A novel radiomics signature based on T2-weighted imaging accurately predicts hepatic inflammation in individuals with biopsy-proven nonalcoholic fatty liver disease: a derivation and independent validation study

Association between paravertebral muscle radiological parameter alterations and non-alcoholic fatty liver disease

PURPOSE

To assess changes in laboratory indices, paravertebral muscle (PVM) fat infiltration and multi b-value DWI parameters and their potential correlation with NAFLD.

METHODS

This retrospective analysis included 178 patients with histopathologically confirmed NAFLD, incluiding 76 with non-alcoholic steatohepatitis (NASH). Differences in PVM fat infiltration ratio (FIR), DWI parameters, and laboratory indices were compared between two groups. The correlation between FIR and NAFLD activity score (NAS) was also analysed. Binary logistic regression was used to identify the independent risk factors for NASH. The clinical utility of PVM fat infiltration, DWI parameters, and laboratory indices for diagnosing NASH in patients with NAFLD was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

The FIRs at the L2 and L3 levels were significantly higher in the with NASH group than those in the without NASH group. The heterogeneity index (α) and perfusion fraction (f) values at the L3 level of PVM were lower in the with NASH group. Moreover, the FIR at the L3 level was positively correlated with NAS. FIR at the L3 level was an independent risk factor for NASH along with alanine aminotransferase level. The area under the ROC curve (AUC) using L3 level PVM radiological parameters and laboratory indices for diagnosing NASH in patients with NAFLD was significantly higher than that using the degree of PVM fat infiltration, DWI parameters, or laboratory indices alone.

CONCLUSIONS

Radiological parameters of the PVM were correlated with NAFLD. An integrated curve combining PVM radiological parameters may help distinguish NASH from NAFLD, thereby offering novel insights into the diagnosis of NASH.

A Novel Score Based on Controlled Attenuation Parameter Accurately Predicts Hepatic Steatosis in Individuals With Metabolic Dysfunction Associated Steatotic Liver Disease: A Derivation and Independent Validation Study

INTRODUCTION

In metabolic dysfunction-associated steatotic liver disease, the diagnostic efficacy of controlled attenuation parameter (CAP) was not very accurate in evaluating liver fat content. The aim of this study was to develop a score, based on CAP and conventional clinical parameters, to improve the diagnostic performance of CAP regarding liver fat content.

METHODS

A total of 373 participants from 2 independent Chinese cohorts were included and divided into derivation (n = 191), internal validation (n = 75), and external validation (n = 107) cohorts. Based on the significant difference index between the 2 groups defined by the magnetic resonance imaging-proton density fat fraction (MRI-PDFF) in derivation cohort, the optimal model (CAP-BMI-AST score [CBST]) was screened by the number of parameters and the area under the receiver operating characteristic curve (AUROC). In the internal and external validation cohorts, the AUROC and corresponding 95% confidence intervals (CIs) were used to compare the diagnostic performance of CBST with that of CAP.

RESULTS

We constructed the CBST = -14.27962 + 0.05431 × CAP - 0.14266 × body mass index + 0.01715 × aspartate aminotransferase. When MRI-PDFF was ≥20%, ≥10%, and ≥5%, the AUROC for CBST was 0.77 (95% CI 0.70-0.83), 0.89 (95% CI 0.83-0.94), and 0.93 (95% CI 0.88-0.98), which was higher than that for CAP respectively. In the internal validation cohort, the AUROC for CBST was 0.80 (95% CI 0.70-0.90), 0.95 (95% CI 0.91-1.00), and 0.98 (95% CI 0.94-1.00). The optimal thresholds of CBST were -0.5345, -1.7404, and -1.9959 for detecting MRI-PDFF ≥20%, ≥10%, and ≥5%, respectively.

DISCUSSION

The CBST score can accurately evaluate liver steatosis and is superior to the CAP.

Radiomics and liver: Where we are and where we are headed?

Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.

Clinic-radiomics model using liver magnetic resonance imaging helps predict chronicity of drug-induced liver injury

BACKGROUND AND AIMS

Some drug-induced liver injury (DILI) cases may become chronic, even after drug withdrawal. Radiomics can predict liver disease progression. We established and validated a predictive model incorporating the clinical characteristics and radiomics features for predicting chronic DILI.

METHODS

One hundred sixty-eight DILI patients who underwent liver gadolinium-diethylenetriamine pentaacetate-enhanced magnetic resonance imaging were recruited. The patients were clinically diagnosed using the Roussel Uclaf causality assessment method. Patients who progressed to chronicity or recovery were randomly divided into the training (70%) and validation (30%) cohorts, respectively. Hepatic T1-weighted images were segmented to extract 1672 radiomics features. Least absolute shrinkage and selection operator regression was used for feature selection, and Rad-score was constructed using support vector machines. Multivariable logistic regression analysis was performed to build a clinic-radiomics model incorporating clinical characteristics and Rad-scores. The clinic-radiomics model was evaluated for its discrimination, calibration, and clinical usefulness in the independent validation set.

RESULTS

Of 1672 radiomics features, 28 were selected to develop the Rad-score. Cholestatic/mixed patterns and Rad-score were independent risk factors of chronic DILI. The clinic-radiomics model, including the Rad-score and injury patterns, distinguished chronic from recovered DILI patients in the training (area under the receiver operating characteristic curve [AUROC]: 0.89, 95% confidence interval [95% CI]: 0.87-0.92) and validation (AUROC: 0.88, 95% CI: 0.83-0.91) cohorts with good calibration and great clinical utility.

CONCLUSION

The clinic-radiomics model yielded sufficient accuracy for predicting chronic DILI, providing a practical and non-invasive tool for managing DILI patients.

The latest application progress of radiomics in prediction and diagnosis of liver diseases

INTRODUCTION

Early detection and individualized treatment of patients with liver disease is the key to survival. Radiomics can extract high-throughput quantitative features by multimode imaging, which has good application prospects for the diagnosis, staging and prognosis of benign and malignant liver diseases. Therefore, this paper summarizes the current research status in the field of liver disease, in order to help these patients achieve personalized and precision medical care.

AREAS COVERED

This paper uses several keywords on the PubMed database to search the references, and reviews the workflow of traditional radiomics, as well as the characteristics and influencing factors of different imaging modes. At the same time, the references on the application of imaging in different benign and malignant liver diseases were also summarized.

EXPERT OPINION

For patients with liver disease, the traditional imaging evaluation can only provide limited information. Radiomics exploits the characteristics of high-throughput and high-dimensional extraction, enabling liver imaging capabilities far beyond the scope of traditional visual image analysis. Recent studies have demonstrated the prospect of this technology in personalized diagnosis and treatment decision in various fields of the liver. However, further clinical validation is needed in its application and practice.