Assessment of liver fibrosis severity using computed tomography-based liver and spleen volumetric indices in patients with chronic liver disease

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios.

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.

Computed Tomography-Derived Extracellular Volume Fraction and Splenic Size for Liver Fibrosis Staging

OBJECTIVE

Extracellular volume fraction (fECV) and liver and spleen size have been correlated with liver fibrosis stages and cirrhosis. The purpose of the current study was to determine the predictive value of fECV alone and in conjunction with measurement of liver and spleen size for severity of liver fibrosis.

METHODS

This was a retrospective study of 95 subjects (65 with liver biopsy and 30 controls). Spearman rank correlation coefficient was used to assess correlation between radiological markers and fibrosis stage. Receiver operating characteristic analysis was performed to assess the discriminative ability of radiological markers for significant (F2+) and advanced (F3+) fibrosis and cirrhosis (F4), by reporting the area under the curve (AUC).

RESULTS

The cohort had a mean age of 51.4 ± 14.4 years, and 52 were female (55%). There were 36, 5, 6, 9, and 39 in fibrosis stages F0, F1, F2, F3, and F4, respectively. Spleen volume alone showed the highest correlation (r = 0.552, P < 0.001) and AUCs of 0.823, 0.807, and 0.785 for identification of significant and advanced fibrosis and cirrhosis, respectively. Adding fECV to spleen length improved AUCs (0.764, 0.745, and 0.717 to 0.812, 0.781, and 0.738, respectively) compared with splenic length alone. However, adding fECV to spleen volume did not improve the AUCs for significant or advanced fibrosis or cirrhosis.

CONCLUSIONS

Spleen size (measured in length or volume) showed better correlation with liver fibrosis stages compared with fECV. The combination of fECV and spleen length had higher accuracy compared with fECV alone or spleen length alone.

Radiomic Features at Contrast-Enhanced CT Predict Virus-Driven Liver Fibrosis: A Multi-Institutional Study

INTRODUCTION

Liver fibrosis is a major cause of morbidity and mortality among in patients with chronic hepatitis. Radiomics, particularly of the spleen, may improve diagnostic accuracy and treatment strategies. External validations are necessary to ensure reliability and generalizability.

METHODS

In this retrospective study, we developed 3 radiomics models using contrast-enhanced computed tomography scans from 167 patients with liver fibrosis (training group) between January 2020 and December 2021. Radiomic features were extracted from arterial venous, portal venous, and equilibrium phase images. Recursive feature selection random forest and the least absolute shrinkage and selection operator logistic regression were used for feature selection and dimensionality reduction. Performance was assessed by area under the curve, C-index, calibration plots, and decision curve analysis. External validation was performed on 114 patients from 2 institutions.

RESULTS

Twenty-five radiomic features were significantly associated with fibrosis stage, with 80% of the top 10 features originating from portal venous phase spleen images. The radiomics models showed good performance in the validation cohort (C-indices 0.723-0.808) and excellent calibration. Decision curve analysis indicated clinical benefits, with machine learning-based radiomics models (Random Forest score and support vector machine based radiomics score) providing more significant advantages.

DISCUSSION

Radiomic features offer significant benefits over existing serum indices for staging virus-driven liver fibrosis, underscoring the value of radiomics in enhancing diagnostic accuracy. Specifically, radiomics analysis of the spleen presents additional noninvasive options for assessing fibrosis, highlighting its potential in improving patient management and outcomes.

Comparison between CT volumetry, technetium99m galactosyl-serum-albumin scintigraphy, and gadoxetic-acid-enhanced MRI to estimate the liver fibrosis stage in preoperative patients

OBJECTIVES

To compare the efficacy of computed tomography volumetry (CTV), technetium99m galactosyl-serum-albumin (99mTc-GSA) scintigraphy, and gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic-acid-enhanced MRI (EOB-MRI) in estimating the liver fibrosis (LF) stage in patients undergoing liver resection.

METHODS

This retrospective study included 91 consecutive patients who had undergone preoperative dynamic CT and 99mTc-GSA scintigraphy. EOB-MRI was performed in 76 patients. CTV was used to measure the total liver volume (TLV), spleen volume (SV), normalised to the body surface area (BSA), and liver-to-spleen volume ratio (TLV/SV). 99mTc-GSA scintigraphy provided LHL15, HH15, and GSA indices. The liver-to-spleen ratio (LSR) was calculated in the hepatobiliary phase of EOB-MRI. Hyaluronic acid and type 4 collagen levels were measured in 65 patients. Logistic regression and receiver operating characteristic (ROC) analyses were performed to identify useful parameters for estimating the LF stage and laboratory data.

RESULTS

According to the multivariable logistic regression analysis, SV/BSA (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.003-1.02; p = 0.011), LSR (OR, 0.06; 95%CI, 0.004-0.70; p = 0.026), and hyaluronic acid (OR, 1.01; 95%CI, 1.001-1.02; p = 0.024) were independent variables for severe LF (F3-4). Combined SV/BSA, LSR, and hyaluronic acid correctly estimated severe LF, with an AUC of 0.91, which was significantly larger than the AUCs of the GSA index (AUC = 0.84), SV/BSA (AUC = 0.83), or LSR (AUC = 0.75) alone.

CONCLUSIONS

Combined CTV, EOB-MRI, and hyaluronic acid analyses improved the estimation accuracy of severe LF compared to CTV, EOB-MRI, or 99mTc-GSA scintigraphy individually.

CLINICAL RELEVANCE STATEMENT

The combined analysis of spleen volume on CT volumetry, liver-to-spleen ratio on gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic-acid-enhanced MRI, and hyaluronic acid can identify severe liver fibrosis associated with a high risk of liver failure after hepatectomy and recurrence in patients with hepatocellular carcinoma.

KEY POINTS

• Spleen volume of CT volumetry normalised to the body surface area, liver-to-spleen ratio of EOB-MRI, and hyaluronic acid were independent variables for liver fibrosis. • CT volumetry and EOB-MRI enable the detection of severe liver fibrosis, which may correlate with post-hepatectomy liver failure and complications. • Combined CT volumetry, gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic-acid-enhanced MRI (EOB-MRI), and hyaluronic acid analyses improved the estimation of severe liver fibrosis compared to technetium99m galactosyl-serum-albumin scintigraphy.

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.

Comparison between Intravoxel Incoherent Motion and Splenic Volumetry to Predict Hepatic Fibrosis Staging in Preoperative Patients

Intravoxel incoherent motion (IVIM) and splenic volumetry (SV) for hepatic fibrosis (HF) prediction have been reported to be effective. Our purpose is to compare the HF prediction of IVIM and SV in 67 patients with pathologically staged HF. SV was divided by body surface area (BSA). IVIM indices, such as slow diffusion-coefficient related to molecular diffusion (D), fast diffusion-coefficient related to perfusion in microvessels (D*), apparent diffusion-coefficient (ADC), and perfusion related diffusion-fraction (f), were calculated by two observers (R1/R2). D (p = 0.718 for R1, p = 0.087 for R2) and D* (p = 0.513, p = 0.708, respectively) showed a poor correlation with HF. ADC (p = 0.034, p = 0.528, respectively) and f (p < 0.001, p = 0.007, respectively) decreased as HF progressed, whereas SV/BSA increased (p = 0.015 for R1). The AUCs of SV/BSA (0.649-0.698 for R1) were higher than those of f (0.575-0.683 for R1 + R2) for severe HF (≥F3-4 and ≥F4), although AUCs of f (0.705-0.790 for R1 + R2) were higher than those of SV/BSA (0.628 for R1) for mild or no HF (≤F0-1). No significant differences to identify HF were observed between IVIM and SV/BSA. SV/BSA allows a higher estimation for evaluating severe HF than IVIM. IVIM is more suitable than SV/BSA for the assessment of mild or no HF.

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

OBJECTIVES

Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?

METHODS

A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.

RESULTS

One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.

CONCLUSION

Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.

KEY POINTS

• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

Quantitative tumor burden imaging parameters of the spleen at MRI for predicting treatment response in patients with acute leukemia

Objectives

To study the value of standardized volume and intravoxel incoherent motion (IVIM) parameters of the spleen based on tumor burden for predicting treatment response in newly diagnosed acute leukemia (AL).

Methods

Patients with newly diagnosed AL were recruited and underwent abdominal IVIM diffusion-weighted imaging within one week before the first induction chemotherapy. Quantitative parameters of magnetic resonance imaging (MRI) included the standardized volume (representing volumetric tumor burden) and IVIM parameters (standard apparent diffusion coefficient [sADC]; pure diffusion coefficient [D]; pseudo-diffusion coefficient [D∗]; and pseudo-perfusion fraction [f], representing functional tumor burden) of the spleen. Clinical biomarkers of tumor burden were collected. Patients were divided into complete remission (CR) and non-CR groups according to the treatment response after the first standardized induction chemotherapy, and the MRI and clinical parameters were compared between the two groups. The correlations of MRI parameters with clinical biomarkers were analyzed. Multivariate logistic regression was performed to determine the independent predictors for treatment response. Receiver operating characteristic curves were used to analyze the predicted performance.

Results

76 AL patients (CR: n = 43; non-CR: n = 33) were evaluated. Standardized spleen volume, sADC, D, f, white blood cell counts, and lactate dehydrogenase were significantly different between CR and non-CR groups (all p < 0.05). Standardized spleen volume, sADC, and D were correlated with white blood cell and lactate dehydrogenase, and f was correlated with lactate dehydrogenase (all p < 0.05). Standardized spleen volume (hazard ratio = 4.055, p = 0.042), D (hazard ratio = 0.991, p = 0.027), and f (hazard ratio = 1.142, p = 0.008) were independent predictors for treatment response, and the combination of standardized spleen volume, D, and f showed more favorable discrimination (area under the curve = 0.856) than individual predictors.

Conclusion

Standardized volume, D, and f of the spleen could be used to predict treatment response in newly diagnosed AL, and the combination of morphological and functional parameters would further improve the predicted performance. IVIM parameters of the spleen may be viable indicators for evaluating functional tumor burden in AL.

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.

Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging

Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.

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.

Autoimmune Hepatitis and Fibrosis

Autoimmune hepatitis (AIH) is a chronic immune-inflammatory disease of the liver, generally considered a rare condition. The clinical manifestation is extremely varied and can range from paucisymptomatic forms to severe hepatitis. Chronic liver damage causes activation of hepatic and inflammatory cells leading to inflammation and oxidative stress through the production of mediators. This results in increased collagen production and extracellular matrix deposition leading to fibrosis and even cirrhosis. The gold standard for the diagnosis of fibrosis is liver biopsy; however, there are serum biomarkers, scoring systems, and radiological methods useful for diagnosis and staging. The goal of AIH treatment is to suppress fibrotic and inflammatory activities in the liver to prevent disease progression and achieve complete remission. Therapy involves the use of classic steroidal anti-inflammatory drugs and immunosuppressants, but in recent years scientific research has focused on several new alternative drugs for AIH that will be discussed in the review.

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.

A meritorious integrated medical regimen for hepatic fibrosis and its complications via the systematic review and meta-analysis for Dahuang Zhechong pill-based therapy

Background

Hepatic fibrosis is a health challenge due to the absence of satisfactory therapy, especially at the cirrhosis stage. Dahuang Zhechong pill (DHZCP)-based therapy is reportedly a successful treatment for hepatic fibrosis and is even beneficial for the treatment of cirrhosis. Hence, a systematic review and clinical evidence assessment of DHZCP-based therapy should be performed, and clinical recommendations based on its efficacy for the treatment of hepatic fibrosis should be generated. With respect to potential indicators, the comparative value of the hepatic function, spleen thickness, and portal vein internal diameter should be evaluated.

Materials and methods

PubMed, the Excerpta Medica Database, the Cochrane Library, the Web of Science, the WanFang Database, the Chinese Scientific Journal Database, and the Chinese National Knowledge Infrastructure database were searched to identify clinical trials. Three subgroup analyses were performed based on the stage of disease, medication use, and the course of treatment. Statistical analyses were performed using Review Manager 5.4.

Results

A total of 18 studies including 1,494 patients were evaluated. The DHZCP-based therapy was effective in reducing the plasma levels of hyaluronic acid, and laminin, procollagen III, and IV collagen were also reduced irrespective of the hepatitis stage or the presence of hepatic cirrhosis. Abnormalities in alanine aminotransferase, aspartate aminotransferase, albumin, and total bilirubin were reversed. A 6-month course of treatment was the most beneficial DHZCP-based therapy regimen. Alanine aminotransferase improvement was more obvious in patients with cirrhosis, and alanine aminotransferase was reduced significantly in patients with hepatic cirrhosis. With respect to pharmacological mechanisms, DHZCP-based therapy could inhibit hepatic stellate cell growth and activation, reduce inflammation, and prevent extracellular matrix formation. Hepatic portal hypertension and splenomegaly were ameliorated significantly in the DHZCP-based therapy group.

Conclusion

Dahuang Zhechong pill-based therapy has demonstrated efficacy as a treatment for hepatic fibrosis and cirrhosis. A 6-month course of treatment is the recommended option for DHZCP-based therapy in clinical practice. The combination of DHZCP-based therapy and entecavir is a favorable treatment for hepatic cirrhosis.

Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques

Liver fibrosis features excessive protein accumulation in the liver interstitial space resulting from repeated tissue injury due to chronic liver disease. Liver fibrosis eventually proceeds to cirrhosis and associated complications. So, early diagnosis and staging of liver fibrosis are of vital importance for clinical treatment. Liver biopsy remains the gold standard for the diagnosing and staging of fibrosis, but it is suboptimal due to various limitations. Recently, efforts have been made to migrate toward noninvasive techniques for assessing liver fibrosis. CT is relatively easy to perform, relatively standardized for different scanners, and does not require additional hardware in liver fibrosis staging. MRI is frequently performed to characterize indeterminate liver lesions. Because it does not use ionizing radiation and features high image contrast, its role has increased in the staging of liver fibrosis. More recently, several studies on liver fibrosis staging using deep learning algorithms in CT or MRI have been proposed and have shown meaningful results. In this review, we summarize the basic concept, diagnostic performance, and advantages and limitations of each technique to noninvasively stage liver fibrosis.

Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker

BACKGROUND

To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images.

MATERIALS AND METHODS

This retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51-67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2. Optimal cutoffs to diagnose significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49-66 years; 89 men).

RESULTS

In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; p < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; p < .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70-87%, specificity range of 71-97%, and accuracy range of 82-86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis.

CONCLUSION

Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.

A novel machine learning-based radiomic model for diagnosing high bleeding risk esophageal varices in cirrhotic patients

BACKGROUND AND AIM

To develop and validate a novel machine learning-based radiomic model (RM) for diagnosing high bleeding risk esophageal varices (HREV) in patients with cirrhosis.

METHODS

A total of 796 qualified participants were enrolled. In training cohort, 218 cirrhotic patients with mild esophageal varices (EV) and 240 with HREV RM were included to training and internal validation groups. Additionally, 159 and 340 cirrhotic patients with mild EV and HREV RM, respectively, were used for external validation. Interesting regions of liver, spleen, and esophagus were labeled on the portal venous-phase enhanced CT images. RM was assessed by area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, calibration and decision curve analysis (DCA).

RESULTS

The AUROCs for mild EV RM in training and internal validation were 0.943 and 0.732, sensitivity and specificity were 0.863, 0.773 and 0.763, 0.763, respectively. The AUROC, sensitivity, and specificity were 0.654, 0.773 and 0.632, respectively, in external validation. Interestingly, the AUROCs for HREV RM in training and internal validation were 0.983 and 0.834, sensitivity and specificity were 0.948, 0.916 and 0.977, 0.969, respectively. The related AUROC, sensitivity and specificity were 0.736, 0.690 and 0.762 in external validation. Calibration and DCA indicated RM had good performance. Compared with Baveno VI and its expanded criteria, HREV RM had a higher accuracy and net reclassification improvements that were as high as 49.0% and 32.8%.

CONCLUSION

The present study developed a novel non-invasive RM for diagnosing HREV in cirrhotic patients with high accuracy. However, this RM still needs to be validated by a large multi-center cohort.

Diagnosis of Liver Cirrhosis and Liver Fibrosis by Artificial Intelligence Algorithm-Based Multislice Spiral Computed Tomography

This research was aimed at investigating the artificial intelligence (AI) segmentation algorithm-based multislice spiral computed tomography (MSCT) in the diagnosis of liver cirrhosis and liver fibrosis. Besides, it was aimed at providing new methods for the diagnosis of liver cirrhosis and liver fibrosis. All patients were divided into the control group, mild liver fibrosis group, and significant liver fibrosis group. A total of 112 patients were included, with 40 cases in the mild liver fibrosis group, 48 cases in the significant liver fibrosis group, and 24 cases who underwent computed tomography (CT) examination in the control group. In the research, deconvolution algorithm of AI segmentation algorithm was adopted to process the images. The average hepatic arterial fraction (HAF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 17.59 ± 10.03%, 18.23 ± 5.57%, and 20.98 ± 6.63%, respectively. The average MTT values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 12.69 ± 1.78S, 12.53 ± 2.05S, and 12.04 ± 1.57S, respectively. The average blood flow (BF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 105.68 ± 15.57 mL 100 g-1·min-1, 116.07 ± 16.5 mL·100 g-1·min-1, and 110.39 ± 16.32 mL·100 g-1·min-1, respectively. Besides, the average blood volume (BV) values of patients in the control group, mild liver fibrosis group, and significant liver fibrosis group were 15.69 ± 4.35 mL·log-1, 16.97 ± 2.68 mL·log-1, and 16.11 ± 4.87 mL·100 g-1, respectively. According to statistics, the differences among the average HAF, MTT, BF, and BV values showed no statistical meaning. AI segmentation algorithm-based MSCT imaging could promote the diagnosis of liver cirrhosis and liver fibrosis effectively and offer new methods to clinical diagnosis of liver cirrhosis and liver fibrosis.

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.

Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen

The aim of this study was to develop a deep learning-based algorithm for fully automated spleen segmentation using CT images and to evaluate the performance in conditions directly or indirectly affecting the spleen (e.g., splenomegaly, ascites). For this, a 3D U-Net was trained on an in-house dataset (n = 61) including diseases with and without splenic involvement (in-house U-Net), and an open-source dataset from the Medical Segmentation Decathlon (open dataset, n = 61) without splenic abnormalities (open U-Net). Both datasets were split into a training (n = 32.52%), a validation (n = 9.15%) and a testing dataset (n = 20.33%). The segmentation performances of the two models were measured using four established metrics, including the Dice Similarity Coefficient (DSC). On the open test dataset, the in-house and open U-Net achieved a mean DSC of 0.906 and 0.897 respectively (p = 0.526). On the in-house test dataset, the in-house U-Net achieved a mean DSC of 0.941, whereas the open U-Net obtained a mean DSC of 0.648 (p < 0.001), showing very poor segmentation results in patients with abnormalities in or surrounding the spleen. Thus, for reliable, fully automated spleen segmentation in clinical routine, the training dataset of a deep learning-based algorithm should include conditions that directly or indirectly affect the spleen.

Nature of the liver volume depending on the gender and age assessing volumetry from a reconstruction of the computed tomography

Although the liver is a regenerating organ, excessive loss of liver volume (LV) can cause fatal liver failure. It is unclear whether LV is correlated with age; however, it is known that liver function decreases with age. In addition, the gender-related role of LV remains unclear. This study aimed to investigate the changes in LV by age and gender. Between January and December 2018, 374 consecutive patients who underwent abdominal multidetector computed tomography (MDCT) for any abdominal examinations were enrolled. LV was evaluated using MDCT. The relationship between the LV and body mass index (BMI), body surface area (BSA), age, and gender was investigated. The modified LV (mLV) was calculated by a formula measured LV × 1.5/BSA. LV correlated to BSA more than to BMI in both the males (R: 0.559 vs. 0.416) and females (R: 0.479 vs. 0.300) in our study. Age was negatively correlated to LV and BSA, and correlated to LV more than to BSA in males (R: 0.546 vs. 0.393) and females (R: 0.506 vs. 0.385). In addition, the absolute slope between age and LV in the males was higher than that in the females (14.1 vs. 10.2, respectively). Furthermore, the absolute slope of age and mLV in the males was slightly higher than in the females (9.1 vs. 7.3, respectively). In conclusion, LV in the normal liver is correlated to age rather than the one in the diseased liver. Liver volume in the males decreased more with age than LV in the females.

Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis

Objective

Although the liver-to-spleen volume ratio (LSVR) based on CT reflects portal hypertension, its prognostic role in cirrhotic patients has not been proven. We evaluated the utility of LSVR, automatically measured from CT images using a deep learning algorithm, as a predictor of hepatic decompensation and transplantation-free survival in patients with hepatitis B viral (HBV)-compensated cirrhosis.

Materials and Methods

A deep learning algorithm was used to measure the LSVR in a cohort of 1027 consecutive patients (mean age, 50.5 years; 675 male and 352 female) with HBV-compensated cirrhosis who underwent liver CT (2007–2010). Associations of LSVR with hepatic decompensation and transplantation-free survival were evaluated using multivariable Cox proportional hazards and competing risk analyses, accounting for either the Child-Pugh score (CPS) or Model for End Stage Liver Disease (MELD) score and other variables. The risk of the liver-related events was estimated using Kaplan-Meier analysis and the Aalen-Johansen estimator.

Results

After adjustment for either CPS or MELD and other variables, LSVR was identified as a significant independent predictor of hepatic decompensation (hazard ratio for LSVR increase by 1, 0.71 and 0.68 for CPS and MELD models, respectively; p < 0.001) and transplantation-free survival (hazard ratio for LSVR increase by 1, 0.8 and 0.77, respectively; p < 0.001). Patients with an LSVR of < 2.9 (n = 381) had significantly higher 3-year risks of hepatic decompensation (16.7% vs. 2.5%, p < 0.001) and liver-related death or transplantation (10.0% vs. 1.1%, p < 0.001) than those with an LSVR ≥ 2.9 (n = 646). When patients were stratified according to CPS (Child-Pugh A vs. B–C) and MELD (< 10 vs. ≥ 10), an LSVR of < 2.9 was still associated with a higher risk of liver-related events than an LSVR of ≥ 2.9 for all Child-Pugh (p ≤ 0.045) and MELD (p ≤ 0.009) stratifications.

Conclusion

The LSVR measured on CT can predict hepatic decompensation and transplantation-free survival in patients with HBV-compensated cirrhosis.

Artificial intelligence for hepatitis evaluation

Recently, increasing attention has been paid to the application of artificial intelligence (AI) to the diagnosis of diverse hepatic diseases, which comprises traditional machine learning and deep learning. Recent studies have shown the possible value of AI based data mining in predicting the incidence of hepatitis, classifying the different stages of hepatitis, diagnosing or screening for hepatitis, forecasting the progression of hepatitis, and predicting response to antiviral drugs in chronic hepatitis C patients. More importantly, AI based on radiology has been proven to be useful in predicting hepatitis and liver fibrosis as well as grading hepatocellular carcinoma (HCC) and differentiating it from benign liver tumors. It can predict the risk of vascular invasion of HCC, the risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis, and the risk of liver failure after hepatectomy in HCC patients. In this review, we summarize the application of AI in hepatitis, and identify the challenges and future perspectives.

Quantitative Susceptibility Mapping versus R2*-based Histogram Analysis for Evaluating Liver Fibrosis: Preliminary Results

PURPOSE

The staging of liver fibrosis is clinically important, and a less invasive method is preferred. Quantitative susceptibility mapping (QSM) has shown a great potential in estimating liver fibrosis in addition to R2* relaxometry. However, few studies have compared QSM analysis and liver fibrosis. We aimed to evaluate the feasibility of estimating liver fibrosis by using QSM and R2*-based histogram analyses by comparing it with ultrasound-based transient elastography and the stage of histologic fibrosis.

METHODS

Fourteen patients with liver disease were enrolled. Data sets of multi-echo gradient echo sequence with breath-holding were acquired on a 3-Tesla scanner. QSM and R2* were reconstructed by water-fat separation method, and ROIs were analyzed for these images. Quantitative parameters with histogram features (mean, variance, skewness, kurtosis, and 1st, 10th, 50th, 90th, and 99th percentiles) were extracted. These data were compared with the elasticity measured by ultrasound transient elastography and histological stage of liver fibrosis (F0 to F4, based on the new Inuyama classification) determined by biopsy or hepatectomy. The correlation of histogram parameters with intrahepatic elasticity and histologically confirmed fibrosis stage was examined. Texture parameters were compared between subgroups divided according to fibrosis stage. Receiver operating characteristic (ROC) analysis was also performed. P < 0.05 indicated statistical significance.

RESULTS

The six histogram parameters of both QSM and R2*were significantly correlated with intrahepatic elasticity. In particular, three parameters (variance, percentiles [90th and 99th]) of QSM showed high correlation (r = 0.818-0.844), whereas R2* parameters showed a moderate correlation with elasticity. Four parameters of QSM were significantly correlated with fibrosis stage (ρ = 0.637-0.723) and differentiated F2-4 from F0-1 fibrosis and F3-4 from F0-2 fibrosis with areas under the ROC curve of > 0.8, but those of R2* did not.

CONCLUSION

QSM may serve as a promising surrogate indicator in detecting liver fibrosis.

Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in Healthy Individuals and Those with Viral Hepatitis

Background Reference intervals guiding volumetric assessment of the liver and spleen have yet to be established. Purpose To establish population-based and personalized reference intervals for liver volume, spleen volume, and liver-to-spleen volume ratio (LSVR). Materials and Methods This retrospective study consecutively included healthy adult liver donors from 2001 to 2013 (reference group) and from 2014 to 2016 (healthy validation group) and patients with viral hepatitis from 2007 to 2017. Liver volume, spleen volume, and LSVR were measured with CT by using a deep learning algorithm. In the reference group, the reference intervals for the volume indexes were determined by using the population-based (ranges encompassing the central 95% of donors) and personalized (quantile regression modeling of the 2.5th and 97.5th percentiles as a function of age, sex, height, and weight) approaches. The validity of the reference intervals was evaluated in the healthy validation group and the viral hepatitis group. Results The reference and healthy validation groups had 2989 donors (mean age ± standard deviation, 30 years ± 9; 1828 men) and 472 donors (mean age, 30 years ± 9; 334 men), respectively. The viral hepatitis group had 158 patients (mean age, 48 years ± 12; 95 men). The population-based reference intervals were 824.5-1700.0 cm³ for liver volume, 81.1-322.0 cm³ for spleen volume, and 3.96-13.78 for LSVR. Formulae and a web calculator (https://i-pacs.com/calculators) were presented to calculate the personalized reference intervals. In the healthy validation group, both the population-based and personalized reference intervals were used to classify the volume indexes of 94%-96% of the donors as falling within the reference interval. In the viral hepatitis group, when compared with the population-based reference intervals, the personalized reference intervals helped identify more patients with volume indexes outside the reference interval (liver volume, 21.5% [34 of 158] vs 13.3% [21 of 158], P = .01; spleen volume, 29.1% [46 of 158] vs 22.2% [35 of 158], P = .01; LSVR, 35.4% [56 of 158] vs 26.6% [42 of 158], P < .001). Conclusion Reference intervals derived from a deep learning approach in healthy adults may enable evidence-based assessments of liver and spleen volume in clinical practice. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Ringl in this issue.

Prognostic value of splenic volume in hepatocellular carcinoma patients receiving transarterial chemoembolization

Background

Liver function is a key determinant for the survival of hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE). However, establishing robust prognostic indicators for liver insufficiencies and patient survival remains an unmet demand. This retrospective study evaluated the prognostic value of splenic volume (SV) in HCC patients undergoing TACE.

Methods

A total of 67 HCC patients who underwent at least two consecutive TACE procedures were retrospectively included in this study. Comprehensive clinical information and follow-up data were collected, and the SV was measured based on dynamic contrast enhanced images. Risk factors of SV enlargement were assessed. The prognostic value of SV on survival was analyzed and compared with Child-Pugh (CP) classification and albumin-bilirubin (ALBI) grade.

Results

The baseline SV was 299.74±143.63 cm³, and showed a moderate and statistically significant correlation with CP classification (R=0.31, P<0.05). The SV increased remarkably after the first and second TACE procedures (330.16±155.38 cm³, P<0.01, and 355.63±164.26 cm³, P<0.01, respectively). In survival analysis, the optimal cut-off value of SV was determined as 373 cm³ using X-tile software, and the patients were divided into the small SV group and the large SV groups accordingly. Based on the pre-TACE SV, the median overall survival (mOS) for patients in the small SV group and the large SV group was 458 days and 249 days, respectively (P<0.05). After the first and second TACE, the mOS in the small SV group and the large SV group were 454 vs. 266 days (P<0.05) and 526 vs. 266 days (P<0.05), respectively. No prognostic value of CP classification and ALBI grade was identified for these patients. Furthermore, there were no significant differences between the small and large SV groups in age, tumor stage, and ALBI grade, except for CP classification (P<0.05).

Conclusions

SV was correlated with CP classification and was a robust predictor for HCC patients undergoing TACE treatment.

Liver fibrosis assessment with multiphasic dual-energy CT: diagnostic performance of iodine uptake parameters

OBJECTIVES

To evaluate the ability of iodine uptake parameters from hepatic multiphasic CT to predict liver fibrosis, and compare absolute contrast enhancement (ΔHU) with dual-energy iodine density (ID) methods.

METHODS

One hundred seventeen patients with pathologically proven liver fibrosis who underwent dual-energy CT during portal-venous phase (PVP) and 3-min delayed phase (DP) between January 2017 and Octotber 2019 were retrospectively included. Two radiologists measured the hepatic and blood-pool iodine uptake using ΔHU and ID methods; extracellular volume fraction (ECV) and the iodine washout rate (IWR) calculated with both methods were compared between different fibrosis stages (F0-1 vs. F2-4, F0-2 vs. F3-4, or F0-3 vs. F4). The inter-observer reproducibility (intraclass correlation coefficients [ICCs]) for ECV and IWR was compared between the ΔHU and ID methods. The areas under the receiver operating characteristic curves (AUCs) to predict liver fibrosis severity were calculated for serum and imaging fibrosis markers. To identify independent predictors, multivariable logistic regression analysis was performed, and combined performance was assessed for the ΔHU and ID models.

RESULTS

Patients with F ≥ 2 (n = 70), F ≥ 3 (n = 51), and F4 (n = 29) had higher ECV and lower IWR than those with F ≤ 1, F ≤ 2, and F ≤ 3, respectively (all p < 0.001). ICCs were higher in the ID method than in the ΔHU method (ECV: p = 0.045; IWR: p < 0.001). The AUC ranges of ECV_ΔHU, ECV_ID, IWR_ΔHU, and IWR_ID for predicting liver fibrosis severity were 0.65-0.71, 0.67-0.73, 0.76-0.81, and 0.81-0.85, respectively. IWR and fibrosis-4 index were independent predictors, with combined AUCs of 0.82-0.87 for the ΔHU model and 0.86-0.89 for the ID model.

CONCLUSIONS

IWR more accurately predicted liver fibrosis than ECV in routine multiphasic CT. The dual-energy ID method yielded higher inter-observer reproducibility and predictive values than the single-energy ΔHU method.

KEY POINTS

• The IWR calculated from hepatic iodine uptake during PVP and 3-min DP predicted liver fibrosis (AUC, 0.76-0.85), while the ECV had a relatively limited predictive value (ACU, 0.65-0.73). • Compared with the conventional ΔHU method, the dual-energy ID method provided superior inter-observer reproducibility for measurement of ECV (p = 0.046) and IWR (p < 0.001). • The IWR and FIB-4 served as independent predictors of liver fibrosis; their combination yielded the high diagnostic performance particularly when using the ID method (combined AUCs of 0.86-0.89).

Radiomics and deep learning in liver diseases

Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.

Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images

Objective

Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions.

Materials and Methods

A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation.

Results

In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively). For the measurement of volumetric indices, the Bland-Altman 95% LOA was −0.17 ± 3.07% for liver volume and −0.56 ± 3.78% for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institution was comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation.

Conclusion

The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portal venous phase CT images of patients with various liver conditions.

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

OBJECTIVES

Deep learning enables an automated liver and spleen volume measurements on CT. The purpose of this study was to develop an index combining liver and spleen volumes and clinical factors for detecting high-risk varices in B-viral compensated cirrhosis.

METHODS

This retrospective study included 419 patients with B-viral compensated cirrhosis who underwent endoscopy and CT from 2007 to 2008 (derivation cohort, n = 239) and from 2009 to 2010 (validation cohort, n = 180). The liver and spleen volumes were measured on CT images using a deep learning algorithm. Multivariable logistic regression analysis of the derivation cohort developed an index to detect endoscopically confirmed high-risk varix. The cumulative 5-year risk of varix bleeding was evaluated with patients stratified by their index values.

RESULTS

The index of spleen volume-to-platelet ratio was devised from the derivation cohort. In the validation cohort, the cutoff index value for balanced sensitivity and specificity (> 3.78) resulted in the sensitivity of 69.4% and the specificity of 78.5% for detecting high-risk varix, and the cutoff index value for high sensitivity (> 1.63) detected all high-risk varices. The index stratified all patients into the low (index value ≤ 1.63; n = 118), intermediate (n = 162), and high (index value > 3.78; n = 139) risk groups with cumulative 5-year incidences of varix bleeding of 0%, 1.0%, and 12.0%, respectively (p < .001).

CONCLUSION

The spleen volume-to-platelet ratio obtained using deep learning-based CT analysis is useful to detect high-risk varices and to assess the risk of varix bleeding.

KEY POINTS

• The criterion of spleen volume to platelet > 1.63 detected all high-risk varices in the validation cohort, while the absence of visible varix did not exclude all high-risk varices. • Visual varix grade ≥ 2 detected high-risk varix with a high specificity (96.5-100%). • Combining spleen volume-to-platelet ratio ≤ 1.63 and visual varix grade of 0 identified low-risk patients who had no high-risk varix and varix bleeding on 5-year follow-up.

Immunosuppression and cardiovascular dysfunction in patients with severe versus mild coronavirus disease 2019: a case series

Objectives

As coronavirus disease 2019 (COVID-19) continues to spread globally, we aimed to describe and compare changes in the immune and cardiovascular systems of patients with mild versus severe COVID-19 at different time points during the course of disease.

Methods

One hundred and one patients diagnosed with COVID-19 who underwent serial peripheral blood collection and chest computed tomography (CT) imaging were enrolled in this study and grouped by the severity of their illness. Changes in the immune and cardiovascular systems were analysed and compared between groups.

Results

The study included 43 women and 58 men, with a median age of 45 years (interquartile range [IQR], 16-71). We identified spleen shrinkage in 27.7% of study patients. Ratios of spleen volume to patient (skin) volume were compared, with evidence that severe patients had more splenic shrinkage than mild patients. Lymphopenia was observed in 65.3% of patients, and 27.3% of patients had persistently low levels of lymphocytes after discharge. Tachycardia occurred mainly during the first 2 days of hospitalisation, with increases in creatine kinase-myocardial band levels in 10 (9.9%) patients and arrhythmias in 16 (15.8%) patients.

Conclusions

In addition to pulmonary manifestations, our study demonstrated that other organ systems can also be affected during COVID-19 infection, with evidence of immunosuppression and cardiovascular dysfunction, which may contribute to increased mortality rates in critically ill COVID-19 patients.

Prognostic role of spleen volume measurement using computed tomography in patients with compensated chronic liver disease from hepatitis B viral infection

OBJECTIVES

To determine the prognostic value of CT-based splenic volume measurement in patients with compensated chronic liver disease (cCLD) from chronic hepatitis B (CHB).

METHODS

A total of 584 patients having multiphasic liver CT scans between January and December 2011 were retrospectively reviewed. Spleen volume was measured using a semi-automated three-dimensional volumetric software program. Electronic medical records and national registry data were reviewed to determine the diagnosis of hepatocellular carcinoma (HCC), hepatic decompensation, or death. The cumulative incidence (CI) of the development of decompensation, HCC occurrence, and overall survival (OS) were estimated by the Kaplan-Meier method. The Cox proportional hazard regression model was used to evaluate prognostic factors. The optimal cutoff spleen volume to predict each outcome was obtained using a minimal p value approach method.

RESULTS

After a median follow-up of 92 months, 114 patients developed HCC with a 7-year CI of 17.2%. A larger spleen volume was a significant predictor of HCC occurrence (HR = 2.13, p = 0.009). Decompensation occurred in 30 patients with a 7-year CI of 5.0%, and a larger spleen volume was also significantly associated with the development of decompensation (HR = 4.66, p = 0.005). Twenty-three patients died, and their estimated 7-year OS was 96.4%. A larger spleen volume also significantly affected OS (HR = 6.15, p = 0.007). The optimal cutoff spleen volume was set at 532 mL for HCC occurrence, 656.9 mL for the development of decompensation, and 741.1 mL for OS.

CONCLUSIONS

A larger spleen volume was significantly associated with HCC occurrence, development of decompensation, and poor OS in patients with cCLD from CHB.

KEY POINTS

• Spleen volume could be easily acquired from routine multiphasic liver CT scan using a semi-automated 3D volumetric software program with excellent inter-observer agreement. • A larger spleen volume was significantly associated with a higher rate of hepatocellular carcinoma occurrence, the development of decompensation, and poor overall survival in patients with compensated chronic liver disease from chronic hepatitis B.