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

Intrahepatic cholangiocarcinoma: how do hepatectomy outcomes compare to liver metastases and hepatocellular carcinoma?

BACKGROUND

The incidence of intrahepatic cholangiocarcinoma (ICC) continues to rise, and hepatectomy is the only cure. Perioperative outcomes following hepatectomy for colorectal liver metastases (CRLM) and hepatocellular carcinoma (HCC) are better described than for ICC. The aim was to compare post-hepatectomy outcomes for ICC to CRLM and HCC.

METHODS

The 2014-2020 ACS NSQIP hepatectomy PUF was utilized. Patients with ICC, CRLM, and HCC were identified and others excluded. Demographic, disease, and procedural characteristics were collected. Univariable and multivariable analyses (Chi-Square for categorical variables; Kruskal-Wallis for continuous variables) were performed for mortality, serious morbidity, bile leak, post-hepatectomy liver failure (PHLF), and 30-day readmission.

RESULTS

17,789 patients underwent hepatectomy including 2377 for ICC, 10,195 for CRLM, and 5217 for HCC. Patients undergoing hepatectomy for ICC vs. HCC vs. CRLM were noted to have higher 30-day mortality (4.8% vs. 2.5% vs. 1.0%, respectively p < 0.05). ICC was associated with higher overall and serious morbidity, bile leak, severe PHLF, and readmission. Multivariable analyses confirmed higher odds ratios for mortality and morbidity (p < 0.05) in patients with ICC.

CONCLUSION

Hepatectomy for ICC is associated with worse short-term outcomes than for CRLM or HCC. Surgeons should be aware of these risks during surgical planning.

Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis

PURPOSE

Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis.

MATERIALS AND METHODS

Thirty-three rabbits were randomly divided into 27 carbon tetrachloride-induced liver fibrosis group and 6 control group. Spectral CT contrast-enhanced scan was performed in batches, and the liver fibrosis was staged according to the histopathological results. The portal venous phase spectral CT parameters [70 keV CT value, normalized iodine concentration (NIC), spectral HU curve slope (λHU)] were measured, and MaZda texture analysis was performed on 70 keV monochrome images. Three dimensionality reduction methods and four statistical methods in B11 module were used to perform discriminant analysis and calculate misclassified rate (MCR), and ten texture features under the lowest combination of MCR were statistically analyzed. Receiver operating characteristic curve (ROC) was used to calculate the diagnostic performance of spectral parameters and texture features for significant liver fibrosis. Finally, the binary logistic regression was used to further screen independent predictors and establish model.

RESULTS

A total of 23 experimental rabbits and 6 control rabbits were included, of which 16 had significant liver fibrosis. Three spectral CT parameters with significant liver fibrosis were significantly lower than those of non-significant liver fibrosis (p < 0.05), and the AUC ranged from 0.846 to 0.913. The combination analysis of mutual information (MI) and nonlinear discriminant analysis (NDA) had the lowest MCR, which with 0%. In the filtered texture features, four were statistically significant and AUC > 0.5, ranges from 0.764 to 0.875. The logistic regression model showed that Perc.90% and NIC could be used as independent predictors, the overall prediction accuracy of the model was 89.7% and the AUC was 0.976.

CONCLUSION

Spectral CT parameters and texture features have high diagnostic value for predicting significant liver fibrosis in rabbits, and the combination of the two can improve its diagnostic efficiency.

Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging

Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.

Updates on Quantitative MRI of Diffuse Liver Disease: A Narrative Review

Diffuse liver diseases are highly prevalent conditions around the world, including pathological liver changes that occur when hepatocytes are damaged and liver function declines, often leading to a chronic condition. In the last years, Magnetic Resonance Imaging (MRI) is reaching an important role in the study of diffuse liver diseases moving from qualitative to quantitative assessment of liver parenchyma. In fact, this can allow noninvasive accurate and standardized assessment of diffuse liver diseases and can represent a concrete alternative to biopsy which represents the current reference standard. MRI approach already tested for other pathologies include diffusion-weighted imaging (DWI) and radiomics, able to quantify different aspects of diffuse liver disease. New emerging MRI quantitative methods include MR elastography (MRE) for the quantification of the hepatic stiffness in cirrhotic patients, dedicated gradient multiecho sequences for the assessment of hepatic fat storage, and iron overload. Thus, the aim of this review is to give an overview of the technical principles and clinical application of new quantitative MRI techniques for the evaluation of diffuse liver disease.

The utility of texture analysis of kidney MRI for evaluating renal dysfunction with multiclass classification model

We evaluated a multiclass classification model to predict estimated glomerular filtration rate (eGFR) groups in chronic kidney disease (CKD) patients using magnetic resonance imaging (MRI) texture analysis (TA). We identified 166 CKD patients who underwent MRI comprising Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images, apparent diffusion coefficient (ADC) maps, and T2* maps. The patients were divided into severe, moderate, and control groups based on eGFR borderlines of 30 and 60 mL/min/1.73 m². After extracting 93 texture features (TFs), dimension reduction was performed using inter-observer reproducibility analysis and sequential feature selection (SFS) algorithm. Models were created using linear discriminant analysis (LDA); support vector machine (SVM) with linear, rbf, and sigmoid kernels; decision tree (DT); and random forest (RF) classifiers, with synthetic minority oversampling technique (SMOTE). Models underwent 100-time repeat nested cross-validation. Overall performances of our classification models were modest, and TA based on T1-weighted IP/OP/WO images provided better performance than those based on ADC and T2* maps. The most favorable result was observed in the T1-weighted WO image using RF classifier and the combination model was derived from all T1-weighted images using SVM classifier with rbf kernel. Among the selected TFs, total energy and energy had weak correlations with eGFR.

Diffusion-Weighted Magnetic Resonance Imaging: Clinical Potential and Applications

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