) lymph nodes in the HIV-negative control group were confirmed as positive for metastases. There were 7 histogram features (P = .017-0.032), 3 gray-level co-occurrence features (P = .009-.025), and 9 gray-level run-length features (P < .001-.033) that demonstrated a significant difference in HIV-positive patients with either benign or malignant lymph nodes.

CONCLUSIONS

CT texture analysis may be useful as a noninvasive method of obtaining additional quantitative information to differentiate nodal metastases from disease-specific nodal reactivity in HIV-positive patients with head and neck squamous cell carcinoma.

Evaluation of renal dysfunction using texture analysis based on DWI, BOLD, and susceptibility-weighted imaging

OBJECTIVE

To explore the value of texture analysis based on diffusion-weighted imaging (DWI), blood oxygen level-dependent MRI (BOLD), and susceptibility-weighted imaging (SWI) in evaluating renal dysfunction.

METHODS

Seventy-two patients (mean age 53.72 ± 13.46 years) underwent MRI consisting of DWI, BOLD, and SWI. According to their estimated glomerular filtration rate (eGFR), the patients were classified into either severe renal function impairment (sRI, eGFR < 30 mL/min/1.73 m²), non-severe renal function impairment (non-sRI, eGFR ≥ 30 mL/min/1.73 m², and < 80 mL/min/1.73 m²), or control (CG, eGFR ≥ 80 mL/min/1.73 m²) groups. Thirteen texture features were extracted and then were analyzed to select the most valuable for discerning the three groups with each imaging method. A ROC curve was performed to compare the capacities of the features to differentiate non-sRI from sRI or CG.

RESULTS

Six features proved to be the most valuable for assessing renal dysfunction: 0.25Quantile_DWI, 0.5Quantile_DWI, Homogeneity_DWI, Entropy_BOLD, Skewness_SWI, and Correlation_SWI. Three features derived from DWI (0.25Quantile_DWI, 0.5Quantile_DWI, and Homogeneity_DWI) were smaller in sRI than in non-sRI; Entropy_BOLD and Correlation_SWI were smaller in non-sRI than in CG (p < 0.05). 0.25Quantile_DWI, 0.5Quantile_DWI, and Homogeneity_DWI showed similar capacities for differentiating sRI from non-sRI. Similarly, Entropy_BOLD and Correlation_SWI showed equal capacities for differentiating non-sRI from CG.

CONCLUSION

Texture analysis based on DWI, BOLD, and SWI can assist in assessing renal dysfunction, and texture features based on BOLD and SWI may be suitable for assessing renal dysfunction during early stages.

KEY POINTS

• Texture analysis based on MRI techniques allowed for assessing renal dysfunction. • Texture features based on BOLD and SWI, but not DWI, may be suitable for assessing renal function impairment during early stages. • SWI exhibited a similar capacity to BOLD for assessing renal dysfunction.

Imaging of Hepatic Fibrosis

PURPOSE OF REVIEW

The purpose of this review is to discuss the current imaging techniques for non-invasive assessment of liver fibrosis (LF).

RECENT FINDINGS

Elastography-based techniques are the most widely used imaging methods for the evaluation of LF. Currently, MR elastography (MRE) is the most accurate non-invasive method for detection and staging of LF. Ultrasound-based vibration-controlled transient elastography (VCTE) is the most widely used as it can be easily performed at the point of care but has technical limitations especially in the obese. Innovations and technical improvements continue to evolve in elastography for improving accuracy and avoiding misinterpretation from confounding factors. Other imaging methods including diffusion-weighted imaging (DWI), hepatocellular contrast-enhanced (HCE) MRI, T1 relaxometry, T1ρ imaging, textural analysis, liver surface nodularity, susceptibility-weighted imaging, and perfusion imaging are promising but need further evaluation and clinical validation. MRE is the most accurate imaging technique for assessment of LF.

Liver Fibrosis Quantification by Magnetic Resonance Imaging

Liver fibrosis is a hallmark of chronic liver disease characterized by the excessive accumulation of extracellular matrix proteins. Although liver biopsy is the reference standard for diagnosis and staging of liver fibrosis, it has some limitations, including potential pain, sampling variability, and low patient acceptance. Hence, there has been an effort to develop noninvasive imaging techniques for diagnosis, staging, and monitoring of liver fibrosis. Many quantitative techniques have been implemented on magnetic resonance imaging (MRI) for this indication. The most widely validated technique is magnetic resonance elastography, which aims to measure viscoelastic properties of the liver and relate them to fibrosis stage. Several additional MRI methods have been developed or adapted to liver fibrosis quantification. Diffusion-weighted imaging measures the Brownian motion of water molecules which is restricted by collagen fibers. Texture analysis assesses the changes in the texture of liver parenchyma associated with fibrosis. Perfusion imaging relies on signal intensity and pharmacokinetic models to extract quantitative perfusion parameters. Hepatocellular function, which decreases with increasing fibrosis stage, can be estimated by the uptake of hepatobiliary contrast agents. Strain imaging measures liver deformation in response to physiological motion such as cardiac contraction. T1ρ quantification is an investigational technique, which measures the spin-lattice relaxation time in the rotating frame. This article will review the MRI techniques used in liver fibrosis staging, their advantages and limitations, and diagnostic performance. We will briefly discuss future directions, such as longitudinal monitoring of disease, prediction of portal hypertension, and risk stratification of hepatocellular carcinoma.

Liver fibrosis: in vivo evaluation using intravoxel incoherent motion-derived histogram metrics with histopathologic findings at 3.0 T

PURPOSE

To retrospectively determine the feasibility of intravoxel incoherent motion (IVIM) imaging based on histogram analysis for the staging of liver fibrosis (LF) using histopathologic findings as the reference standard.

METHODS

56 consecutive patients (14 men, 42 women; age range, 15-76, years) with chronic liver diseases (CLDs) were studied using IVIM-DWI with 9 b-values (0, 25, 50, 75, 100, 150, 200, 500, 800 s/mm²) at 3.0 T. Fibrosis stage was evaluated using the METAVIR scoring system. Histogram metrics including mean, standard deviation (Std), skewness, kurtosis, minimum (Min), maximum (Max), range, interquartile (Iq) range, and percentiles (10, 25, 50, 75, 90th) were extracted from apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps. All histogram metrics among different fibrosis groups were compared using one-way analysis of variance or nonparametric Kruskal-Wallis test. For significant parameters, receivers operating characteristic curve (ROC) analyses were further performed for the staging of LF.

RESULTS

Based on their METAVIR stage, the 56 patients were reclassified into three groups as follows: F0-1 group (n = 25), F2-3 group (n = 21), and F4 group (n = 10). The mean, Iq range, percentiles (50, 75, and 90th) of D* maps between the groups were significant differences (all P < 0.05). Area under the ROC curve (AUC) of the mean, Iq range, 50, 75, and 90th percentile of D* maps for identifying significant LF (≥F2 stage) was 0.901, 0.859, 0.876, 0.943, and 0.886 (all P < 0.0001), respectively; for diagnosing severe fibrosis or cirrhosis (F4), AUC was 0.917, 0.922, 0.943, 0.985, and 0.939 (all P < 0.0001), respectively. The histogram metrics of ADC, D, and f maps demonstrated no significant difference among the groups (all P > 0.05).

CONCLUSION

Histogram analysis of D* map derived from IVIM can be used to stage liver fibrosis in patients with CLDs and provide more quantitative information beyond the mean value.

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

BACKGROUND AND PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

Independent primary tumor CT texture analysis parameters are associated with local failure in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.

Texture analysis as a radiomic marker for differentiating renal tumors

PURPOSE

To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma.

MATERIALS AND METHODS

Following IRB approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed. A support vector machine (SVM) method was also applied to classify tumor types. Texture analysis results were compared to the various tumors and areas under the curve (AUC) were calculated. Similar calculations were performed with the SVM data.

RESULTS

One hundred nineteen patients were included. Excellent discriminators of tumors were identified among the histogram-based features noting features skewness and kurtosis, which demonstrated AUCs of 0.91 and 0.93 (p < 0.0001), respectively, for differentiating clear cell subtype from oncocytoma. Histogram feature median demonstrated an AUC of 0.99 (p < 0.0001) for differentiating papillary subtype from oncocytoma and an AUC of 0.92 for differentiating oncocytoma from other tumors. Machine learning further improved the results achieving very good to excellent discrimination of tumor subtypes. The ability of machine learning to distinguish clear cell subtype from other tumors and papillary subtype from other tumors was excellent with AUCs of 0.91 and 0.92, respectively.

CONCLUSION

Texture analysis is a promising non-invasive tool for distinguishing renal tumors on CT images. These results were further improved upon application of machine learning, and support the further development of texture analysis as a quantitative biomarker for distinguishing various renal tumors.

Evaluation of hepatic fibrosis: a review from the society of abdominal radiology disease focus panel

Hepatic fibrosis is potentially reversible; however early diagnosis is necessary for treatment in order to halt progression to cirrhosis and development of complications including portal hypertension and hepatocellular carcinoma. Morphologic signs of cirrhosis on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) alone are unreliable and are seen with more advanced disease. Newer imaging techniques to diagnose liver fibrosis are reliable and accurate, and include magnetic resonance elastography and US elastography (one-dimensional transient elastography and point shear wave elastography or acoustic radiation force impulse imaging). Research is ongoing with multiple other techniques for the noninvasive diagnosis of hepatic fibrosis, including MRI with diffusion-weighted imaging, hepatobiliary contrast enhancement, and perfusion; CT using perfusion, fractional extracellular space techniques, and dual-energy, contrast-enhanced US, texture analysis in multiple modalities, quantitative mapping, and direct molecular imaging probes. Efforts to advance the noninvasive imaging assessment of hepatic fibrosis will facilitate earlier diagnosis and improve patient monitoring with the goal of preventing the progression to cirrhosis and its complications.

Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

Our aim was to evaluate changes in texture features based on variations in CT parameters on a phantom. Scans were performed with varying milliampere, kilovolt, section thickness, pitch, and acquisition mode. Forty-two texture features were extracted by using an in-house-developed Matlab program. Two-tailed t tests and false-detection analyses were performed with significant differences in texture features based on detector array configurations (Q values = 0.001-0.006), section thickness (Q values = 0.0002-0.001), and acquisition mode (Q values = 0.003-0.006). Variations in milliampere and kilovolt had no significant effect.

Liver fibrosis: Review of current imaging and MRI quantification techniques

Liver fibrosis is characterized by the accumulation of extracellular matrix proteins such as collagen in the liver interstitial space. All causes of chronic liver disease may lead to fibrosis and cirrhosis. The severity of liver fibrosis influences the decision to treat or the need to monitor hepatic or extrahepatic complications. The traditional reference standard for diagnosis of liver fibrosis is liver biopsy. However, this technique is invasive, associated with a risk of sampling error, and has low patient acceptance. Imaging techniques offer the potential for noninvasive diagnosis, staging, and monitoring of liver fibrosis. Recently, several of these have been implemented on ultrasound (US), computed tomography, or magnetic resonance imaging (MRI). Techniques that assess changes in liver morphology, texture, or perfusion that accompany liver fibrosis have been implemented on all three imaging modalities. Elastography, which measures changes in mechanical properties associated with liver fibrosis-such as strain, stiffness, or viscoelasticity-is available on US and MRI. Some techniques assessing liver shear stiffness have been adopted clinically, whereas others assessing strain or viscoelasticity remain investigational. Further, some techniques are only available on MRI-such as spin-lattice relaxation time in the rotating frame (T₁ ρ), diffusion of water molecules, and hepatocellular function based on the uptake of a liver-specific contrast agent-remain investigational in the setting of liver fibrosis staging. In this review, we summarize the key concepts, advantages and limitations, and diagnostic performance of each technique. The use of multiparametric MRI techniques offers the potential for comprehensive assessment of chronic liver disease severity.

LEVEL OF EVIDENCE

5 J. MAGN. RESON. IMAGING 2017;45:1276-1295.

Enhanced Laws textures: A potential MRI surrogate marker of hepatic fibrosis in a murine model

PURPOSE

To compare enhanced Laws textures derived from parametric proton density (PD) maps to other MRI surrogate markers (T₂, PD, apparent diffusion coefficient (ADC)) in assessing degrees of liver fibrosis in an ex vivo murine model of hepatic fibrosis imaged using 11.7T MRI.

METHODS

This animal study was IACUC approved. Fourteen male, C57BL/6 mice were divided into control and experimental groups. The latter were fed a 3,5-dicarbethoxy-1,4-dihydrocollidine (DDC) supplemented diet to induce hepatic fibrosis. Ex vivo liver specimens were imaged using an 11.7T scanner, from which the parametric PD, T₂, and ADC maps were generated from spin-echo pulsed field gradient and multi-echo spin-echo acquisitions. A sequential enhanced Laws texture analysis was applied to the PD maps: automated dual-clustering algorithm, optimal thresholding algorithm, global grayscale correction, and Laws texture features extraction. Degrees of fibrosis were independently assessed by digital image analysis (a.k.a. %Area Fibrosis). Scatterplot graphs comparing enhanced Laws texture features, T₂, PD, and ADC values to degrees of fibrosis were generated and correlation coefficients were calculated.

RESULTS

Hepatic fibrosis and the enhanced Laws texture features were strongly correlated with higher %Area Fibrosis associated with higher Laws textures (r=0.89). Without the proposed enhancements, only a moderate correlation was detected between %Area Fibrosis and unenhanced Laws texture features (r=0.70). Correlation also existed between %Area Fibrosis and ADC (r=0.86), PD (r=0.65), and T₂ (r=0.66).

CONCLUSIONS

Higher degrees of hepatic fibrosis are associated with increased Laws textures. The proposed enhancements could improve the accuracy of Laws texture features significantly.

Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images

PURPOSE

Tumor heterogeneity in medical imaging is a current research trend due to its potential relationship with tumor malignancy. The aim of this study is to analyze the effect of dynamic range and matrix size changes on the results of different heterogeneity measures.

MATERIALS AND METHODS

Four patients harboring three glioblastomas and one metastasis were considered. Sixteen textural heterogeneity measures were computed for each patient, with a configuration including co-occurrence matrices (CM) features (local heterogeneity) and run-length matrices (RLM) features (regional heterogeneity). The coefficient of variation measured agreement between the textural measures in two types of experiments: (i) fixing the matrix size and changing the dynamic range and (ii) fixing the dynamic range and changing the matrix size.

RESULTS

None of the measures considered were robust under dynamic range changes. The CM Entropy and the RLM high gray-level run emphasis (HGRE) were the outstanding textural features due to their robustness under matrix size changes. Also, the RLM low gray-level run emphasis (LGRE) provided robust results when the dynamic range considered was sufficiently high (more than 8 levels). All of the remaining textural features were not robust.

CONCLUSION

Tumor texture studies based on images with different characteristics (e.g. multi-center studies) should first fix the dynamic range to be considered. For studies involving images of different resolutions either (i) only robust measures should be used (in our study CM entropy, RLM HGRE and/or RLM LGRE) or (ii) images should be resampled to match those of the lowest resolution before computing the textural features.

The Semi-Variogram and Spectral Distortion Measures for Image Texture Retrieval

Semi-variogram estimators and distortion measures of signal spectra are utilized in this paper for image texture retrieval. On the use of the complete Brodatz database, most high retrieval rates are reportedly based on multiple features and the combinations of multiple algorithms, while the classification using single features is still a challenge to the retrieval of diverse texture images. The semi-variogram, which is theoretically sound and the cornerstone of spatial statistics, has the characteristics shared between true randomness and complete determinism and, therefore, can be used as a useful tool for both the structural and statistical analysis of texture images. Meanwhile, spectral distortion measures derived from the theory of linear predictive coding provide a rigorously mathematical model for signal-based similarity matching and have been proven useful for many practical pattern classification systems. Experimental results obtained from testing the proposed approach using the complete Brodatz database, and the the University of Illinois at Urbana-Champaign texture database suggests the effectiveness of the proposed approach as a single-feature-based dissimilarity measure for real-time texture retrieval.

Using texture analyses of contrast enhanced CT to assess hepatic fibrosis

PURPOSE

To determine the ability of texture analyses of contrast-enhanced CT images for distinguishing between varying degrees of hepatic fibrosis in patients with chronic liver disease using histopathology as the reference standard.

MATERIALS AND METHODS

Following IRB approval, 83 patients who underwent contrast enhanced 64-MDCT of the abdomen and pelvis in the portal venous phase between 12/2005 and 01/2013 and who had a liver biopsy within 6 months of the CT were included. An in-house developed, MATLAB-based texture analysis program was employed to extract 41 texture features from each of 5 axial segmented volumes of liver. Using the Ishak fibrosis staging scale, histopathologic grades of hepatic fibrosis were correlated with texture parameters after stratifying patients into three analysis groups, comparing Ishak scales 0-2 with 3-6, 0-3 with 4-6, and 0-4 with 5-6. To assess the utility of texture features, receiver operating characteristic (ROC) curves were constructed and the area under the curve (AUC) was used to determine the performance of each feature in distinguishing between normal/low and higher grades of hepatic fibrosis.

RESULTS

A total of 19 different texture features with 7 histogram features, one grey level co-occurrence matrix, 6 gray level run length, 1 Laws feature, and 4 gray level gradient matrix demonstrated statistically significant differences for discriminating between fibrosis groupings. The highest AUC values fell in the range of fair performance for distinguishing between different fibrosis groupings.

CONCLUSION

These findings suggest that texture-based analyses of contrast-enhanced CT images offer a potential avenue toward the non-invasive assessment of liver fibrosis.