Spleen volume-based non-invasive tool for predicting hepatic decompensation in people with compensated cirrhosis (CHESS1701)

A nomogram for predicting prognosis in patients with transjugular intrahepatic portosystemic shunt creation based on deep learning-derived spleen volume-to-platelet ratio

OBJECTIVES

The objective of our study was to develop a nomogram to predict post-transjugular intrahepatic portosystemic shunt (TIPS) survival in patients with cirrhosis based on CT images.

METHODS

This retrospective cohort study included patients who had received TIPS operation at the Wenzhou Medical University First Affiliated Hospital between November 2013 and April 2017. To predict prognosis, a nomogram and Web-based probability were developed to assess the overall survival (OS) rates at 1, 3, and 5 years based on multivariate analyses. With deep learning algorithm, the automated measurement of liver and spleen volumes can be realized. We assessed the predictive accuracy and discriminative ability of the nomogram using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

RESULTS

Age, total bilirubin, and spleen volume-to-platelet ratio (SVPR) were identified as the independent risk factors for OS. The nomogram was constructed based on the above risk factors. The C-index (0.80, 0.74, 0.70), ROC curve (area under curve: 0.828, 0.761, 0.729), calibration curve, and DCA showed that nomogram good at predictive value, stability, and clinical benefit in the prediction of 1-, 3-, 5-year OS in patients with TIPS creation.

CONCLUSIONS

We constructed a nomogram for predicting prognosis in patients with TIPS creation based on risk factors. The nomogram can help clinicians in identifying patients with poor prognosis, eventually facilitating earlier treatment and selecting suitable patients before TIPS.

ADVANCES IN KNOWLEDGE

This study developed the first nomogram based on SVPR to predict the prognosis of patients treated with TIPS. The nomogram could help clinician in non-invasive decision-making.

Prediction of late recurrence after curative-intent resection using MRI-measured spleen volume in patients with hepatocellular carcinoma and cirrhosis

BACKGROUND

Late recurrence of hepatocellular carcinoma (HCC) after liver resection is regarded as a de novo tumor primarily related to the severity of underlying liver disease. We aimed to investigate risk factors, especially spleen volume, associated with late recurrence in patients with HCC and cirrhosis.

METHODS

We retrospectively analyzed 301 patients with HCC and cirrhosis who received curative resection and preoperative MRI. Patients were followed for late recurrence for at least 2 years. Spleen volume was automatically measured on MRI with artificial intelligence techniques, and qualitative MRI imaging features reflecting tumor aggressiveness were evaluated. Uni- and multivariable Cox regression analyses were performed to identify independent predictors and a risk score was developed to predict late recurrence.

RESULTS

Eighty-four (27.9%) patients developed late recurrence during follow-up. Preoperative spleen volume was independently associated with late recurrence, and patients with a volume > 370 cm3 had significantly higher recurrence risk (hazard ratio 2.02, 95%CI 1.31-3.12, p = 0.002). Meanwhile, no qualitative imaging features were associated with late recurrence. A risk score was developed based on the APRI score, spleen volume, and tumor number, which had time-dependent area under the curve ranging from 0.700 to 0.751. The risk score at a cutoff of 0.42 allowed for the identification of two risk categories with distinct risk of late recurrence.

CONCLUSIONS

Preoperative spleen volume on MRI was independently associated with late recurrence after curative-intent resection in patients with HCC and cirrhosis. A risk score was proposed for individualized risk prediction and tailoring of postoperative surveillance strategies.

CRITICAL RELEVANCE STATEMENT

Spleen volume measured on MRI with the aid of AI techniques was independently predictive of late HCC recurrence after liver resection. A risk score based on spleen volume, APRI score, and tumor number was developed for accurate prediction of late recurrence.

KEY POINTS

• Preoperative spleen volume measured on MRI was independently associated with late recurrence after curative-intent resection in patients with HCC and cirrhosis. • Qualitative MRI features reflecting tumor aggressiveness were not associated with late recurrence. • A risk score based on spleen volume was developed for accurate prediction of late recurrence and risk stratification.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding

OBJECTIVES

To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB).

METHODS

This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA).

RESULTS

The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores.

CONCLUSION

A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice.

KEY POINTS

• The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection

BACKGROUND/AIMS

The prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB.

METHODS

This retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses.

RESULTS

During a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD.

CONCLUSION

Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.

Prognostic assessment of liver cirrhosis and its complications: current concepts and future perspectives

Liver cirrhosis is an irreversible stage of chronic liver disease with varying clinical course. Acute decompensation of liver cirrhosis represents a watershed in prognosis and is characterized by the occurrence of clinical complications such as ascites, jaundice, hepatic encephalopathy, infections, or portal-hypertensive hemorrhages. Emergent data indicate that an acute decompensation can be subdivided into stable decompensated cirrhosis (SDC), unstable decompensated cirrhosis (UDC), pre-acute-on chronic liver failure (pre-ACLF) and acute-on chronic liver failure (ACLF), while the mortality risk varies greatly between the respective subgroups. ACLF is the most severe form of acutely decompensated cirrhosis and characterized by the development of organ failure(s) and a high short-term mortality. Due to the dynamic disease course of acute decompensation, it is paramount to detect patients at particular risk for severe complications those at high risk for developing ACLF as early as possible in order to initiate optimal management. This review describes new concepts and perspectives in the definition and classification of decompensated cirrhosis and provides on overview on emerging predictive scoring systems, non-invasive measurement methods and new biomarkers, which allow an early identification of patients with acute decompensation at risk.

Validation of the albumin-bilirubin score for identifying decompensation risk in patients with compensated cirrhosis

BACKGROUND

The albumin-bilirubin (ALBI) score is an index of liver function recently developed to assess prognosis in patients with hepatocellular carcinoma (HCC). It can detect small changes in liver dysfunction and has been successfully applied to the prediction of survival in patients with non-malignant liver diseases of various etiologies.

AIM

To investigate the ALBI score for identifying decompensation risk at the 3-year follow-up in patients with compensated cirrhosis.

METHODS

One-hundred and twenty-three patients with compensated cirrhosis without HCC in King Chulalongkorn Memorial Hospital diagnosed by imaging were retrospectively enrolled from January 2016 to December 2020. A total of 113 patients (91.9%) had Child A cirrhosis with a median model for end-stage liver disease (MELD) score of less than 9. Baseline clinical and laboratory variables and decompensation events were collected. The ALBI score was calculated and validated to classify decompensation risk into low-, middle-, and high-risk groups using three ALBI grade ranges (ALBI grade 1: ≤ -2.60; grade 2: > -2.60 but ≤ -1.39; grade 3: > -1.39). Decompensation events were defined as ascites development, variceal bleeding, or grade 3 or 4 hepatic encephalopathy.

RESULTS

Among 123 cirrhotic patients enrolled, 13.8% (n = 17) developed decompensating events at a median time of 25 [95% confidence interval (CI): 17-31] mo. Median baseline ALBI score in compensated cirrhosis was significantly lower than that of patients who developed decompensation events [-2.768 (-2.956 to -2.453) vs -2.007 (-2.533 to -1.537); P = 0.01]. Analysis of decompensation risk at 3 years showed that ALBI score had a time-dependent area under the curve (tAUC) of 0.86 (95%CI: 0.78-0.92), which was significantly better than that of ALBI-Fibrosis-4 (ALBI-FIB4) score (tAUC = 0.77), MELD score (tAUC = 0.66), Child-Pugh score (tAUC = 0.65), and FIB-4 score (tAUC = 0.48) (P < 0.05 for all). The 3-year cumulative incidence of decompensation was 3.1%, 22.6%, and 50% in the low-, middle-, and high-risk groups, respectively (P < 0.001). The odds ratio for decompensation in patients of the high-risk group was 23.33 (95%CI: 3.88-140.12, P = 0.001).

CONCLUSION

The ALBI score accurately identifies decompensation risk at the 3-year follow-up in patients with compensated cirrhosis. Those cirrhotic patients with a high-risk grade of ALBI score showed a 23 times greater odds of decompensation.

Clinical applications of artificial intelligence in liver imaging

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.