Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance images

TMM: A comprehensive CAD system for hepatic fibrosis 5-grade METAVIR staging based on liver MRI

BACKGROUND

Precise staging of hepatic fibrosis with MRI is necessary as it can assist precision medicine. Computer aided diagnosis (CAD) system with distinguishing radiomics features and radiologists domain knowledge is expected to obtain 5-grade meta-analysis of histological data in viral hepatitis (METAVIR) staging.

PURPOSE

This study aims to obtain the precise staging of hepatic fibrosis based on MRI as it predicts the risk of future liver-related morbidity and the need for treatment, monitoring and surveillance. Based on METAVIR score, fibrosis can be divided into five stages. Most previous researches focus on binary classification, such as cirrhosis versus non-cirrhosis, early versus advanced fibrosis, and substantial fibrosis or not. In this paper, a comprehensive CAD system TMM is proposed to precisely class hepatic fibrosis into five stages for precision medicine instead of the common binary classification.

METHODS

We propose a novel hepatic fibrosis staging CAD system TMM which includes three modules, Two-level Image Statistical Radiomics Feature (TISRF), Monotonic Error Correcting Output Codes (MECOC) and Monotone Multiclassification with Deep Forest (MMDF). TISRF extracts radiomics features for distinguishing different hepatic fibrosis stages. MECOC is proposed to encode monotonic multiclass by making full use of the progressive severity of hepatic fibrosis and increase the fault tolerance and error correction ability. MMDF combines multiple Deep Forest network to ensure the final five-class classification, which can achieve more precise classification than the common binary classification. The performance of the proposed hepatic fibrosis CAD system is tested on the hepatic data collected from our rabbits models of fibrosis.

RESULTS

A total of 140 regions of interest (ROI) are selected from MRI T1W of liver fibrosis models in 35 rabbits with F0(n = 16), F1(n = 28), F2(n = 29), F3(n = 44) and F4(n = 23). The performance is evaluated by five-fold cross-validation. TMM can achieve the highest total accuracy of 72.14% for five fibrosis stages compared with other popular classifications. To make a comprehensive comparison, a binary classification experiment have been carried out.

CONCLUSIONS

T1WI can obtain precise staging of hepatic fibrosis with the help of comprehensive CAD including radiomics features extraction inspired by radiologists, monotonic multiclass according to the severity of hepatic fibrosis, and deep learning classification.

Opportunistic Evaluation of Trabecular Bone Texture by MRI Reflects Bone Mineral Density and Microarchitecture

CONTEXT

Many individuals at high risk for fracture are never evaluated for osteoporosis and subsequently do not receive necessary treatment. Utilization of magnetic resonance imaging (MRI) is burgeoning, providing an ideal opportunity to use MRI to identify individuals with skeletal deficits. We previously reported that MRI-based bone texture was more heterogeneous in postmenopausal women with a history of fracture compared to controls.

OBJECTIVE

The present study aimed to identify the microstructural characteristics that underlie trabecular texture features.

METHODS

In a prospective cohort, we measured spine volumetric bone mineral density (vBMD) by quantitative computed tomography (QCT), peripheral vBMD and microarchitecture by high-resolution peripheral QCT (HRpQCT), and areal BMD (aBMD) by dual-energy x-ray absorptiometry. Vertebral trabecular bone texture was analyzed using T1-weighted MRIs. A gray level co-occurrence matrix was used to characterize the distribution and spatial organization of voxelar intensities and derive the following texture features: contrast (variability), entropy (disorder), angular second moment (ASM; uniformity), and inverse difference moment (IDM; local homogeneity).

RESULTS

Among 46 patients (mean age 64, 54% women), lower peripheral vBMD and worse trabecular microarchitecture by HRpQCT were associated with greater texture heterogeneity by MRI-higher contrast and entropy (r ∼ -0.3 to 0.4, P < .05), lower ASM and IDM (r ∼ +0.3 to 0.4, P < .05). Lower spine vBMD by QCT was associated with higher contrast and entropy (r ∼ -0.5, P < .001), lower ASM and IDM (r ∼ +0.5, P < .001). Relationships with aBMD were less pronounced.

CONCLUSION

MRI-based measurements of trabecular bone texture relate to vBMD and microarchitecture, suggesting that this method reflects underlying microstructural properties of trabecular bone. Further investigation is required to validate this methodology, which could greatly improve identification of patients with skeletal fragility.

Diffusion-Weighted Imaging as a Quantitative Imaging Biomarker for Predicting Proliferation Rate in Hepatocellular Carcinoma: Developing a Radiomics Nomogram

PURPOSE

This study aimed to explore the predictive performance of diffusion-weighted imaging with apparent diffusion coefficient map in predicting the proliferation rate of hepatocellular carcinoma and to develop a radiomics-based nomogram.

METHODS

This was a single-center retrospective study. A total of 110 patients were enrolled. The sample included 38 patients with low Ki67 expression (Ki67 ≤10%) and 72 with high Ki67 expression (Ki67 >10%) as demonstrated by surgical pathology. Patients were randomly divided into either a training (n = 77) or validation (n = 33) cohort. Diffusion-weighted imaging with apparent diffusion coefficient maps was used to extract radiomic features and the signal intensity values of tumor (SItumor), normal liver (SIliver), and background noise (SIbackground) from all samples. Subsequently, the clinical model, radiomic model, and fusion model (with clinical data and radiomic signature) were developed and validated.

RESULTS

The area under the curve (AUC) of the clinical model for predicting the Ki67 expression including serum α-fetoprotein level (P = 0.010), age (P = 0.015), and signal noise ratio (P = 0.026) was 0.799 and 0.715 in training and validation cohorts, respectively. The AUC of the radiomic model constructed by 9 selected radiomic features was 0.833 and 0.772 in training and validation cohorts, respectively. The AUC of the fusion model containing serum α-fetoprotein level (P = 0.011), age (P = 0.019), and rad score (P < 0.001) was 0.901 and 0.781 in training and validation cohorts, respectively.

CONCLUSIONS

Diffusion-weighted imaging as a quantitative imaging biomarker can predict Ki67 expression level in hepatocellular carcinoma across various models.

Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques

Liver fibrosis features excessive protein accumulation in the liver interstitial space resulting from repeated tissue injury due to chronic liver disease. Liver fibrosis eventually proceeds to cirrhosis and associated complications. So, early diagnosis and staging of liver fibrosis are of vital importance for clinical treatment. Liver biopsy remains the gold standard for the diagnosing and staging of fibrosis, but it is suboptimal due to various limitations. Recently, efforts have been made to migrate toward noninvasive techniques for assessing liver fibrosis. CT is relatively easy to perform, relatively standardized for different scanners, and does not require additional hardware in liver fibrosis staging. MRI is frequently performed to characterize indeterminate liver lesions. Because it does not use ionizing radiation and features high image contrast, its role has increased in the staging of liver fibrosis. More recently, several studies on liver fibrosis staging using deep learning algorithms in CT or MRI have been proposed and have shown meaningful results. In this review, we summarize the basic concept, diagnostic performance, and advantages and limitations of each technique to noninvasively stage liver fibrosis.

MRI-based Texture Analysis of Trabecular Bone for Opportunistic Screening of Skeletal Fragility

CONTEXT

Many individuals at high risk for osteoporosis and fragility fracture are never screened by traditional methods. Opportunistic use of imaging obtained for other clinical purposes is required to foster identification of these patients.

OBJECTIVE

The aim of this pilot study was to evaluate texture features as a measure of bone fragility, by comparing clinically acquired magnetic resonance imaging (MRI) scans from individuals with and without a history of fragility fracture.

METHODS

This study retrospectively investigated 100 subjects who had lumbar spine MRI performed at our institution. Cases (n = 50) were postmenopausal women with osteoporosis and a confirmed history of fragility fracture. Controls (n = 50) were age- and race-matched postmenopausal women with no known fracture history. Trabecular bone from the lumbar vertebrae was segmented to create regions of interest within which a gray level co-occurrence matrix was used to quantify the distribution and spatial organization of voxel intensity. Heterogeneity in the trabecular bone texture was assessed by several features, including contrast (variability), entropy (disorder), and angular second moment (homogeneity).

RESULTS

Texture analysis revealed that trabecular bone was more heterogeneous in fracture patients. Specifically, fracture patients had greater texture variability (+76% contrast; P = 0.005), greater disorder (+10% entropy; P = 0.005), and less homogeneity (-50% angular second moment; P = 0.005) compared with controls.

CONCLUSIONS

MRI-based textural analysis of trabecular bone discriminated between patients with known osteoporotic fractures and controls. Further investigation is required to validate this promising methodology, which could greatly expand the number of patients screened for skeletal fragility.

The utility of two-dimensional shear wave elastography and texture analysis for monitoring liver fibrosis in rat model

BACKGROUND

Liver fibrosis is a common pathological change caused by a variety of etiologies. Early diagnosis and timely treatment can reverse or delay disease progression and improve the prognosis. This study aimed to assess the potential utility of two-dimensional shear wave elastography and texture analysis in dynamic monitoring of the progression of liver fibrosis in rat model.

METHODS

Twenty rats were divided into control group (n = 4) and experimental groups (n = 4 per group) with carbon tetrachloride administration for 2, 3, 4, and 6 weeks. The liver stiffness measurement was performed by two-dimensional shear wave elastography, while the optimal texture analysis subsets to distinguish fibrosis stage were generated by MaZda. The results of elastography and texture analysis were validated through comparing with histopathology.

RESULTS

Liver stiffness measurement was 6.09 ± 0.31 kPa in the control group and 7.10 ± 0.41 kPa, 7.80 ± 0.93 kPa, 8.64 ± 0.93 kPa, 9.91 ± 1.13 kPa in the carbon tetrachloride induced groups for 2, 3, 4, 6 weeks, respectively (P < 0.05). By texture analysis, histogram and co-occurrence matrix had the most frequency texture parameters in staging liver fibrosis. Receiver operating characteristic curve of liver elasticity showed that the sensitivity and specificity were 95.0% and 92.5% to discriminate liver fibrosis and non-fibrosis, respectively. In texture analysis, five optimal parameters were selected to classify liver fibrosis and non-fibrosis.

CONCLUSIONS

Two-dimensional shear wave elastography showed potential applications for noninvasive monitoring of the progression of hepatic fibrosis, even in mild fibrosis. Texture analysis can further extract and quantify the texture features in ultrasonic image, which was a supplementary to further visual information and acquired high diagnostic accuracy for severe fibrosis.

Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions

Purpose

To compare the performance of histogram analysis and intra-perinodular textural transition (Ipris) for distinguishing between benign and malignant testicular lesions.

Patients and Methods

This retrospective study included 76 patients with 80 pathologically confirmed testicular lesions (55 malignant, 25 benign). All patients underwent preoperative T2-weighted imaging (T2WI) on a 3.0T MR scanner. All testicular lesions were manually segmented on axial T2WI, and histogram and Ipris features were extracted. Thirty enrolled patients were randomly selected to estimate the robustness of the features. We used intraclass correlation coefficients (ICCs) to evaluate intra- and interobserver agreement of features, independent t-test or Mann–Whitney U-test to compare features between benign and malignant lesions, and receiver operating characteristic curve analysis to evaluate the diagnostic performance of features.

Results

Eighteen histogram features and forty-eight Ipris features were extracted from T2WI of each lesion. Most (60/66) histogram and Ipris features had good robustness (ICC of both intra- and interobserver variabilities >0.6). Three histogram and nine Ipris features were significantly different between the benign and malignant groups. The area under the curve values for Energy, TotalEnergy, and Ipris_shell1_id_std were 0.807, 0.808, and 0.708, respectively, which were relatively higher than those of other features.

Conclusion

Ipris features may be useful for identifying benign and malignant testicular tumors but have no significant advantage over conventional histogram features.

Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis

BACKGROUND

Liver fibrosis (LF) continues to develop and eventually progresses to cirrhosis. However, LF and early-stage cirrhosis (ESC) can be reversed in some cases, while advanced cirrhosis is almost impossible to cure. Advances in quantitative imaging techniques have made it possible to replace the gold standard biopsy method with non-invasive imaging, such as radiomics. Therefore, the purpose of this study is to develop a radiomics model to identify LF and ESC.

METHODS

Patients with LF (n = 108) and ESC (n = 116) were enrolled in this study. As a control, patients with healthy livers were involved in the study (n = 145). Diffusion-weighted imaging (DWI) data sets with three b-values (0, 400, and 800 s/mm) of enrolled cases were collected in this study. Then, radiomics features were extracted from manually delineated volumes of interest. Two modeling strategies were performed after univariate analysis and feature selection. Finally, an optimal model was determined by the receiver operating characteristic area under the curve (AUC).

RESULTS

The optimal models were built in plan 1. For model 1 in plan 1, the AUCs of the training and validation cohorts were 0.973 (95% confidence interval [CI] 0.946-1.000) and 0.948 (95% CI 0.903-0.993), respectively. For model 2 in plan 1, the AUCs of the training and validation cohorts were 0.944, 95% CI 0.905 to 0.983, and 0.968, 95% CI 0.940 to 0.996, respectively.

CONCLUSIONS

Radiomics analysis of DWI images allows for accurate identification of LF and ESC, and the non-invasive biomarkers extracted from the functional DWI images can serve as a better alternative to biopsy.

Diffusion-weighted imaging and texture analysis: current role for diffuse liver disease

Multiparametric MRI represents the primary imaging modality to assess diffuse liver disease, both in a qualitative and in a quantitative manner. Diffusion-weighted imaging (DWI) is among the imaging techniques that can be used to assess fibrosis due to its unique capability to assess microstructural changes at the tissue level. DWI is based on water mobility patterns and has the potential to become a non-invasive and non-destructive virtual biopsy to assess diffuse liver disease, overcoming sampling bias errors due to its three-dimensional imaging capabilities. Parallel to DWI, another quantitative method called texture analysis may be used to assess early and advanced diffused liver disease through quantifying spatial relationships in a global and local level, applying to any type of digital imaging technique like MRI or CT. Initial results using texture analysis hold great promise. In the current paper, we will review the role of DWI and texture analysis using MR images in assessing diffuse liver disease.

CT Image-Based Texture Analysis to Predict Microvascular Invasion in Primary Hepatocellular Carcinoma

The objective of this study was to determine the clinical value of computed tomography (CT) image-based texture analysis in predicting microvascular invasion of primary hepatocellular carcinoma (HCC). CT images of patients with HCC from May 2017 to May 2019 confirmed by surgery and histopathology were retrospectively analyzed. Image features including tumor margin, tumor capsule, peritumoral enhancement, hypoattenuating halo, intratumoral arteries, and tumor-liver differences were assessed. All patients were divided into microvascular invasion (MVI)-negative group (n = 34) and MVI-positive group (n = 68). Preoperative CT images were further imported into MaZda software, where the regions of interest of the lesions were manually delineated. Texture features of lesions based on pre-contrast, arterial, portal, and equilibrium phase CT images were extracted. Thirty optimal texture parameters were selected from each phase by Fisher's coefficient (Fisher), classification error probability combined with average correlation coefficient (POE+ACC), and mutual information (MI). Finally, receiver operating characteristic curve analysis was performed. The results showed that the Edmonson-Steiner grades, tumor size, tumor margin, and intratumoral artery characteristics were significantly different between the two groups (P = 0.012, < 0.001, < 0.001, = 0.003, respectively). There were 58 parameters with significant differences between the MVI-negative and MVI-positive groups (P < 0.001 for all). Among them, 12, 14, 17, and 15 parameters were derived from the pre-contrast phase, arterial phase, portal phase, and equilibrium phase respectively. According to the ROC analysis, optimal texture parameters based on the pre-contrast, arterial, portal, and equilibrium phases were 135dr_GLevNonU (AUC, 0.766; the cutoff value, 1055.00), Vertl_RLNonUni (AUC, 0.764; the cutoff value, 5974.38), 45dgr_GLevNonU (AUC, 0.762; the cutoff value, 924.34), and Vertl_RLNonUni (AUC, 0.754; the cutoff value, 4868.80), respectively. Texture analysis of preoperative CT images may be used as a non-invasive method to predict microvascular invasion in patients with primary hepatocellular carcinomas, and further to guide the treatment and evaluate prognosis. The most valuable parameters were derived from the gray-level run-length matrix.

MR Imaging Texture Analysis in the Abdomen and Pelvis

Add "which is a" before "distribution"? Texture analysis (TA) is a form of radiomics that refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MR images of the abdomen and pelvis, with the main strength quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MRTA. Despite these limitations, there is a growing body of literature supporting MRTA. This review discusses application of MRTA to the abdomen and pelvis.

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.

Clinical and Preclinical Imaging of Hepatosplenic Schistosomiasis

Schistosomiasis, a neglected tropical disease, is a major cause of chronic morbidity and disability, and premature death. The hepatosplenic form of schistosomiasis is characterized by hepatosplenomegaly, liver fibrosis, portal hypertension, and esophageal varices, whose rupture may cause bleeding and death. We review currently available abdominal imaging modalities and describe their basic principles, strengths, weaknesses, and usefulness in the assessment of hepatosplenic schistosomiasis (HSS). Advanced imaging methods are presented that could be of interest for hepatosplenic schistosomiasis evaluation by yielding morphological, functional, and molecular parameters of disease progression. We also provide a comprehensive view of preclinical imaging studies and current research objectives such as parasite visualization in hosts, follow-up of the host's immune response, and development of noninvasive quantitative methods for liver fibrosis assessment.

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.

Two-dimensional Texture Analysis Based on CT Images to Differentiate Pancreatic Lymphoma and Pancreatic Adenocarcinoma: A Preliminary Study

OBJECTIVE

To retrospectively assess the diagnostic performance of texture analysis and characteristics of CT images for the discrimination of pancreatic lymphoma (PL) from pancreatic adenocarcinoma (PA).

METHODS

Fifteen patients with pathologically proved PL were compared with 30 age-matched controls with PA in a 1:2 ratio. Patients underwent a CT scan with three phases including the precontrast phase, the arterial phase, and the portal vein phase. The regions of interest of PA and PL were drawn and analyzed to derive texture parameters with MaZda software. Texture features and CT characteristics were selected for the discrimination of PA and PL by the least absolute shrinkage and selection operator and logistic regression analysis. Receiver operating characteristic analysis was performed to assess the diagnostic performance of texture analysis and characteristics of CT images.

RESULTS

Sixty texture features were obtained by MaZda. Of these, four texture features were selected by least absolute shrinkage and selection operator. Following this, three texture features and nine CT characteristics were excluded by logistic regression analysis. Finally, "S(5, -5)SumAverg" (texture feature) and "Size" (CT characteristic) were selected for the receiver operating characteristic analysis. The AUC of "S(5, -5)SumAverg" and "Size" were to be 0.704 and 0.821, respectively, with no significance between them (p = 0.3064).

CONCLUSION

Two-dimensional texture analysis is a quantitative method for differential diagnosis of PL from PA. The diagnostic performance of both texture analysis and CT characteristics was similar.

Texture analysis of multi-phase MRI images to detect expression of Ki67 in hepatocellular carcinoma

AIM

To determine whether texture analysis of preoperative magnetic resonance imaging (MRI) images could be used to detect Ki67 expression, a widely used cell proliferation marker in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

In total, 83 patients were included, 25 with low Ki67 (Ki67 ≤10%) HCC expression and 58 with high Ki67 (Ki67 ≥10%) HCC expression as demonstrated by retrospective surgical evaluation. All patients were examined using a 3 T MRI unit with one standard protocol. The region of interest was drawn manually by one radiologist. Texture analysis included histogram, co-occurrence matrix, run-length matrix, gradient, auto-regressive model, and wavelet transform features as calculated by MaZda (version 4.6; quantitative texture analysis software). The features reduced by the Fisher, probability of classification error, and average correlation coefficient (POE+ACC), mutual information were used to select the features that predicted Ki67 proliferation status with highest accuracy and then using the B11 program for data analysis and classification.

RESULTS

The misclassification rate of the principal component analysis (PCA) in the hepatobiliary phase (HBP), T2-weighted imaging (T2WI), arterial phase (AP), and portal vein phase (PVP) was 36/83 (43.37%), 35/82 (42.68%), 40/83 (48.19%), and 34/83 (40.96%), respectively. The misclassification of the linear discriminant analysis in HBP, T2WI, AP, and PVP phase was 13/83 (15.66%), 21/82 (25.61%), 9/83 (10.84%), and 8/83 (9.64%), respectively. The misclassification of the nonlinear discriminant analysis in HBP, T2WI, AP, and PVP phase was 7/83 (8.43%), 6/82 (7.32%), 5/83 (6.02%), and 7/83 (8.43%), respectively.

CONCLUSIONS

Texture analysis of HBP, AP, and PVP were helpful for predicting Ki67 expression and may provide a less-invasive method to investigate critical histopathology markers for HCC.

Diagnostic value of MR-based texture analysis for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD)

PURPOSE

To investigate the performance of MR-based texture analysis (TA) for the assessment of hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD).

METHODS

Fifty-four adult patients (33 females, 21 males, mean age 49.8 ± 13.5 years) with biopsy-proven NAFLD were enrolled and underwent MR imaging on a 1.5 T system. TA parameters were extracted on axial noncontrast 3D-GRE T1W images (slice thickness = 4.6 mm) using a commercially available research software (TexRAD). Receiver operating curves (ROC), areas under the ROC (AUROC) and 95% confidence intervals (CI) were calculated to assess the accuracy of each TA parameter for the diagnosis of significant (F ≥ 2) and advanced fibrosis (F ≥ 3). The correlation between TA and histopathological features of nonalcoholic steatohepatitis (NASH) was tested calculating the Spearman's rank correlation coefficient (ρ).

RESULTS

Thirty-seven (68%) subjects had significant fibrosis and 20 (37%) had advanced fibrosis. The TA parameters with the best performance were standard deviation (SD) and entropy, respectively, with AUROC 0.755 (95% CI 0.619-0.862, p ≤ 0.0002) and 0.769 (95% CI 0.634-0.873, p < 0.0001) for significant fibrosis and AUROC 0.746 (95% CI 0.609-0.854, p ≤ 0.0004) and 0.754 (95% CI 0.618-0.861, p ≤ 0.0002) for advanced fibrosis. SD and entropy demonstrated a moderate correlation with the degree of fibrosis (ρ = 0.457 and 0.480; p < 0.01). No significant correlation was found between TA parameters and other histopathological features of NASH.

CONCLUSIONS

Entropy and SD extracted on T1-weighted MR images have fair accuracy for the diagnosis of significant and advanced hepatic fibrosis in patients with NAFLD.

Liver Fibrosis Conventional and Molecular Imaging Diagnosis Update

Liver fibrosis is a serious, life-threatening disease with high morbidity and mortality that result from diverse causes. Liver biopsy, considered the "gold standard" to diagnose, grade, and stage liver fibrosis, has limitations in terms of invasiveness, cost, sampling variability, inter-observer variability, and the dynamic process of fibrosis. Compelling evidence has demonstrated that all stages of fibrosis are reversible if the injury is removed. There is a clear need for safe, effective, and reliable non-invasive assessment modalities to determine liver fibrosis in order to manage it precisely in personalized medicine. However, conventional imaging methods used to assess morphological and structural changes related to liver fibrosis, including ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI), are only useful in assessing advanced liver disease, including cirrhosis. Functional imaging techniques, including MR elastography (MRE), US elastography, and CT perfusion are useful for assessing moderate to advanced liver fibrosis. MRE is considered the most accurate noninvasive imaging technique, and US elastography is currently the most widely used noninvasive means. However, these modalities are less accurate in early-stage liver fibrosis and some factors affect the accuracy of these techniques. Molecular imaging is a target-specific imaging mechanism that has the potential to accurately diagnose early-stage liver fibrosis. We provide an overview of recent advances in molecular imaging for the diagnosis and staging of liver fibrosis which will enable clinicians to monitor the progression of disease and potentially reverse liver fibrosis. We compare the promising technologies with conventional and functional imaging and assess the utility of molecular imaging in precision and personalized clinical medicine in the early stages of liver fibrosis.

Radiomics Analysis of Gadoxetic Acid-enhanced MRI for Staging Liver Fibrosis

Purpose To develop and validate a radiomics-based model for staging liver fibrosis by using gadoxetic acid-enhanced hepatobiliary phase MRI. Materials and Methods In this retrospective study, 436 patients (mean age, 51 years; age range, 18-86 years; 319 men [mean age, 51 years; age range, 18-86 years]; 117 women [mean age, 50 years; age range, 18-79 years]) with pathologic analysis-proven liver fibrosis who underwent gadoxetic acid-enhanced MRI from June 2015 to December 2016 were randomized in a three-to-one ratio into development (n = 329) and test (n = 107) cohorts, respectively. In the development cohort, a model was developed to calculate radiomics fibrosis index (RFI) by using logistic regression with elastic net regularization to differentiate stage F3-F4 from stage F0-F2. Optimal RFI cutoffs to diagnose clinically significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4), and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. In the test cohort, the diagnostic performance of RFI was compared with that of normalized liver enhancement, aspartate transaminase-to-platelet ratio index (APRI), and fibrosis-4 index by using the Obuchowski index. Results In the test cohort, RFI (Obuchowski index, 0.86) significantly outperformed normalized liver enhancement (Obuchowski index, 0.77; P < .03), APRI (Obuchowski index, 0.60; P < .001), and fibrosis-4 index (Obuchowski index, 0.62; P < .001) for staging liver fibrosis. By using the cutoffs, RFI had sensitivities and specificities as follows: 81% (95% confidence interval: 71%, 89%) and 78% (95% confidence interval: 63%, 89%) for diagnosing stage F2-F4, respectively; 79% (95% confidence interval: 67%, 88%) and 82% (95% confidence interval: 69%, 91%), respectively, for diagnosing stage F3-F4; and 92% (95% confidence interval: 79%, 98%) and 75% (95% confidence interval: 62%, 83%), respectively, for diagnosing stage F4. Conclusion Radiomics analysis of gadoxetic acid-enhanced hepatobiliary phase images allows for accurate diagnosis of liver fibrosis. © RSNA, 2018 Online supplemental material is available for this article.

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.

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.

A comprehensive literatures update of clinical researches of superparamagnetic resonance iron oxide nanoparticles for magnetic resonance imaging

This paper aims to update the clinical researches using superparamagnetic iron oxide (SPIO) nanoparticles as magnetic resonance imaging (MRI) contrast agent published during the past five years. PubMed database was used for literature search, and the search terms were (SPIO OR superparamagnetic iron oxide OR Resovist OR Ferumoxytol OR Ferumoxtran-10) AND (MRI OR magnetic resonance imaging). The literature search results show clinical research on SPIO remains robust, particularly fuelled by the approval of ferumoxytol for intravenously administration. SPIOs have been tested on MR angiography, sentinel lymph node detection, lymph node metastasis evaluation; inflammation evaluation; blood volume measurement; as well as liver imaging. Two experimental SPIOs with unique potentials are also discussed in this review. A curcumin-conjugated SPIO can penetrate brain blood barrier (BBB) and bind to amyloid plaques in Alzheime's disease transgenic mice brain, and thereafter detectable by MRI. Another SPIO was fabricated with a core of Fe3O4 nanoparticle and a shell coating of concentrated hydrophilic polymer brushes and are almost not taken by peripheral macrophages as well as by mononuclear phagocytes and reticuloendothelial system (RES) due to the suppression of non-specific protein binding caused by their stealthy ''brush-afforded'' structure. This SPIO may offer potentials for the applications such as drug targeting and tissue or organ imaging other than liver and lymph nodes.

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.

Intraoral radiographs texture analysis for dental implant planning

BACKGROUND AND OBJECTIVES

Computer vision extracts features or attributes from images improving diagnosis accuracy and aiding in clinical decisions. This study aims to investigate the feasibility of using texture analysis of periapical radiograph images as a tool for dental implant treatment planning.

METHODS

Periapical radiograph images of 127 jawbone sites were obtained before and after implant placement. From the superimposition of the pre- and post-implant images, four regions of interest (ROI) were delineated on the pre-implant images for each implant site: mesial, distal and apical peri-implant areas and a central area. Each ROI was analysed using Matlab® software and seven image attributes were extracted: mean grey level (MGL), standard deviation of grey levels (SDGL), coefficient of variation (CV), entropy (En), contrast, correlation (Cor) and angular second moment (ASM). Images were grouped by bone types-Lekholm and Zarb classification (1,2,3,4). Peak insertion torque (PIT) and resonance frequency analysis (RFA) were recorded during implant placement. Differences among groups were tested for each image attribute. Agreement between measurements of the peri-implant ROIs and overall ROI (peri-implant + central area) was tested, as well as the association between primary stability measures (PIT and RFA) and texture attributes.

RESULTS

Differences among bone type groups were found for MGL (p = 0.035), SDGL (p = 0.024), CV (p < 0.001) and En (p < 0.001). The apical ROI showed a significant difference from the other regions for all attributes, except Cor. Concordance correlation coefficients were all almost perfect (ρ > 0.93), except for ASM (ρ = 0.62). Texture attributes were significantly associated with the implant stability measures.

CONCLUSION

Texture analysis of periapical radiographs may be a reliable non-invasive quantitative method for the assessment of jawbone and prediction of implant stability, with potential clinical applications.

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.

Non-invasive diagnosis of liver fibrosis and cirrhosis

The evaluation and follow up of liver fibrosis and cirrhosis have been traditionally performed by liver biopsy. However, during the last 20 years, it has become evident that this “gold-standard” is imperfect; even according to its proponents, it is only “the best” among available methods. Attempts at uncovering non-invasive diagnostic tools have yielded multiple scores, formulae, and imaging modalities. All are better tolerated, safer, more acceptable to the patient, and can be repeated essentially as often as required. Most are much less expensive than liver biopsy. Consequently, their use is growing, and in some countries the number of biopsies performed, at least for routine evaluation of hepatitis B and C, has declined sharply. However, the accuracy and diagnostic value of most, if not all, of these methods remains controversial. In this review for the practicing physician, we analyze established and novel biomarkers and physical techniques. We may be witnessing in recent years the beginning of the end of the first phase for the development of non-invasive markers. Early evidence suggests that they might be at least as good as liver biopsy. Novel experimental markers and imaging techniques could produce a dramatic change in diagnosis in the near future.

Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images

RATIONALE AND OBJECTIVES

To retrospectively evaluate the diagnostic performance of texture analysis (TA) for the discrimination of angiomyolipoma (AML) with minimal fat, clear cell renal cell cancer (ccRCC), and papillary renal cell cancer (pRCC) on computed tomography (CT) images and to determine the scanning phase, which contains the strongest discriminative power.

MATERIALS AND METHODS

Patients with pathologically proved AMLs (n = 18) lacking visible macroscopic fat at CT and patients with pathologically proved ccRCCs (n = 18) and pRCCs (n = 14) were included. All patients underwent CT scan with three phases (precontrast phase [PCP], corticomedullary phase [CMP], and nephrographic phase [NP]). The selected images were analyzed and classified with TA software (MaZda). Texture classification was performed for 1) minimal fat AML versus ccRCC, 2) minimal fat AML versus pRCC, and 3) ccRCC versus pRCC. The classification results were arbitrarily divided into several levels according to the misclassification rates: excellent (misclassification rates ≤10%), good (10%< misclassification rates ≤20%), moderate (20%< misclassification rates ≤30%), fair (30%< misclassification rates ≤40%), and poor (misclassification rates ≥40%).

RESULTS

Excellent classification results (error of 0.00%-9.30%) were obtained with nonlinear discriminant analysis for all the three groups, no matter which phase was used. On comparison of the three scanning phases, we observed a trend toward better lesion classification with PCP for minimal fat AML versus ccRCC, CMP, and NP images for ccRCC versus pRCC and found similar discriminative power for minimal fat AML versus pRCC.

CONCLUSIONS

TA might be a reliable quantitative method for the discrimination of minimal fat AML, ccRCC, and pRCC.

Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T

Purpose. To noninvasively assess liver fibrosis using combined-contrast-enhanced (CCE) magnetic resonance imaging (MRI) and texture analysis. Materials and Methods. In this IRB-approved, HIPAA-compliant prospective study, 46 adults with newly diagnosed HCV infection and recent liver biopsy underwent CCE liver MRI following intravenous administration of superparamagnetic iron oxides (ferumoxides) and gadolinium DTPA (gadopentetate dimeglumine). The image texture of the liver was quantified in regions-of-interest by calculating 165 texture features. Liver biopsy specimens were stained with Masson trichrome and assessed qualitatively (METAVIR fibrosis score) and quantitatively (% collagen stained area). Using L₁ regularization path algorithm, two texture-based multivariate linear models were constructed, one for quantitative and the other for quantitative histology prediction. The prediction performance of each model was assessed using receiver operating characteristics (ROC) and correlation analyses. Results. The texture-based predicted fibrosis score significantly correlated with qualitative (r = 0.698, P < 0.001) and quantitative (r = 0.757, P < 0.001) histology. The prediction model for qualitative histology had 0.814–0.976 areas under the curve (AUC), 0.659–1.000 sensitivity, 0.778–0.930 specificity, and 0.674–0.935 accuracy, depending on the binary classification threshold. The prediction model for quantitative histology had 0.742–0.950 AUC, 0.688–1.000 sensitivity, 0.679–0.857 specificity, and 0.696–0.848 accuracy, depending on the binary classification threshold. Conclusion. CCE MRI and texture analysis may permit noninvasive assessment of liver fibrosis.

Intrahepatic portal vein blood volume estimated by non-contrast magnetic resonance imaging for the assessment of portal hypertension

PURPOSE

To investigate the feasibility of estimating the portal vein blood volume that flows into the intrahepatic volume (IHPVBV) in each cardiac cycle using non-contrast MR venography technique as a surrogate marker of portal hypertension (PH).

MATERIALS AND METHODS

Ten patients with chronic liver disease and clinical symptoms of PH (40% males, median age: 54.0, range: 44-73 years old) and ten healthy volunteers (80% males, median age: 54.0, range: 44-66 years old) were included in this study. A non-contrast Triple-Inversion-Recovery Arterial-Spin-Labeling (TIR-ASL) technique was used to quantify the IHPVBV in one and two cardiac cycles. Liver (LV) and spleen volumes (SV) were measured by manual segmentation from anatomical MR images as morphological markers of PH. All images were acquired in a 1.5T Philips Achieva MR scanner.

RESULTS

PH patients had larger SV (P=0.02) and lower liver-to-spleen ratio (P=0.02) compared with healthy volunteers. The median IHPVBV in healthy volunteers was 13.5cm(3) and 26.5cm(3) for one and two cardiac cycles respectively, whereas in PH patients a median volume of 3.1cm(3) and 9.0cm(3) was observed. When correcting by LV, the IHPVBV was significantly higher in healthy volunteers than PH patients for one and two cardiac cycles. The combination of morphological information (liver-to-spleen ratio) and functional information (IHPVBV/LV) can accurately identify the PH patients with a sensitivity of 90% and specificity of 100%.

CONCLUSION

Results show that the portal vein blood volume that flows into the intrahepatic volume in one and two cardiac cycles is significantly lower in PH patients than in healthy volunteers and can be quantified with non-contrast MRI techniques.

Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI

PURPOSE

To develop a classification model using texture features and support vector machine in contrast-enhanced T1-weighted images to differentiate between brain metastasis and radiation necrosis.

METHODS

Texture features were extracted from 115 lesions: 32 of them previously diagnosed as radiation necrosis, 23 as radiation-treated metastasis and 60 untreated metastases; including a total of 179 features derived from six texture analysis methods. A feature selection technique based on support vector machine was used to obtain a subset of features that provide optimal performance.

RESULTS

The highest classification accuracy evaluated over test sets was achieved with a subset of ten features when the untreated metastases were not considered; and with a subset of seven features when the classifier was trained with untreated metastases and tested on treated ones. Receiver operating characteristic curves provided area-under-the-curve (mean ± standard deviation) of 0.94 ± 0.07 in the first case, and 0.93 ± 0.02 in the second.

CONCLUSION

High classification accuracy (AUC > 0.9) was obtained using texture features and a support vector machine classifier in an approach based on conventional MRI to differentiate between brain metastasis and radiation necrosis.

Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma

OBJECTIVE

The purpose of this article is to evaluate differences in texture measures on apparent diffusion coefficient (ADC) maps between low- and high-stage clear cell renal cell carcinomas (RCCs).

MATERIALS AND METHODS

In this retrospective study, 61 patients with clear cell RCC at pathologic examination and who underwent preoperative MRI with diffusion-weighted imaging were included. Clear cell RCCs were clinically staged on review of preoperative MRI by a board-certified radiologist blinded to the pathologic findings. Whole lesions were segmented on ADC maps by two readers independently, from which first-order texture features (i.e., mean and skewness) and second-order texture features (i.e., cooccurrence matrix measures) were calculated. Texture metrics were compared between low- and high-stage clear cell RCC.

RESULTS

In 61 patients, there were 62 clear cell RCCs (33 low stage [stages I and II] and 29 high stage [stages III and IV]) at pathologic examination. Staging accuracy of qualitative interpretation was 100% for low-stage lesions and 37.9% (11/29) for high-stage lesions. There was no statistically significant difference in mean ADC between high- and low-stage clear cell RCCs (1.77×10(-3) vs 1.80×10(-3) mm2/s; p=0.7). However, high-stage clear cell RCCs were larger (6.96±2.93 vs 3.49±1.57 cm; p<0.0001) and had statistically significantly (p≤0.0001) higher ADC skewness (0.02±0.33 vs -0.52±0.65) and cooccurrence matrix correlation (0.64±0.11 vs 0.49±0.13). Multivariate logistic regression identified size, skewness, and cooccurrence matrix correlation as significant independent predictors of high stage (AUC=0.92). Interreader correlation in texture metrics ranged from 0.82 to 0.89.

CONCLUSION

First- and second-order ADC texture metrics differ between low- and high-stage clear cell RCCs. A model that includes size and ADC texture measures may help to stage clear cell RCCs noninvasively.

[Image postprocessing part 2: algorithms and workflow]

Apart from various display options, a large variety of complex algorithms to analyze radiological datasets have become available by means of advanced visualization. Basic knowledge of common concepts and properties allows the physician to choose and employ these algorithms more efficiently and successfully. In addition to functionality alone, the seamless integration of these methods into the radiological workflow is of special importance. Different scenarios can be implemented to achieve this integration. Detailed information on the individual goals of an installation and on the existing infrastructure represent prerequisites for successful integration.

Customized first and second order statistics based operators to support advanced texture analysis of MRI images

Texture analysis is the process of highlighting key characteristics thus providing an exhaustive and unambiguous mathematical description of any object represented in a digital image. Each characteristic is connected to a specific property of the object. In some cases the mentioned properties represent aspects visually perceptible which can be detected by developing operators based on Computer Vision techniques. In other cases these properties are not visually perceptible and their computation is obtained by developing operators based on Image Understanding approaches. Pixels composing high quality medical images can be considered the result of a stochastic process since they represent morphological or physiological processes. Empirical observations have shown that these images have visually perceptible and hidden significant aspects. For these reasons, the operators can be developed by means of a statistical approach. In this paper we present a set of customized first and second order statistics based operators to perform advanced texture analysis of Magnetic Resonance Imaging (MRI) images. In particular, we specify the main rules defining the role of an operator and its relationship with other operators. Extensive experiments carried out on a wide dataset of MRI images of different body regions demonstrating usefulness and accuracy of the proposed approach are also reported.