A Combination Model of Radiomics Features and Clinical Biomarkers as a Nomogram to Differentiate Nonadvanced From Advanced Liver Fibrosis: A Retrospective Study

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.

Suboptimal diagnostic accuracy of ultrasound and CT for compensated cirrhosis: Evidence from prospective cohort studies

INTRODUCTION

Abdominal ultrasound (US) and CT are important tools for the initial evaluation of patients with liver disease. Our study aimed to determine the accuracy of these methods for diagnosing cirrhosis.

METHODS

In all, 377 participants from 4 prospective cohort studies evaluating patients with various liver diseases were included. All patients were included between 2017 and 2022 and had undergone a liver biopsy as well as US and/or CT. Using the histological assessment as the gold standard, we calculated diagnostic accuracy for US and CT. Liver biopsies were evaluated by expert histopathologists and diagnostic scans by experienced radiologists.

RESULTS

The mean age was 54 ± 14 years and 47% were female. Most patients had NAFLD (58.3%) or alcohol-associated liver disease (25.5%). The liver biopsy showed cirrhosis in 147 patients (39.0%). Eighty-three patients with cirrhosis had Child-Pugh A (56.4% of patients with cirrhosis) and 64 had Child-Pugh B/C (43.6%). Overall, the sensitivity for diagnosing cirrhosis by US was 0.71 (95% CI 0.62-0.79) and for CT 0.74 (95% CI 0.64-0.83). The specificity was high for US (0.94, 95% CI 0.90-0.97) and for CT (0.93, 95% CI 0.83-0.98). When evaluating patients with Child-Pugh A cirrhosis, sensitivity was only 0.62 (95% CI 0.49-0.74) for US and 0.60 (95% CI 0.43-0.75) for CT. For patients with Child-Pugh B/C, sensitivity was 0.83 (95% CI 0.70-0.92) for US and 0.87 (95% CI 0.74-0.95) for CT. When limiting our analysis to NAFLD (20% with cirrhosis), the sensitivity for US was 0.45 (95% CI 0.28-0.64) and specificity was 0.97 (95% CI 0.93-0.99).

CONCLUSION

US and CT show moderate sensitivity and may potentially overlook compensated cirrhosis underlining the need for additional diagnostic testing.

Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study

OBJECTIVES

To investigate the value of R2* mapping-based radiomics nomograms in staging liver fibrosis in patients with chronic hepatitis B.

METHODS

Between January 2020 and December 2020, 151 patients with chronic hepatitis B were randomly divided into training (n = 103) and validation (n = 48) cohorts. From January to February 2021, 58 patients were included in a test cohort. Radiomics features were selected using the interclass correlation coefficient and least absolute shrinkage and selection operator method. Three radiomics nomograms, combining the radiomics score (Radscore) derived from R2* mapping and clinical variables, were used for staging significant and advanced fibrosis, and cirrhosis. Performance of the model was evaluated using the AUC. The utility and clinical benefits were evaluated using the continuous net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).

RESULTS

The Radscore calculated by 12 radiomics features and independent factors (laminin and platelet) of advanced fibrosis were used to construct the radiomics nomograms. In the test cohort, the AUCs of the radiomics nomograms for staging significant fibrosis, advanced fibrosis, and cirrhosis were 0.738 (95% confidence interval [CI]: 0.604-0.872), 0.879 (95% CI: 0.779-0.98), and 0.952 (95% CI: 0.878-1), respectively. NRI, IDI, and DCA confirmed that radiomics nomograms demonstrated varying degrees of clinical benefit and improvement for advanced fibrosis and cirrhosis, but not for significant fibrosis.

CONCLUSIONS

Radiomics nomograms combined with R2* mapping-based Radscore, laminin, and platelet have value in staging advanced fibrosis and cirrhosis but limited value for staging significant fibrosis.

KEY POINTS

• Laminin and platelets were independent predictors of advanced fibrosis. • Radiomics analysis based on R2* mapping was beneficial for evaluating advanced fibrosis and cirrhosis. • It was difficult to distinguish significant fibrosis using a radiomics nomogram, which is possibly due to the complex pathological microenvironment of chronic liver diseases.

Development of an MRI-Based Radiomics-Clinical Model to Diagnose Liver Fibrosis Secondary to Pancreaticobiliary Maljunction in Children

BACKGROUND

Preoperative diagnosis of liver fibrosis in children with pancreaticobiliary maljunction (PBM) is needed to guide clinical decision-making and improve patient prognosis.

PURPOSE

To develop and validate an MR-based radiomics-clinical nomogram for identifying liver fibrosis in children with PBM.

STUDY TYPE

Retrospective.

POPULATION

A total of 136 patients with PBM from two centers (center A: 111 patients; center B: 25 patients). Cases from center A were randomly divided into training (74 patients) and internal validation (37 patients) sets. Cases from center B were assigned to the external validation set. Liver fibrosis was determined by histopathological examination.

FIELD STRENGTH/SEQUENCE

A 3.0 T (two vendors)/T1-weighted imaging and T2-weighted imaging.

ASSESSMENT

Clinical factors associated with liver fibrosis were evaluated. A total of 3562 radiomics features were extracted from segmented liver parenchyma. Maximum relevance minimum redundancy and least absolute shrinkage and selection operator were recruited to screen radiomics features. Based on the selected variables, multivariate logistic regression was used to construct the clinical model, radiomics model, and combined model. The combined model was visualized as a nomogram to show the impact of the radiomics signature and key clinical factors on the individual risk of developing liver fibrosis.

STATISTICAL TESTS

Mann-Whitney U and chi-squared tests were used to compare clinical factors. P < 0.05 was considered statistically significant in the final models.

RESULTS

Two clinical factors and four radiomics features were selected as they were associated with liver fibrosis in the training (AUC, 0.723, 0.927), internal validation (AUC, 0.718, 0.885), and external validation (AUC, 0.737, 0.865) sets. The radiomics-clinical nomogram yielded the best performance in the training (AUC, 0.977), internal validation (AUC, 0.921), and external validation (AUC, 0.878) sets, with good calibration (P > 0.05).

DATA CONCLUSION

Our radiomic-based nomogram is a noninvasive, accurate, and preoperative diagnostic tool that is able to detect liver fibrosis in PBM children.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis

PURPOSE

To develop and validate a radiomic nomogram based on texture features from out-of-phase T1W images and clinical biomarkers in prediction of liver fibrosis.

MATERIALS AND METHODS

Patients clinically diagnosed with chronic liver fibrosis who underwent liver biopsy and noncontrast MRI were enrolled. All patients were assigned to the nonsignificant fibrosis group with fibrosis stage <2 and the significant fibrosis group with stage ≥2. Texture parameters were extracted from out-of-phase T1-weighted (T1W) images and calculated using the Artificial Intelligent Kit (AK). Boruta and LASSO regressions were used for feature selection and a multivariable logistic regression was used for construction of a combinational model integrating radiomics and clinical biomarkers. The performance of the models was assessed by using the receiver operator curve (ROC) and decision curve.

RESULTS

ROC analysis of the radiomics model that included the most discriminative features showed AUCs of the training and test groups were 0.80 and 0.78. A combinational model integrating RADscore and fibrosis 4 index was established. ROC analysis of the training and test groups showed good to excellent performance with AUC of 0.93 and 0.86. Decision curves showed the combinational model added more net benefit than radiomic and clinical models alone.

CONCLUSIONS

The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.

Effects of slice thickness on CT radiomics features and models for staging liver fibrosis caused by chronic liver disease

OBJECTIVES

To investigate the effects of slice thickness on CT radiomics features and models for staging liver fibrosis.

METHODS

A total of 108 pathologically confirmed liver fibrosis patients from a single center were retrospectively collected and divided into different groups. Both thick (5- or 7-mm) and thin slices (1.3- or 2-mm) were analyzed. A fivefold cross-validation with 100 repeats was conducted. The minimum redundancy-maximum relevance algorithm was used to reduce the radiomics features, and the top 10 ranking features were included for further analysis for each loop. The random forest was used for model establishment. The models with median AUC were selected for the assessment of the discriminative performance for both datasets. Mutual features selected by the models with AUC > 0.8 were searched and considered as the most predictive ones.

RESULTS

A total of 162 and 643 radiomics features with excellent reliability were selected from thick- and thin-slice datasets, respectively. The overall discriminative performance of the 500 AUCs from the thin-slice dataset was better than the thick slice. The median AUC values of the thick-sliced datasets were significantly lower than those of the thin-sliced datasets (0.78 and 0.90 for differentiating F1 vs. F2-4, 0.72 and 0.85 for differentiating F1-2 vs. F3-4, both P = 0.03). For differentiating F1-3 vs. F4, no significant difference was found (0.85 vs 0.94, P = 0.15). Six mutual predictive features across all the datasets were found.

CONCLUSIONS

The radiomics features extracted from thin-slice images and their corresponding models were better and more stable for staging liver fibrosis.

Liver fibrosis staging by computed tomography: Prospective randomized multicentric evaluation of image analyses

AIM

Liver fibrosis staging is essential. We prospectively evaluated the liver fibrosis staging performance of computed tomography (CT).

METHODS

70 hepato-gastroenterology clinicians were randomized into three stratified groups with different image analyses of radiological semiology, i.e., on raw images (group 1) and on expert-annotated (group 2) and computerized-morphometry-enriched (group 3) images. Radiological fibrosis staging based on seven simple descriptors into four stages equivalent to Metavir stages (F0/1, F2, F3, F4=cirrhosis) was determined at baseline and after image analyses in 10 patients with chronic liver diseases (two per F) concordant for four independent fibrosis stagings including Metavir. 23,800 CT images were analysed, providing 1400 fibrosis stagings.

RESULTS

Fibrosis staging: overall (3 groups) accuracy (correct classification rate) was, baseline: 43%, post-analysis: 60% (p < 0.001) without significant progression in group 1 (6%, p = 0.207) contrary to groups 2 (34%, p < 0.001) and 3 (13%, p = 0.007). Cirrhosis diagnosis: overall accuracy was, baseline: 84%, post-analysis: 89% (p < 0.001) without significant progression in group 1 (0%, p = 1) contrary to groups 2 (8%, p = 0.009) and 3 (7%, p = 0.015). Baseline AUROCs were good (≥0.83) for marked fibrosis (F≥3 or cirrhosis) in all groups. Post-analysis AUROCs became excellent (≥0.89) in group 2 for all diagnostic targets (≥0.98 for F≥3 and cirrhosis) and in group 3 for cirrhosis. In post-analysis group 2, discrimination between all F was excellent (especially, F1 from F0) with an Obuchowski index at 0.87. Negative and positive predictive values for marked fibrosis were 98% and 95%, respectively.

CONCLUSION

Simple CT descriptors accurately discriminate all Metavir liver fibrosis stages.