An index based on deep learning-measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis

Changes in Posttreatment Spleen Volume Associated with Immunotherapy Outcomes for Advanced Hepatocellular Carcinoma

Purpose

We investigated whether spleen volume (SV) changes were associated with treatment outcomes in advanced hepatocellular carcinoma (HCC) patients who received immunotherapy or first-line sorafenib.

Patients and Methods

Patients with advanced HCC who underwent immunotherapy or first-line sorafenib at our institute were retrospectively analyzed. CT was used to measure SV before and within 3 months of treatment initiation. Tumor assessment followed Response Evaluation Criteria in Solid Tumors version 1.1. The association between SV change and tumor response or progression-free survival (PFS) was analyzed. The inverse probability of treatment weighting (IPTW) was used to adjust for differences in baseline characteristics.

Results

The immunotherapy group comprised 143 patients (124 men, mean age, 59.8 years ± 11.2 [standard deviation]), while the sorafenib group had 57 (47 men, mean age, 59.6 years ± 9.9). SV increased in 108 (75.5%) immunotherapy and 21 (36.8%) sorafenib patients. In the immunotherapy group, patients with increased SV were more likely than those with decreased SV to have a higher disease control rate (76.9% vs 57.1%, p = 0.024) and durable clinical benefit (52.8% vs 25.7%, p = 0.005). It was also associated with extended PFS in the immunotherapy group in both the univariate (p = 0.028) and multivariate (p = 0.014) analysis. By contrast, in the sorafenib group, an increased in SV was not associated with treatment response but was presumably associated with reduced PFS (p = 0.072) in the multivariate analysis. After IPTW adjustment, the increase in SV remained a significant predictor for DCB and PFS in the immunotherapy group.

Conclusion

Most patients exhibited an increase in SV after the initiation of immunotherapy, which may be used to predict response and prognosis. However, this association was not observed in patients who received sorafenib.

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.

CT Rule-in and Rule-out Criteria for Clinically Significant Portal Hypertension in Chronic Liver Disease

Background The value of CT in assessment of clinically significant portal hypertension (CSPH) has not been well determined. Purpose To evaluate the performance of CT features that have been associated with portal hypertension for diagnosing CSPH in patients with chronic liver disease (CLD). Materials and Methods This retrospective study included patients with CLD who underwent contrast-enhanced CT and subsequent hepatic venous pressure gradient (HVPG) measurement within 3 months at two tertiary institutions from January 2001 to December 2019. Two readers independently evaluated the presence of gastroesophageal varix, spontaneous portosystemic shunt (SPSS), and ascites on CT images. Splenomegaly at CT was determined using three methods, as follows: personalized or fixed volume criteria, based on spleen volume as measured by a deep learning algorithm, or manually measured spleen diameter. The diagnostic performance of these findings alone or in combination for detecting CSPH (HVPG ≥10 mm Hg) was evaluated. Results A total of 235 patients (mean age, 53.2 years ± 13.0 [SD]; 155 male patients), including 110 (46.8%) with CSPH, were included. Detection of CSPH according to the presence of both splenomegaly and at least one other CT feature (ie, gastroesophageal varix, SPSS, and ascites) achieved specificities of 94.4%-97.6%, whereas detection of CSPH according to the presence of any feature (ie, splenomegaly, gastroesophageal varix, SPSS, or ascites) achieved sensitivities of 94.5%-98.2%. When employing the former as rule-in criteria with the absence of splenomegaly, gastroesophageal varix, SPSS, and ascites as rule-out criteria for CSPH, 171-185 (range, 72.8%-78.7%) of 235 patients were correctly classified as either having CSPH or not, seven to 13 (range, 3%-5.5%) of 235 patients were incorrectly classified, and 42-54 (range, 17.9%-23%) of 235 patients were unclassified. Conclusion The presence or absence of splenomegaly, gastroesophageal varix, SPSS, and/or ascites on CT images may be useful for ruling in and ruling out CSPH in patients with CLD. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Fraum in this issue.

Change in Splenic Volume as a Surrogate Marker for Immunotherapy Response in Patients with Advanced Urothelial and Renal Cell Carcinoma-Evaluation of a Novel Approach of Fully Automated Artificial Intelligence Based Splenic Segmentation

BACKGROUND

In the treatment of advanced urothelial (aUC) and renal cell carcinoma (aRCC), biomarkers such as PD-1 and PD-L1 are not robust prognostic markers for immunotherapy (IO) response. Previously, a significant association between IO and a change in splenic volume (SV) was described for several tumour entities. To the best of our knowledge, this study presents the first correlation of SV to IO in aUC and aRCC.

METHODS

All patients with aUC (05/2017-10/2021) and aRCC (01/2012-05/2022) treated with IO at our academic centre were included. SV was measured at baseline, 3 and 9 months after initiation of IO using an in-house developed convolutional neural network-based spleen segmentation method. Uni- and multivariate Cox regression models for overall survival (OS) and progression-free survival (PFS) were used.

RESULTS

In total, 35 patients with aUC and 30 patients with aRCC were included in the analysis. Lower SV at the three-month follow-up was significantly associated with improved OS in the aRCC group.

CONCLUSIONS

We describe a new, innovative artificial intelligence-based approach of a radiological surrogate marker for IO response in aUC and aRCC which presents a promising new predictive imaging marker. The data presented implicate improved OS with lower follow-up SV in patients with aRCC.

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.

Baveno VII algorithm outperformed other models in ruling out high-risk varices in individuals with HBV-related cirrhosis

BACKGROUND & AIMS

The Baveno VII consensus recommends that spleen stiffness measurement (SSM) ≤40 kPa is safe for ruling out high-risk varices (HRVs) and avoiding endoscopic screening in patients who do not meet the Baveno VI criteria. This study aimed to validate the performance of the Baveno VII algorithm in individuals with HBV-related cirrhosis.

METHODS

Consecutive individuals with HBV-related cirrhosis who underwent liver stiffness measurement (LSM) and SSM - using a 50 Hz shear wave frequency, spleen diameter measurement, and esophagogastroduodenoscopy (EGD) were prospectively enrolled from June 2020. A 100 Hz probe has been adopted for additional SSM assessment since July 2021.

RESULTS

From June 2020 to January 2022, 996 patients were screened and 504 were enrolled for analysis. Among the 504 patients in whom SSM was assessed using a 50 Hz probe, the Baveno VII algorithm avoided more EGDs (56.7% vs. 39.1%, p <0.001) than Baveno VI criteria, with a comparable missed HRV rate (3.8% vs. 2.5%). Missed HRV rates were >5% for all other measures: 11.3% for LSM-longitudinal spleen diameter to platelet ratio score, 20.0% for platelet count/longitudinal spleen diameter ratio, and 8.8% for Rete Sicilia Selezione Terapia-hepatitis. SSM@100 Hz was assessed in 232 patients, and the Baveno VII algorithm with SSM@100 Hz spared more EGDs (75.4% vs. 59.5%, p <0.001) than that with SSM@50 Hz, both with a missed HRV rate of 3.0% (1/33).

CONCLUSIONS

We validated the Baveno VII algorithm, demonstrating the excellent performance of SSM@50 Hz and SSM@100 Hz in ruling out HRV in individuals with HBV-related cirrhosis. Furthermore, the Baveno VII algorithm with SSM@100 Hz could safely rule out more EGDs than that with SSM@50 Hz.

CLINICAL TRIAL NUMBER

NCT04890730.

IMPACT AND IMPLICATIONS

The Baveno VII guideline proposed that for patients who do not meet the Baveno VI criteria, SSM ≤40 kPa could avoid further unnecessary endoscopic screening. The current study validated the Baveno VII algorithm using 50 Hz and 100 Hz probes, which both exhibited excellent performance in ruling out HRVs in individuals with HBV-related cirrhosis. Compared with the Baveno VII algorithm with SSM@50 Hz, SSM@100 Hz had a better capability to safely rule out unnecessary EGDs. Baveno VII algorithm will be a practical tool to triage individuals with cirrhosis in future clinical practice.

A nomogram for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma based on spleen-volume-to-platelet ratio

BACKGROUND AND OBJECTIVES

Post-hepatectomy liver failure (PHLF) is one of the most serious complications after hepatectomy in patients with hepatocellular carcinoma (HCC), and has an association with high morbidity and mortality. This study aimed to explore the risk factors of PHLF and to establish and validate a nomogram to predict PHLF.

METHODS

We retrospectively analyzed 971 HCC patients undergoing major liver resection at the Eastern Hepatobiliary Surgery Hospital between 2011 and 2016, and established a nomogram based on multivariate analyses for predicting PHLF. The predictive accuracy and discriminative ability of the nomogram were evaluated by concordance index (C-index) and calibration curve. The predictive ability of PHLF of this nomogram was compared with conventional models using receiver operating characteristic (ROC) curves.

RESULTS

The incidence of PHLF was 18.8%. Multivariate analysis identified age, BMI, preoperative ascites, preoperative prealbumin, spleen volume-to-platelet ratio, total bilirubin, and intraoperative blood loss as independent predictors of PHLF. The area under ROC curve (AUROC) of the predictive model was 0.668 and was higher than that of the albumin-bilirubin score (ALBI). The optimal cut-off value of the model was 124.

CONCLUSIONS

We constructed a nomogram for predicting PHLF based on risk factors. The nomogram can assist clinicians in identifying patients with high-risk PHLF, eventually facilitating earlier interventions and improving clinical outcomes.

An interpretable artificial intelligence system for detecting risk factors of gastroesophageal variceal bleeding

Bleeding risk factors for gastroesophageal varices (GEV) detected by endoscopy in cirrhotic patients determine the prophylactical treatment patients will undergo in the following 2 years. We propose a methodology for measuring the risk factors. We create an artificial intelligence system (ENDOANGEL-GEV) containing six models to segment GEV and to classify the grades (grades 1-3) and red color signs (RC, RC0-RC3) of varices. It also summarizes changes in the above results with region in real time. ENDOANGEL-GEV is trained using 6034 images from 1156 cirrhotic patients across three hospitals (dataset 1) and validated on multicenter datasets with 11009 images from 141 videos (dataset 2) and in a prospective study recruiting 161 cirrhotic patients from Renmin Hospital of Wuhan University (dataset 3). In dataset 1, ENDOANGEL-GEV achieves intersection over union values of 0.8087 for segmenting esophageal varices and 0.8141 for gastric varices. In dataset 2, the system maintains fairly accuracy across images from three hospitals. In dataset 3, ENDOANGEL-GEV surpasses attended endoscopists in detecting RC of GEV and classifying grades (p < 0.001). When ranking the risk of patients combined with the Child‒Pugh score, ENDOANGEL-GEV outperforms endoscopists for esophageal varices (p < 0.001) and shows comparable performance for gastric varices (p = 0.152). Compared with endoscopists, ENDOANGEL-GEV may help 12.31% (16/130) more patients receive the right intervention. We establish an interpretable system for the endoscopic diagnosis and risk stratification of GEV. It will assist in detecting the first bleeding risk factors accurately and expanding the scope of quantitative measurement of diseases.

Prediction of Decompensation and Death in Advanced Chronic Liver Disease Using Deep Learning Analysis of Gadoxetic Acid-Enhanced MRI

OBJECTIVE

This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in patients with advanced chronic liver disease (ACLD).

MATERIALS AND METHODS

We included patients who underwent baseline and 1-year follow-up MRI from a prospective cohort that underwent gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance between November 2011 and August 2012 at a tertiary medical center. Baseline liver condition was categorized as non-ACLD, compensated ACLD, and decompensated ACLD. The liver-to-spleen signal intensity ratio (LS-SIR) and liver-to-spleen volume ratio (LS-VR) were automatically measured on the HBP images using a deep learning algorithm, and their percentage changes at the 1-year follow-up (ΔLS-SIR and ΔLS-VR) were calculated. The associations of the MRI indices with hepatic decompensation and a composite endpoint of liver-related death or transplantation were evaluated using a competing risk analysis with multivariable Fine and Gray regression models, including baseline parameters alone and both baseline and follow-up parameters.

RESULTS

Our study included 280 patients (153 male; mean age ± standard deviation, 57 ± 7.95 years) with non-ACLD, compensated ACLD, and decompensated ACLD in 32, 186, and 62 patients, respectively. Patients were followed for 11-117 months (median, 104 months). In patients with compensated ACLD, baseline LS-SIR (sub-distribution hazard ratio [sHR], 0.81; p = 0.034) and LS-VR (sHR, 0.71; p = 0.01) were independently associated with hepatic decompensation. The ΔLS-VR (sHR, 0.54; p = 0.002) was predictive of hepatic decompensation after adjusting for baseline variables. ΔLS-VR was an independent predictor of liver-related death or transplantation in patients with compensated ACLD (sHR, 0.46; p = 0.026) and decompensated ACLD (sHR, 0.61; p = 0.023).

CONCLUSION

MRI indices automatically derived from the deep learning analysis of gadoxetic acid-enhanced HBP MRI can be used as prognostic markers in patients with ACLD.

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

Background & Aims

Non-invasive stratification of the liver decompensation risk remains unmet in people with compensated cirrhosis. This study aimed to develop a non-invasive tool (NIT) to predict hepatic decompensation.

Methods

This retrospective study recruited 689 people with compensated cirrhosis (median age, 54 years; 441 men) from 5 centres from January 2016 to June 2020. Baseline abdominal computed tomography (CT), clinical features, and liver stiffness were collected, and then the first decompensation was registered during the follow-up. The spleen-based model was designed for predicting decompensation based on a deep learning segmentation network to generate the spleen volume and least absolute shrinkage and selection operator (LASSO)-Cox. The spleen-based model was trained on the training cohort of 282 individuals (Institutions I–III) and was validated in 2 external validation cohorts (97 and 310 individuals from Institutions IV and V, respectively) and compared with the conventional serum-based models and the Baveno VII criteria.

Results

The decompensation rate at 3 years was 23%, with a 37.6-month median (IQR 21.1–52.1 months) follow-up. The proposed model showed good performance in predicting decompensation (C-index ≥0.84) and outperformed the serum-based models (C-index comparison test p <0.05) in both the training and validation cohorts. The hazard ratio (HR) for decompensation in individuals with high risk was 7.3 (95% CI 4.2–12.8) in the training and 5.8 (95% CI 3.9–8.6) in the validation (log-rank test, p <0.05) cohorts. The low-risk group had a negligible 3-year decompensation risk (≤1%), and the model had a competitive performance compared with the Baveno VII criteria.

Conclusions

This spleen-based model provides a non-invasive and user-friendly method to help predict decompensation in people with compensated cirrhosis in diverse healthcare settings where liver stiffness is not available.

Lay summary

People with compensated cirrhosis with larger spleen volume would have a higher risk of decompensation. We developed a spleen-based model and validated it in external validation cohorts. The proposed model might help predict hepatic decompensation in people with compensated cirrhosis when invasive tools are unavailable.

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Spleen volume is a predictor for decompensation by rapid risk increasement after spleen volume >364 cm³.

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The spleen-based model revealed incremental prognostic improvement (C-index >0.84).

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Low-risk patients identified by the spleen-based model had a negligible 3-year risk (≤1%) of decompensation.

Noncirrhotic Portal Hypertension after Trastuzumab Emtansine in HER2-positive Breast Cancer as Determined by Deep Learning-measured Spleen Volume at CT

Background Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate approved for use in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Case reports have suggested an association between T-DM1 and portal hypertension. Purpose To evaluate the association of T-DM1 therapy with spleen volume changes and portal hypertension on CT scans and clinical findings compared with lapatinib and capecitabine therapy. Materials and Methods Patients with HER2-positive breast cancer who were administered at least two cycles of T-DM1 or lapatinib and capecitabine (controls) in a tertiary institution from 2001 to 2020 and who underwent CT before initial treatment and at least once during treatment were retrospectively enrolled. Spleen volume changes and the signs of portal hypertension (gastroesophageal varix [GEV], spontaneous portosystemic shunt [SPSS], and ascites) were evaluated at contrast-enhanced CT. Patients were followed until treatment ended or for 2 years after the start of treatment. Spleen volume changes were measured with a deep learning algorithm and evaluated by using a linear mixed model. The incidences of splenomegaly and portal hypertension were compared between the T-DM1 and control groups by using a χ² test or Fisher exact test. Results The T-DM1 group included 111 patients (mean age, 54 years ± 11 [SD]; 111 women) and the control group included 122 patients (mean age, 50 years ± 9; 121 women). Spleen volume progressively increased with T-DM1 therapy but was constant in the control group (104% ± 5 vs -1% ± 6 at the 33rd treatment cycle, respectively; P < .001). Incidences of splenomegaly (46% [51 of 111] vs 3% [four of 122] of patients; P < .001), GEV (11% [12 of 111] vs 1% [one of 122] of patients; P < .001), and SPSS (27% [30 of 111] vs 1% [one of 122] of patients; P < .001) were higher in the T-DM1 group than in the control group. Conclusion Trastuzumab emtansine therapy was associated with noncirrhotic portal hypertension at CT, with higher incidences of splenomegaly, gastroesophageal varix, and spontaneous portosystemic shunt than those with lapatinib and capecitabine therapy. © RSNA, 2022 Online supplemental material is available for this article.

Baseline Splenic Volume Outweighs Immuno-Modulated Size Changes with Regard to Survival Outcome in Patients with Hepatocellular Carcinoma under Immunotherapy

Simple Summary

Splenic volume (SV) has been identified as a highly predictive parameter for prognosis in patients with hepatocellular carcinoma (HCC). Moreover, an association between immunotherapy and an increase in SV has been described for various types of cancer. In our cohort of patients with HCC under immunotherapy, SV was a highly predictive factor for overall survival at baseline and initial follow-up. Although a large proportion of patients (76%) showed an SV increase after the initiation of immunotherapy, this additional immuno-modulated SV change was negligible compared to long-standing changes in the splanchnic circulation in our patient cohort.

Abstract

Background: An association between immunotherapy and an increase in splenic volume (SV) has been described for various types of cancer. SV is also highly predictive of overall survival (OS) in patients with hepatocellular carcinoma (HCC). We evaluated SV and its changes with regard to their prognostic influence in patients with HCC undergoing immunotherapy. Methods: All patients with HCC who received immunotherapy in first or subsequent lines at our tertiary care center between 2016 and 2021 were screened for eligibility. SV was assessed at baseline and follow-up using an AI-based tool for spleen segmentation. Patients were dichotomized into high and low SV based on the median value. Results: Fifty patients were included in the analysis. The median SV prior to treatment was 532 mL. The median OS of patients with high and low SV was 5.1 months and 18.1 months, respectively (p = 0.01). An increase in SV between treatment initiation and the first follow-up was observed in 28/37 (75.7%) patients with follow-up imaging available. This increase in itself was not prognostic for median OS (7.0 vs. 8.5 months, p = 0.73). However, patients with high absolute SV at the first follow-up continued to have impaired survival (4.0 months vs. 30.7 months, p = 0.004). Conclusion: High SV prior to and during treatment was a significant prognostic factor for impaired outcome. Although a large proportion of patients showed an SV increase after the initiation of immunotherapy, this additional immuno-modulated SV change was negligible compared to long-standing changes in the splanchnic circulation in patients with HCC.

Fully automated AI-based splenic segmentation for predicting survival and estimating the risk of hepatic decompensation in TACE patients with HCC

OBJECTIVES

Splenic volume (SV) was proposed as a relevant prognostic factor for patients with hepatocellular carcinoma (HCC). We trained a deep-learning algorithm to fully automatically assess SV based on computed tomography (CT) scans. Then, we investigated SV as a prognostic factor for patients with HCC undergoing transarterial chemoembolization (TACE).

METHODS

This retrospective study included 327 treatment-naïve patients with HCC undergoing initial TACE at our tertiary care center between 2010 and 2020. A convolutional neural network was trained and validated on the first 100 consecutive cases for spleen segmentation. Then, we used the algorithm to evaluate SV in all 327 patients. Subsequently, we evaluated correlations between SV and survival as well as the risk of hepatic decompensation during TACE.

RESULTS

The algorithm showed Sørensen Dice Scores of 0.96 during both training and validation. In the remaining 227 patients assessed with the algorithm, spleen segmentation was visually approved in 223 patients (98.2%) and failed in four patients (1.8%), which required manual re-assessments. Mean SV was 551 ml. Survival was significantly lower in patients with high SV (10.9 months), compared to low SV (22.0 months, p = 0.001). In contrast, overall survival was not significantly predicted by axial and craniocaudal spleen diameter. Furthermore, patients with a hepatic decompensation after TACE had significantly higher SV (p < 0.001).

CONCLUSION

Automated SV assessments showed superior survival predictions in patients with HCC undergoing TACE compared to two-dimensional spleen size estimates and identified patients at risk of hepatic decompensation. Thus, SV could serve as an automatically available, currently underappreciated imaging biomarker.

KEY POINTS

• Splenic volume is a relevant prognostic factor for prediction of survival in patients with HCC undergoing TACE, and should be preferred over two-dimensional surrogates for splenic size. • Besides overall survival, progression-free survival and hepatic decompensation were significantly associated with splenic volume, making splenic volume a currently underappreciated prognostic factor prior to TACE. • Splenic volume can be fully automatically assessed using deep-learning methods; thus, it is a promising imaging biomarker easily integrable into daily radiological routine.

Artificial intelligence in the diagnosis of cirrhosis and portal hypertension

Clinically significant portal hypertension is associated with an increased risk of developing gastroesophageal varices and hepatic decompensation. Hepatic venous pressure gradient measurement and esophagogastroduodenoscopy are the gold-standard methods for assessing clinically significant portal hypertension and gastroesophageal varices, respectively. However, invasiveness, cost, and feasibility limit their widespread use, especially if repeated and serial evaluations are required to assess the efficacy of pharmacotherapy. Artificial intelligence describes a range of techniques that allow machines to perform tasks typically thought to require human reasoning and problem-solving skills. Artificial intelligence has made great strides in the field of medicine, and is also involved in portal hypertension diagnosis. Artificial intelligence tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time. This review focuses on the recent advances in artificial intelligence for the noninvasive diagnosis of portal hypertension and gastroesophageal varices and monitoring of risk assessment of its complications in clinical practice.

A systematic review on application of deep learning in digestive system image processing

With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.

Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases

Artificial intelligence (AI) has been widely involved in every aspect of healthcare in the preclinical stage. In the digestive system, AI has been trained to assist auxiliary examinations including histopathology, endoscopy, ultrasonography, computerized tomography, and magnetic resonance imaging in detection, diagnosis, classification, differentiation, prognosis, and quality control. In the field of endoscopy, the application of AI, such as automatic detection, diagnosis, classification, and invasion depth, in early gastrointestinal (GI) cancers has received wide attention. There is a paucity of studies of AI application on common GI benign diseases based on endoscopy. In the review, we provide an overview of AI applications to endoscopy on common GI benign diseases including in the esophagus, stomach, intestine, and colon. It indicates that AI will gradually become an indispensable part of normal endoscopic detection and diagnosis of common GI benign diseases as clinical data, algorithms, and other related work are constantly repeated and improved.