Radiomics Based on Contrast-Enhanced MRI in Differentiation Between Fat-Poor Angiomyolipoma and Hepatocellular Carcinoma in Noncirrhotic Liver: A Multicenter Analysis

ObjectiveThis study aims to develop and externally validate a contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics-based model for preoperative differentiation between fat-poor angiomyolipoma (fp-AML) and hepatocellular carcinoma (HCC) in patients with noncirrhotic livers and to compare the diagnostic performance with that of two radiologists.MethodsThis retrospective study was performed with 165 patients with noncirrhotic livers from three medical centers. The dataset was divided into a training cohort (n = 99), a time-independent internal validation cohort (n = 24) from one center, and an external validation cohort (n = 42) from the remaining two centers. The volumes of interest were contoured on the arterial phase (AP) images and then registered to the venous phase (VP) and delayed phase (DP), and a total of 3,396 radiomics features were extracted from the three phases. After the joint mutual information maximization feature selection procedure, four radiomics logistic regression classifiers, including the AP model, VP model, DP model, and combined model, were built. The area under the receiver operating characteristic curve (AUC), diagnostic accuracy, sensitivity, and specificity of each radiomics model and those of two radiologists were evaluated and compared.ResultsThe AUCs of the combined model reached 0.789 (95%CI, 0.579–0.999) in the internal validation cohort and 0.730 (95%CI, 0.563–0.896) in the external validation cohort, higher than the AP model (AUCs, 0.711 and 0.638) and significantly higher than the VP model (AUCs, 0.594 and 0.610) and the DP model (AUCs, 0.547 and 0.538). The diagnostic accuracy, sensitivity, and specificity of the combined model were 0.708, 0.625, and 0.750 in the internal validation cohort and 0.619, 0.786, and 0.536 in the external validation cohort, respectively. The AUCs for the two radiologists were 0.656 and 0.594 in the internal validation cohort and 0.643 and 0.500 in the external validation cohort. The AUCs of the combined model surpassed those of the two radiologists and were significantly higher than that of the junior one in both validation cohorts.ConclusionsThe proposed radiomics model based on triple-phase CE-MRI images was proven to be useful for differentiating between fp-AML and HCC and yielded comparable or better performance than two radiologists in different centers, with different scanners and different scanning parameters.

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Differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma using contrast-enhanced ultrasound: A systematic review and meta-analysis

Clinical Hemorheology and Microcirculation ◽

10.3233/ch-211145 ◽

2021 ◽

pp. 1-17

Author(s):

Yanling Chen ◽

Wenping Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Diagnostic Accuracy ◽

Publication Bias ◽

Likelihood Ratio ◽

Intrahepatic Cholangiocarcinoma ◽

Cochrane Library ◽

Arterial Phase ◽

Contrast Enhanced Ultrasound ◽

Diagnostic Ability ◽

Contrast Enhanced

AIM: To explore the diagnostic ability of contrast-enhanced ultrasound (CEUS) in distinguishing intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC). MATERIALS AND METHODS: PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched for studies reporting the diagnostic accuracy of CEUS in differentiating ICC from HCC. The diagnostic ability of CEUS was assessed based on the pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and area under the curve (AUC) with 95% confidence intervals (CIs). The methodologic quality was assessed by the QUADAS-2 tool. Subgroup analyses, meta-regression and investigation of publication bias were performed to identify the source of heterogeneity. RESULTS: A total of eight studies were included, consisting of 1,116 patients with HCC and 529 with ICC. The general diagnostic performance of CEUS in distinguishing ICC and HCC were as follows: pooled sensitivity, 0.92 (95% CI: 0.84–0.96); pooled specificity, 0.87 (95% CI: 0.79–0.92); pooled PLR, 7.1 (95% CI: 4.1–12.0); pooled NLR, 0.09 (95% CI: 0.05–0.19); pooled DOR, 76 (95% CI: 26–220) and AUC, 0.95(95% CI: 0.93–0.97). Different liver background may be a potential factor that influenced the diagnostic accuracy of CEUS according to the subgroup analysis, with the pooled DOR of 89.67 in the mixed liver background group and 46.87 in the cirrhosis group, respectively. Six informative CEUS features that may help differentiate HCC from ICC were extracted. The three CEUS features favoring HCC were arterial phase hyperenhancement(APHE), mild washout and late washout (>60s); the three CEUS favoring ICC were arterial rim enhancement, marked washout and early washout(<60s). No potential publication bias was observed. CONCLUSION: CEUS showed great diagnostic ability in differentiating ICC from HCC, which may be promising for noninvasive evaluation of these diseases.

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A Modified TNM Classification for Primary Operable Colorectal Cancer

JNCI Cancer Spectrum ◽

10.1093/jncics/pkaa093 ◽

2020 ◽

Author(s):

Chundong Zhang ◽

Zubing Mei ◽

Junpeng Pei ◽

Masanobu Abe ◽

Xiantao Zeng ◽

...

Keyword(s):

Colorectal Cancer ◽

Validation Cohort ◽

Tnm Classification ◽

Characteristic Curve ◽

External Validation ◽

Model Fitting ◽

Information Criteria ◽

Stage I ◽

Internal Validation ◽

Clinical Benefits

Abstract Background The American Joint Committee on Cancer (AJCC) 8th tumor/node/metastasis (TNM) classification for colorectal cancer (CRC) has limited ability to predict prognosis. Methods We included 45,379 eligible stage I-III CRC patients from the Surveillance, Epidemiology, and End Results Program. Patients were randomly assigned individually to a training (N =31,772) or an internal validation cohort (N =13,607). External validation was performed in 10,902 additional patients. Patients were divided according to T and N stage permutations. Survival analyses were conducted by a Cox proportional hazard model and Kaplan-Meier analysis, with T1N0 as the reference. Area under receiver operating characteristic curve (AUC) and Akaike information criteria (AIC) were applied for prognostic discrimination and model-fitting, respectively. Clinical benefits were further assessed by decision curve analyses. Results We created a modified TNM (mTNM) classification: stages I (T1-2N0-1a), IIA (T1N1b, T2N1b, T3N0), IIB (T1-2N2a-2b, T3N1a-1b, T4aN0), IIC (T3N2a, T4aN1a-2a, T4bN0), IIIA (T3N2b, T4bN1a), IIIB (T4aN2b, T4bN1b), and IIIC (T4bN2a-2b). In the internal validation cohort, compared to the AJCC 8th TNM classification, the mTNM classification showed superior prognostic discrimination (AUC = 0.675 vs. 0.667, respectively; two-sided P <0.001) and better model-fitting (AIC = 70,937 vs. 71,238, respectively). Similar findings were obtained in the external validation cohort. Decision curve analyses revealed that the mTNM had superior net benefits over the AJCC 8th TNM classification in the internal and external validation cohorts. Conclusions The mTNM classification provides better prognostic discrimination than AJCC 8th TNM classification, with good applicability in various populations and settings, to help better stratify stage I-III CRC patients into prognostic groups.

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Performance of Serum Glypican 3 in Diagnosis of Hepatocellular Carcinoma: A meta-analysis

Annals of Hepatology ◽

10.5604/01.3001.0012.2121 ◽

2018 ◽

Vol 17 (5) ◽

pp. 0-10

Author(s):

Dahai Xu ◽

Chang Su ◽

Liang Sun ◽

Yuanyuan Gao ◽

Youjun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Diagnostic Accuracy ◽

Sensitivity And Specificity ◽

Operating Characteristic ◽

Web Of Science ◽

Meta Analysis ◽

Characteristic Curve ◽

Positive Control ◽

Cochrane Library ◽

Non Invasive

Introduction and aim. Serum glypican-3 (GPC3) has been explored as a non-invasive biomarker of hepatocellular carcinoma (HCC). However, controversy remains on its diagnostic accuracy. Therefore, we aimed to conduct a systematic review and metaanalysis to evaluate the differential diagnostic accuracy of serum GPC3 between HCC and liver cirrhosis (LC) cases. Material and methods. After the strict filtering and screening of studies from NCBI, PUBMED, Clinical Trials, Cochrane library, Embase, Prospero and Web of Science databases, 11 studies were selected. All studies provided the sensitivity and specificity of GPC3 and the alpha-fetoprotein (AFP) in the HCC and LC diagnosis. The sensitivity and specificity, and the area under the receiver operating characteristic curve (AUC) were determined and compared between GPC3 and AFP, which was set as a positive control. Results. Pooled sensitivity (95% CI) and specificity (95% CI) were 0.55 (0.52-0.58) and 0.58 (0.54-0.61) for GPC3, 0.54 (0.51-0.57) and 0.83 (0.80-0.85) for AFP, and 0.85 (0.81-0.89) and 0.79 (0.73-0.84) for GPC3 + AFP, respectively. The AUCs of GPC3, AFP and GPC3 + AFP were 0.7793, 0.7867 and 0.9366, respectively. GPC3 had a nearly similar sensitivity as AFP, while the specificity and AUC of GPC3 was lower than that of AFP. The combination of GPC3 and AFP yielded a better sensitivity and AUC than GPC3 or AFP. Conclusion. Serum GPC3 is inferior to AFP in the differential diagnosis between HCC and LC. However, the combination of GPC3 and AFP exhibited a much better performance.

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Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.582788 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ying Zhao ◽

Nan Wang ◽

Jingjun Wu ◽

Qinhe Zhang ◽

Tao Lin ◽

...

Keyword(s):

Risk Factors ◽

Hepatocellular Carcinoma ◽

Logistic Regression ◽

Therapeutic Response ◽

Validation Cohort ◽

Combined Model ◽

Training Cohort ◽

Contrast Enhanced ◽

Three Phase ◽

Radiological Model

PurposeTo investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).MethodsOne hundred and twenty-two HCC patients (objective response, n = 63; non-response, n = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort (n = 85) and a validation cohort (n = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted. Radiomics models were constructed based on each phase and their combination using logistic regression algorithm. A clinical-radiological model was built based on independent risk factors identified by univariate and multivariate logistic regression analyses. A combined model incorporating the radiomics score and selected clinical-radiological predictors was constructed, and the combined model was presented as a nomogram. Prediction models were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.ResultsAmong all radiomics models, the three-phase radiomics model exhibited better performance in the training cohort with an area under the curve (AUC) of 0.838 (95% confidence interval (CI), 0.753 - 0.922), which was verified in the validation cohort (AUC, 0.833; 95% CI, 0.691 - 0.975). The combined model that integrated the three-phase radiomics score and clinical-radiological risk factors (total bilirubin, tumor shape, and tumor encapsulation) showed excellent calibration and predictive capability in the training and validation cohorts with AUCs of 0.878 (95% CI, 0.806 - 0.950) and 0.833 (95% CI, 0.687 - 0.979), respectively, and showed better predictive ability (P = 0.003) compared with the clinical-radiological model (AUC, 0.744; 95% CI, 0.642 - 0.846) in the training cohort. A nomogram based on the combined model achieved good clinical utility in predicting the treatment efficacy of TACE.ConclusionCE-MRI radiomics analysis may serve as a promising and noninvasive tool to predict therapeutic response to TACE in HCC, which will facilitate the individualized follow-up and further therapeutic strategies guidance in HCC patients.

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Nomogram for Prediction of the International Study Group of Liver Surgery (ISGLS) Grade B/C Posthepatectomy Liver Failure in HBV-related Hepatocellular carcinoma Patients: an external validation and prospective application study

10.21203/rs.3.rs-20106/v1 ◽

2020 ◽

Author(s):

Jiazhou Ye ◽

Rong-yun Mai ◽

Wei-xing Guo ◽

Yan-yan Wang ◽

Liang Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Validation Cohort ◽

External Validation ◽

International Study ◽

Study Group ◽

Internal Validation ◽

Predictive Values ◽

Posthepatectomy Liver Failure ◽

International Study Group ◽

Prospective Application

Abstract Background & Aims: To develop a nomogram for predicting the International Study Group of Liver Surgery (ISGLS) grade B/C posthepatectomy liver failure (PHLF) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients. Methods: Patients initially treated with hepatectomy were included. Univariate regression analysis and stochastic forest algorithm were applied to extract the core indicators and reduce redundancy bias. The nomogram was then constructed by using multivariate logistic regression, and validated in internal and external cohorts, and a prospective clinical application. Results: There were 900, 300 and 387 participants in training, internal and external validation cohorts, with the morbidity of grade B/C PHLF were 13.5%, 11.6% and 20.2%, respectively. The nomogram was generated by integrating preoperative total bilirubin, platelet count, prealbumin, aspartate aminotransferase, prothrombin time and standard future liver remnant volume, then achieved good prediction performance in training (AUC=0.868, 95%CI=0.808–0.880), internal validation (AUC=0.868, 95%CI=0.794–0.916) and external validation cohorts (AUC=0.820, 95%CI=0.756–0.861), with well-fitted calibration curves. Negative predictive values were significantly higher than positive predictive values in training cohort (97.6% vs. 33.0%), internal validation cohort (97.4% vs. 25.9%) and external validation cohort (94.3% vs. 41.1%), respectively. Patients who had a nomogram score <169 or ≧169 were considered to have low or high risk of grade B/C PHLF. Prospective application of the nomogram accurately predicted grade B/C PHLF in clinical practise. Conclusions: The nomogram has a good performance in predicting ISGLS grade B/C PHLF in HBV-related HCC patients and determining appropriate candidates for hepatectomy.

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Nomogram for prediction of the international study Group of Liver Surgery (ISGLS) grade B/C Posthepatectomy liver failure in HBV-related hepatocellular carcinoma patients: an external validation and prospective application study

BMC Cancer ◽

10.1186/s12885-020-07480-2 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Jia-zhou Ye ◽

Rong-yun Mai ◽

Wei-xing Guo ◽

Yan-yan Wang ◽

Liang Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Validation Cohort ◽

External Validation ◽

International Study ◽

Study Group ◽

Internal Validation ◽

Predictive Values ◽

Posthepatectomy Liver Failure ◽

International Study Group ◽

Prospective Application

Abstract Background To develop a nomogram for predicting the International Study Group of Liver Surgery (ISGLS) grade B/C posthepatectomy liver failure (PHLF) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients. Methods Patients initially treated with hepatectomy were included. Univariate regression analysis and stochastic forest algorithm were applied to extract the core indicators and reduce redundancy bias. The nomogram was then constructed by using multivariate logistic regression, and validated in internal and external cohorts, and a prospective clinical application. Results There were 900, 300 and 387 participants in training, internal and external validation cohorts, with the morbidity of grade B/C PHLF were 13.5, 11.0 and 20.2%, respectively. The nomogram was generated by integrating preoperative total bilirubin, platelet count, prealbumin, aspartate aminotransferase, prothrombin time and standard future liver remnant volume, then achieved good prediction performance in training (AUC = 0.868, 95%CI = 0.836–0.900), internal validation (AUC = 0.868, 95%CI = 0.811–0.926) and external validation cohorts (AUC = 0.820, 95%CI = 0.756–0.861), with well-fitted calibration curves. Negative predictive values were significantly higher than positive predictive values in training cohort (97.6% vs. 33.0%), internal validation cohort (97.4% vs. 25.9%) and external validation cohort (94.3% vs. 41.1%), respectively. Patients who had a nomogram score < 169 or ≧169 were considered to have low or high risk of grade B/C PHLF. Prospective application of the nomogram accurately predicted grade B/C PHLF in clinical practise. Conclusions The nomogram has a good performance in predicting ISGLS grade B/C PHLF in HBV-related HCC patients and determining appropriate candidates for hepatectomy.

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An MR-Based Radiomics Model for Differentiation Between Hepatocellular Carcinoma and Focal Nodular Hyperplasia in Non-Cirrhotic Liver

10.21203/rs.3.rs-159301/v1 ◽

2021 ◽

Author(s):

Zongren Ding ◽

Kongying Lin ◽

Jun Fu ◽

Qizhen Huang ◽

Guoxu Fang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Focal Nodular Hyperplasia ◽

Cirrhotic Liver ◽

Arterial Phase ◽

Training Set ◽

Combined Model ◽

Clinical Model ◽

Contrast Enhanced ◽

Nodular Hyperplasia ◽

Validation Set

Abstract Purpose:This study aimed to develop and validate a radiomics model for differentiating between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI).Methods:We retrospectively enrolled 149 HCC patients and 75 FNH patients seen between May 2015 and May 2019 at our center and randomly allocated patients to a training set (n = 156) and a validation set (n = 68). A total of 2,260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forests, and the least absolute shrinkage and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the three models was compared. Results:Eight radiomics features were chosen to build a radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen to build the clinical model. When evaluating the performance of three models, the clinical model that included clinical data and visual MRI findings achieved excellent performance in the training set (AUC, 0.937; 95% CI, 0.887–0.970) and the validation set (AUC, 0.903; 95% CI, 0.807–0.962), and there was no significant difference between the radiomics model and the clinical model. The AUC of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p = 0.002) and validation (0.972 vs. 0.903, p = 0.032) sets.Conclusions:The combined model based on clinical and radiomics features can well distinguish HCC from FNH in non-cirrhotic liver. Our model may assist clinicians in the clinical decision-making process.

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Plasma extracellular vesicle long RNA profiling identifies a diagnostic signature for the detection of pancreatic ductal adenocarcinoma

Gut ◽

10.1136/gutjnl-2019-318860 ◽

2019 ◽

Vol 69 (3) ◽

pp. 540-550 ◽

Cited By ~ 13

Author(s):

Shulin Yu ◽

Yuchen Li ◽

Zhuan Liao ◽

Zheng Wang ◽

Zhen Wang ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Validation Cohort ◽

Characteristic Curve ◽

External Validation ◽

Extracellular Vesicle ◽

Carbohydrate Antigen ◽

Superior Performance ◽

Ductal Adenocarcinoma ◽

Support Vector ◽

Internal Validation

ObjectivePancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose at resectable stage. Recent studies have suggested that extracellular vesicles (EVs) contain long RNAs. The aim of this study was to develop a diagnostic (d-)signature for the detection of PDAC based on EV long RNA (exLR) profiling.DesignWe conducted a case-control study with 501 participants, including 284 patients with PDAC, 100 patients with chronic pancreatitis (CP) and 117 healthy subjects. The exLR profile of plasma samples was analysed by exLR sequencing. The d-signature was identified using a support vector machine algorithm and a training cohort (n=188) and was validated using an internal validation cohort (n=135) and an external validation cohort (n=178).ResultsWe developed a d-signature that comprised eight exLRs, including FGA, KRT19, HIST1H2BK, ITIH2, MARCH2, CLDN1, MAL2 and TIMP1, for PDAC detection. The d-signature showed high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.960, 0.950 and 0.936 in the training, internal validation and external validation cohort, respectively. The d-signature was able to identify resectable stage I/II cancer with an AUC of 0.949 in the combined three cohorts. In addition, the d-signature showed superior performance to carbohydrate antigen 19-9 in distinguishing PDAC from CP (AUC 0.931 vs 0.873, p=0.028).ConclusionThis study is the first to characterise the plasma exLR profile in PDAC and to report an exLR signature for the detection of pancreatic cancer. This signature may improve the prognosis of patients who would have otherwise missed the curative treatment window.

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An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02266-7 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Zongren Ding ◽

Kongying Lin ◽

Jun Fu ◽

Qizhen Huang ◽

Guoxu Fang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Focal Nodular Hyperplasia ◽

Clinical Decision Making ◽

Cirrhotic Liver ◽

Arterial Phase ◽

Combined Model ◽

Clinical Model ◽

Portal Venous Phase ◽

Contrast Enhanced ◽

Nodular Hyperplasia

Abstract Purpose We aimed to develop and validate a radiomics model for differentiating hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI). Methods We retrospectively enrolled 149 HCC and 75 FNH patients treated between May 2015 and May 2019 at our center. Patients were randomly allocated to a training (n=156) and validation set (n=68). In total, 2260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forest, least absolute shrinkage, and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the models was compared. Results Eight radiomics features were chosen for the radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen for the clinical model. A combined model was built using the factors from the previous models. The classification accuracy of the combined model differentiated HCC from FNH in both the training and validation sets (0.956 and 0.941, respectively). The area under the receiver operating characteristic curve of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p=0.002) and validation (0.972 vs. 0.903, p=0.032) sets. Conclusions The combined model provided a non-invasive quantitative method for differentiating HCC from FNH in non-cirrhotic liver with high accuracy. Our model may assist clinicians in the clinical decision-making process.

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Real-life assessment of standardized contrast-enhanced ultrasound (CEUS) and CEUS algorithms (CEUS LI-RADS®/ESCULAP) in hepatic nodules in cirrhotic patients—a prospective multicenter study

European Radiology ◽

10.1007/s00330-021-07872-3 ◽

2021 ◽

Author(s):

D. Strobel ◽

E.-M. Jung ◽

M. Ziesch ◽

M. Praktiknjo ◽

A. Link ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Diagnostic Accuracy ◽

Predictive Value ◽

Real Life ◽

Life Assessment ◽

Arterial Phase ◽

Contrast Enhanced Ultrasound ◽

Non Invasive ◽

Contrast Enhanced ◽

Cirrhotic Patients

Abstract Objectives Hepatocellular carcinoma (HCC) can be diagnosed non-invasively with contrast-enhanced ultrasound (CEUS) in cirrhosis if the characteristic pattern of arterial phase hyperenhancement followed by hypoenhancement is present. Recent studies suggest that diagnosis based on this “hyper-hypo” pattern needs further refinement. This study compares the diagnostic accuracies of standardized CEUS for HCC according to the current guideline definition and following the newly developed CEUS algorithms (CEUS LI-RADS®, ESCULAP) in a prospective multicenter real-life setting. Methods Cirrhotic patients with liver lesions on B-mode ultrasound were recruited prospectively from 04/2018 to 04/2019, and clinical and imaging data were collected. The CEUS standard included an additional examination point after 4–6 min in case of no washout after 3 min. The diagnostic accuracies of CEUS following the guidelines (“hyper-hypo” pattern), based on the examiner’s subjective interpretation (“CEUS subjective”), and based on the CEUS algorithms ESCULAP and CEUS LI-RADS® were compared. Results In total, 470 cirrhotic patients were recruited in 43 centers. The final diagnosis was HCC in 378 cases (80.4%) according to the reference standard (histology 77.4%, MRI 16.4%, CT 6.2%). The “hyper-hypo” pattern yielded 74.3% sensitivity and 63% specificity. “CEUS subjective” showed a higher diagnostic accuracy (sensitivity, 91.5%; specificity, 67.4%; positive predictive value, 92%; negative predictive value, 66%). Sensitivity was higher for ESCULAP (95%) and “CEUS subjective” (91.5%) versus CEUS LI-RADS® (65.2%; p < 0.001). Specificity was highest for CEUS LI-RADS® (78.6%; p < 0.001). Conclusions CEUS has an excellent diagnostic accuracy for the non-invasive diagnosis of HCC in cirrhosis. CEUS algorithms may be a helpful refinement of the “hyper-hypo” pattern defined by current HCC guidelines. Key Points • Contrast-enhanced ultrasound (CEUS) has a high diagnostic accuracy for the non-invasive diagnosis of hepatocellular carcinoma (HCC) in cirrhosis. • The CEUS algorithm ESCULAP (Erlanger Synopsis for Contrast-enhanced Ultrasound for Liver lesion Assessment in Patients at risk) showed the highest sensitivity, whereas the CEUS LI-RADS® (Contrast-Enhanced UltraSound Liver Imaging Reporting and Data System) algorithm yielded the highest specificity. • A standardized CEUS examination procedure with an additional examination point in the late phase, after 4–6 min in lesions with no washout after 3 min, is vital.

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