A novel model based on liver/spleen volumes and portal vein diameter on MRI to predict variceal bleeding in HBV cirrhosis

Establishment and validation of a nomogram for predicting esophagogastric variceal bleeding in patients with liver cirrhosis

BACKGROUND

Patients with decompensated liver cirrhosis suffering from esophagogastric variceal bleeding (EGVB) face high mortality.

AIM

To investigate the risk factors for EGVB in patients with liver cirrhosis and establish a diagnostic nomogram.

METHODS

Patients with liver cirrhosis who met the inclusion criteria were randomly divided into training and validation cohorts in a 6:4 ratio in this retrospective research. Univariate analysis, least absolute shrinkage and selection operator regression, and multivariate analysis were employed to establish the nomogram model. Calibration curve, the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were applied to assess the discrimination, accuracy, and clinical practicability of the nomogram, respectively.

RESULTS

A total of 1115 patients were enrolled in this study. The nomogram was established based on white blood cells (P < 0.001), hemoglobin (P < 0.001), fibrinogen (P < 0.001), total bilirubin (P = 0.007), activated partial thromboplastin time (P = 0.002), total bile acid (P = 0.012), and ascites (P = 0.006). The calibration curve indicated that the actual observation results were in good agreement with the prediction results of the model. The AUC values of the diagnostic model were 0.861 and 0.859 in the training and validation cohorts, respectively, which were higher than that of the aspartate aminotransferase-to-platelet ratio index, fibrosis index based on 4 factors, and aspartate aminotransferase-to-alanine aminotransferase ratio. Additionally, DCA indicated that the net benefit value of the model was higher than that of the other models.

CONCLUSION

This research constructed and validated a nomogram with perfect performance for predicting EGVB events in patients with liver cirrhosis, which could help clinicians with timely diagnosis, individualized treatment, and follow-up.

Application of machine learning methods for predicting esophageal variceal bleeding in patients with cirrhosis

OBJECTIVE

To develop and compare machine learning models based on CT morphology features, serum biomarkers, and basic physical conditions to predict esophageal variceal bleeding.

MATERIALS AND METHODS

Two hundred twenty-four cirrhotic patients with esophageal variceal bleeding and non-bleeding were included in the retrospective study. Clinical and serum biomarkers were used in our study. In addition, the open-access segmentation model was used to generate segmentation masks of the liver and spleen. Four machine learning models based on selected features are used for building prediction models, and the diagnostic performances of models were measured using the receiver operator characteristic analysis.

RESULTS

Two hundred twenty-four cirrhosis patients with esophageal varices, including 112 patients with bleeding (mean age 52.8 ± 11.5 years, range 18-80 years) and 112 patients with non-bleeding (mean age 57.3 ± 10.5 years, range 34-85 years). The two groups showed significant differences in standardized spleen volume, fibrinogen, alanine aminotransferase, aspartate aminotransferase, D-dimer, platelet, and age. The ratio of the training set to the test set was 8:2 in our research, and the 5-fold cross-validation was used in the research. The AUCs of linear regression, random forest, support vector machine, and adaptive boosting were, respectively, 0.742, 0.854, 0.719, and 0.821 in the training set. For the test set, the AUCs of models were, respectively, 0.763, 0.818, 0.648, and 0.804.

CONCLUSIONS

Our study used CT morphological measurements, serum biomarkers, and age to build machine learning models, and the random forest and adaptive boosting had potential added value in predictive model construction.

KEY POINTS

Question Esophageal variceal bleeding is an intractable complication of liver cirrhosis. Early prediction and prevention of esophageal variceal bleeding is important for patients with liver cirrhosis. Findings It was feasible and clinically meaningful to construct machine learning models based on CT morphology features, serum biomarkers, and physical conditions to predict variceal bleeding. Clinical relevance Our study may provide a promising tool with which clinicians can conduct therapeutic decisions on fewer invasive procedures for the prediction of esophageal variceal bleeding.

Predicting Portal Pressure Gradient in Patients with Decompensated Cirrhosis: A Non-invasive Deep Learning Model

BACKGROUND

A high portal pressure gradient (PPG) is associated with an increased risk of failure to control esophagogastric variceal hemorrhage and refractory ascites in patients with decompensated cirrhosis. However, direct measurement of PPG is invasive, limiting its routine use in clinical practice. Consequently, there is an urgent need for non-invasive techniques to assess PPG.

AIM

To develop and validate a deep learning model that predicts PPG values for patients with decompensated cirrhosis and identifies those with high-risk portal hypertension (HRPH), who may benefit from early transjugular intrahepatic portosystemic shunt (TIPS) intervention.

METHODS

Data of 520 decompensated cirrhosis patients who underwent TIPS between June 2014 and December 2022 were retrospectively analyzed. Laboratory and imaging parameters were used to develop an artificial neural network model for predicting PPG, with feature selection via recursive feature elimination for comparison experiments. The best performing model was tested by external validation.

RESULTS

After excluding 92 patients, 428 were included in the final analysis. A series of comparison experiments demonstrated that a three-parameter (3P) model, which includes the international normalized ratio, portal vein diameter, and white blood cell count, achieved the highest accuracy of 87.5%. In two distinct external datasets, the model attained accuracy rates of 85.40% and 90.80%, respectively. It also showed notable ability to distinguish HRPH with an AUROC of 0.842 in external validation.

CONCLUSION

The developed 3P model could predict PPG values for decompensated cirrhosis patients and could effectively distinguish HRPH.

Comprehensive approach to esophageal variceal bleeding: From prevention to treatment

Esophageal variceal bleeding is a severe complication often associated with portal hypertension, commonly due to liver cirrhosis. Prevention and treatment of this condition are critical for patient outcomes. Preventive strategies focus on reducing portal hypertension to prevent varices from developing or enlarging. Primary prophylaxis involves the use of non-selective beta-blockers, such as propranolol or nadolol, which lower portal pressure by decreasing cardiac output and thereby reducing blood flow to the varices. Endoscopic variceal ligation (EVL) may also be employed as primary prophylaxis to prevent initial bleeding episodes. Once bleeding occurs, immediate treatment is essential. Initial management includes hemodynamic stabilization followed by pharmacological therapy with vasoactive drugs such as octreotide or terlipressin to control bleeding. Endoscopic intervention is the cornerstone of treatment, with techniques such as EVL or sclerotherapy applied to directly manage the bleeding varices. In cases where bleeding is refractory to endoscopic treatment, transjugular intrahepatic portosystemic shunt may be considered to effectively reduce portal pressure. Long-term management after an acute bleeding episode involves secondary prophylaxis using beta-blockers and repeated EVL sessions to prevent rebleeding, complemented by monitoring and managing liver function to address the underlying disease. In light of new scientific evidence, including the findings of the study by Peng et al, this editorial aims to review available strategies for the prevention and treatment of esophageal varices.

Artificial intelligence-based evaluation of prognosis in cirrhosis

Cirrhosis represents a significant global health challenge, characterized by high morbidity and mortality rates that severely impact human health. Timely and precise prognostic assessments of liver cirrhosis are crucial for improving patient outcomes and reducing mortality rates as they enable physicians to identify high-risk patients and implement early interventions. This paper features a thorough literature review on the prognostic assessment of liver cirrhosis, aiming to summarize and delineate the present status and constraints associated with the application of traditional prognostic tools in clinical settings. Among these tools, the Child-Pugh and Model for End-Stage Liver Disease (MELD) scoring systems are predominantly utilized. However, their accuracy varies significantly. These systems are generally suitable for broad assessments but lack condition-specific applicability and fail to capture the risks associated with dynamic changes in patient conditions. Future research in this field is poised for deep exploration into the integration of artificial intelligence (AI) with routine clinical and multi-omics data in patients with cirrhosis. The goal is to transition from static, unimodal assessment models to dynamic, multimodal frameworks. Such advancements will not only improve the precision of prognostic tools but also facilitate personalized medicine approaches, potentially revolutionizing clinical outcomes.

Hepatic-associated vascular morphological assessment to predict overt hepatic encephalopathy before TIPS: a multicenter study

BACKGROUND

To provide patients the chance of accepting curative transjugular intrahepatic portosystemic shunt (TIPS) rather than palliative treatments for portal hypertension-related variceal bleeding and ascites, we aimed to assess hepatic-associated vascular morphological change to improve the predictive accuracy of overt hepatic encephalopathy (HE) risks.

METHODS

In this multicenter study, 621 patients undergoing TIPS were subdivided into training (413 cases from 3 hospitals) and external validation datasets (208 cases from another 3 hospitals). In addition to traditional clinical factors, we assessed hepatic-associated vascular morphological changes using maximum diameter (including absolute and ratio values). Three predictive models (clinical, hepatic-associated vascular, and combined) were constructed using logistic regression. Their discrimination and calibration were compared to test the necessity of hepatic-associated vascular assessment and identify the optimal model. Furthermore, to verify the improved performance of ModelC-V, we compared it with four previous models, both in discrimination and calibration.

RESULTS

The combined model outperformed the clinical and hepatic-associated vascular models (training: 0.814, 0.754, 0.727; validation: 0.781, 0.679, 0.776; p < 0.050) and had the best calibration. Compared to previous models, ModelC-V showed superior performance in discrimination. The high-, middle-, and low-risk populations displayed significantly different overt HE incidence (p < 0.001). Despite the limited ability of pre-TIPS ammonia to predict overt HE risks, the combined model displayed a satisfactory ability to predict overt HE risks, both in the low- and high-ammonia subgroups.

CONCLUSION

Hepatic-associated vascular assessment improved the predictive accuracy of overt HE, ensuring curative chances by TIPS for suitable patients and providing insights for cirrhosis-related studies.

The impact of hepatic and splenic volumetric assessment in imaging for chronic liver disease: a narrative review

Chronic liver disease is responsible for significant morbidity and mortality worldwide. Abdominal computed tomography (CT) and magnetic resonance imaging (MRI) can fully visualise the liver and adjacent structures in the upper abdomen providing a reproducible assessment of the liver and biliary system and can detect features of portal hypertension. Subjective interpretation of CT and MRI in the assessment of liver parenchyma for early and advanced stages of fibrosis (pre-cirrhosis), as well as severity of portal hypertension, is limited. Quantitative and reproducible measurements of hepatic and splenic volumes have been shown to correlate with fibrosis staging, clinical outcomes, and mortality. In this review, we will explore the role of volumetric measurements in relation to diagnosis, assessment of severity and prediction of outcomes in chronic liver disease patients. We conclude that volumetric analysis of the liver and spleen can provide important information in such patients, has the potential to stratify patients' stage of hepatic fibrosis and disease severity, and can provide critical prognostic information. CRITICAL RELEVANCE STATEMENT: This review highlights the role of volumetric measurements of the liver and spleen using CT and MRI in relation to diagnosis, assessment of severity, and prediction of outcomes in chronic liver disease patients. KEY POINTS: Volumetry of the liver and spleen using CT and MRI correlates with hepatic fibrosis stages and cirrhosis. Volumetric measurements correlate with chronic liver disease outcomes. Fully automated methods for volumetry are required for implementation into routine clinical practice.

18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma

Objective

To develop and validate an ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma.

Methods

A total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA).

Results

Twenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility.

Conclusion

The ¹⁸F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01283-8.