Profiling hepatocellular carcinoma aggressiveness with contrast-enhanced ultrasound and gadoxetate disodium-enhanced MRI: an intra-individual comparative study based on the Liver Imaging Reporting and Data System

Inter-reader agreement for CT/MRI LI-RADS category M imaging features: a systematic review and meta-analysis

BACKGROUNDS/AIMS

To systematically evaluate inter-reader agreement in the assessment of individual liver imaging reporting and data system (LI-RADS) category M (LR-M) imaging features in computed tomography/magnetic resonance imaging (CT/MRI) LIRADS v2018, and to explore the causes of poor agreement in LR-M assignment.

METHODS

Original studies reporting inter-reader agreement for LR-M features on multiphasic CT or MRI were identified using the MEDLINE, EMBASE, and Cochrane databases. The pooled kappa coefficient (κ) was calculated using the DerSimonian-Laird random-effects model. Heterogeneity was assessed using Cochran's Q test and I2 statistics. Subgroup meta-regression analyses were conducted to explore the study heterogeneity.

RESULTS

In total, 24 eligible studies with 5,163 hepatic observations were included. The pooled κ values were 0.72 (95% confidence interval [CI], 0.65-0.78) for rim arterial phase hyperenhancement, 0.52 (95% CI, 0.39-0.65) for peripheral washout, 0.60 (95% CI, 0.50-0.70) for delayed central enhancement, 0.68 (95% CI, 0.57-0.78) for targetoid restriction, 0.74 (95% CI, 0.65-0.83) for targetoid transitional phase/hepatobiliary phase appearance, 0.64 (95% CI, 0.49-0.78) for infiltrative appearance, 0.49 (95% CI, 0.30-0.68) for marked diffusion restriction, and 0.61 (95% CI, 0.48-0.73) for necrosis or severe ischemia. Substantial study heterogeneity was observed for all LR-M features (Cochran's Q test, P<0.01; I2≥89.2%). Studies with a mean observation size of <3 cm, those performed using 1.5-T MRI, and those with multiple image readers, were significantly associated with poor agreement of LR-M features.

CONCLUSIONS

The agreement for peripheral washout and marked diffusion restriction was limited. The LI-RADS should focus on improving the agreement of LR-M features.

Intraindividual comparison of CT and MRI for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma

OBJECTIVES

To establish and validate scoring models for predicting vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) using computed tomography (CT) and magnetic resonance imaging (MRI), and to intra-individually compare the predictive performance between the two modalities.

METHODS

We retrospectively included 324 patients with surgically confirmed HCC who underwent preoperative dynamic CT and MRI with extracellular contrast agent between June 2019 and August 2020. These patients were then divided into a discovery cohort (n = 227) and a validation cohort (n = 97). Imaging features and Liver Imaging Reporting and Data System (LI-RADS) categories of VETC-positive HCCs were evaluated. Logistic regression analyses were conducted on the discovery cohort to identify clinical and imaging predictors associated with VETC-positive cases. Subsequently, separate CT-based and MRI-based scoring models were developed, and their diagnostic performance was compared using generalized estimating equations.

RESULTS

On both CT and MRI, VETC-positive HCCs exhibited a higher frequency of size > 5.0 cm, necrosis or severe ischemia, non-smooth tumor margin, targetoid appearance, intratumor artery, and heterogeneous enhancement with septations or irregular ring-like structure compared to VETC-negative HCCs (all p < 0.05). Regarding LI-RADS categories, VETC-positive HCCs were more frequently categorized as LR-M than VETC-negative cases (all p < 0.05). In the validation cohort, the CT-based model showed similar sensitivity (76.7% vs. 86.7%, p = 0.375), specificity (83.6% vs. 74.6%, p = 0.180), and area under the curve value (0.80 vs. 0.81, p = 0.910) to the MRI-based model in predicting VETC-positive HCCs.

CONCLUSION

Preoperative CT and MRI demonstrated comparable performance in the identification of VETC-positive HCCs, thus displaying promising predictive capabilities.

CLINICAL RELEVANCE STATEMENT

Both computed tomography and magnetic resonance imaging demonstrated promise in preoperatively identifying the vessel-encapsulating tumor cluster pattern in hepatocellular carcinoma, with no statistically significant difference between the two modalities, potentially adding additional prognostic value.

KEY POINTS

Computed tomography (CT) and magnetic resonance imaging (MRI) show promise in the preoperative identification of vessels encapsulating tumor clusters-positive hepatocellular carcinoma (HCC). HCC with vessels encapsulating tumor cluster patterns were more frequently LR-M compared to those without. These CT and MRI models showed comparable ability in identifying vessels encapsulating tumor clusters-positive HCC.

Establishment of nomogram prediction model of contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for vessels encapsulating tumor clusters pattern of hepatocellular carcinoma

To establish clinical prediction models of vessels encapsulating tumor clusters (VETC) pattern using preoperative contrast-enhanced ultrasound (CEUS) and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid magnetic resonance imaging (EOB-MRI) in patients with hepatocellular carcinoma (HCC). A total of 111 resected HCC lesions from 101 patients were included. Preoperative imaging features of CEUS and EOB-MRI, postoperative recurrence, and survival information were collected from medical records. The best subset regression and multivariable Cox regression were used to select variables to establish the prediction model. The VETC-positive group had a statistically lower survival rate than the VETC-negative group. The selected variables were peritumoral enhancement in the arterial phase (AP), hepatobiliary phase (HBP) on EOB-MRI, intratumoral branching enhancement in the AP of CEUS, intratumoral hypoenhancement in the portal phase of CEUS, incomplete capsule, and tumor size. A nomogram was developed. High and low nomogram scores with a cutoff value of 168 points showed different recurrence-free survival rates and overall survival rates. The area under the curve (AUC) and accuracy were 0.804 and 0.820, respectively, indicating good discrimination. Decision curve analysis showed a good clinical net benefit (threshold probability > 5%), while the Hosmer-Lemeshow test yielded excellent calibration (P = 0.6759). The AUC of the nomogram model combining EOB-MRI and CEUS was higher than that of the models with EOB-MRI factors only (0.767) and CEUS factors only (0.7). The nomogram verified by bootstrapping showed AUC and calibration curves similar to those of the nomogram model. The Prediction model based on CEUS and EOB-MRI is effective for preoperative noninvasive diagnosis of VETC.

[Contrast-enhanced ultrasound (CEUS) for characterisation of focal liver lesions]

Due to the trend towards increased use of imaging and rising awareness among high-risk patients, gastroenterologists and hepatologists are more frequently confronted with patients with focal liver lesions. In the differentiation of these lesions, CT and MRI have increasingly found their way into primary diagnostic steps in everyday clinical practice. Contrast-enhanced sonography, on the other hand, is a very effective and cost-efficient method for assessing focal liver lesions. The success of the method is not only based on the visualisation of microvascularisation in real time. If sonography is performed by the treating physician, he can use the exact knowledge of history and clinical findings to specifically adapt the examination procedure and to interpret the sonographic findings with greater accuracy ("clinical sonography"). At the same time, the method enables the practitioner to combine diagnostics and management decisions in his or her own hands. To achieve excellent results with contrast-enhanced sonography-as with any other imaging method-it is necessary that the examiner is sufficiently qualified.This article systematically presents the sonographic characteristics of the most common liver lesions and clearly shows their contrast patterns using videos (available via QR code). The article illustrates that CEUS could-and from the authors' point of view, should-have an even greater significance in the future.

The comparison of contrast-enhanced ultrasound and gadoxetate disodium-enhanced MRI LI-RADS for nodules ≤2 cm in patients at high risk for HCC: a prospective study

Objectives

To investigate the consistency of LI-RADS of CEUS and EOB-MRI in the categorization of liver nodules ≤2cm in patients at high risk for HCC.

Methods

Patients at high risk for HCC with nodules ≤2cm who underwent CEUS and EOB-MRI in our hospital were prospectively enrolled. The CEUS images and EOB-MRI imaging of each liver nodule were observed to evaluate inter-observer consistency and category according to CEUS LI-RADS V2017 and CT/MRI LI-RADS V2017 criteria double blinded. Pathology and/or follow-up were used as reference standard.

Results

A total of 127 nodules in 119 patients met the inclusion criteria. The inter-observer agreement was good on CEUS and EOB-MRI LI-RADS (kappa = 0.76, 0.76 p < 0.001). The inter-modality agreement was fair (kappa=0.21, p < 0.001). There was no statistical difference in PPV and specificity between CEUS and EOB-MRI LR-5 for HCC, while the difference in AUC was statistically significant. We used new criteria (CEUS LR-5 and EOB-MRI LR-4/5 or CEUS LR-4/5 and EOB-MRI LR-5) to diagnose HCC. The sensitivity, specificity, and AUC of this criteria was 63.4%, 95.6%, and 0.80.

Conclusions

CEUS and EOB-MRI showed fair inter-modality agreement in LI-RADS categorization of nodules ≤2 cm. The inter-observer agreement of CEUS and EOB-MRI LI-RADS were substantial. CEUS and EOB-MRI LR-5 have equally good positive predictive value and specificity for HCC ≤ 2cm, and combining these two modalities may better diagnose HCC ≤ 2 cm.

Clinical Trial Registration

https://clinicaltrials.gov/, identifier NCT04212286.

Dynamic contrast-enhanced ultrasonography with sonazoid predicts microvascular invasion in early-stage hepatocellular carcinoma

OBJECTIVE

Microvascular invasion (MVI) is an independent risk factor for the early recurrence and poor survival of hepatocellular carcinoma (HCC). This study aims to investigate the potential clinical value of dynamic contrast-enhanced ultrasound (DCE-ultrasound)-Sonazoid in pre-operatively assessing MVI in HCC.

METHODS AND MATERIALS

This single centre prospective study included 140 patients with histopathologically confirmed single HCC lesions. Patients were classified according to the post-operative pathological information presence of MVI: MVI+ group (n = 32) and MVI- group (n = 108). All patients underwent DCE-ultrasound within 1 week before surgery. The quantitative perfusion parameters of HCC lesions, margins of HCC lesions, and distal liver parenchyma were obtained and analyzed.

RESULTS

Clinicopathological (serum alpha-fetoprotein, Des-gamma-carboxyprothrombin, and pathological grade) and grayscale imaging features (tumor size) were significantly different between the MVI+ and MVI- groups (p < 0.05). Further quantitative analysis showed that when comparing the MVI+ and MVI- groups, half-decrease time and wash-out rate of HCC lesions and peak enhancement in the arterial phase of difference between the margin area of HCC and distal liver parenchyma were significantly different (p = 0.045, p = 0.035, and p = 0.023, respectively). Combining the above three quantitative parameters, the accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 69.3% (97/140), 37.8% (17/45), 84.3% (80/95), 53.1% (17/32), 74.1% (80/108), respectively.

CONCLUSION

DCE-ultrasound with quantitative perfusion analysis has the potential to predict MVI in HCC lesions.

ADVANCES IN KNOWLEDGE

DCE-ultrasound with quantitative perfusion analysis has the potential to predict MVI in HCC lesions.

Comparison of non-radiomics imaging features and radiomics models based on contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma within 5 cm

OBJECTIVES

The purpose of this study is to establish microvascular invasion (MVI) prediction models based on preoperative contrast-enhanced ultrasound (CEUS) and ethoxybenzyl-enhanced magnetic resonance imaging (EOB-MRI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.

METHODS

Patients with a single HCC ≤ 5 cm and accepting CEUS and EOB-MRI before surgery were enrolled in this study. Totally, 85 patients were randomly divided into the training and validation cohorts in a ratio of 7:3. Non-radiomics imaging features, the CEUS and EOB-MRI radiomics scores were extracted from the arterial phase, portal phase and delayed phase images of CEUS and the hepatobiliary phase images of EOB-MRI. Different MVI predicting models based on CEUS and EOB-MRI were constructed and their predictive values were evaluated.

RESULTS

Since univariate analysis revealed that arterial peritumoral enhancement on the CEUS image, CEUS radiomics score, and EOB-MRI radiomics score were significantly associated with MVI, three prediction models, namely the CEUS model, the EOB-MRI model, and the CEUS-EOB model, were developed. In the validation cohort, the areas under the receiver operating characteristic curve of the CEUS model, the EOB-MRI model, and the CEUS-EOB model were 0.73, 0.79, and 0.86, respectively.

CONCLUSIONS

Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm.

CLINICAL RELEVANCE STATEMENT

Radiomics models based on CEUS and EOB-MRI are effective for MVI predicting and conducive to pretreatment decision-making in patients with a single HCC within 5 cm.

KEY POINTS

• Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. • There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm.

Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system

Purposes

This study aimed to establish a predictive model of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) by contrast-enhanced computed tomography (CT), which relied on a combination of machine learning approach and imaging features covering Liver Imaging and Reporting and Data System (LI-RADS) features.

Methods

The retrospective study included 279 patients with surgery who underwent preoperative enhanced CT. They were randomly allocated to training set, validation set, and test set (167 patients vs. 56 patients vs. 56 patients, respectively). Significant imaging findings for predicting MVI were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression method. Predictive models were performed by machine learning algorithm, support vector machine (SVM), in the training set and validation set, and evaluated in the test set. Further, a combined model adding clinical findings to the radiologic model was developed. Based on the LI-RADS category, subgroup analyses were conducted.

Results

We included 116 patients with MVI which were diagnosed through pathological confirmation. Six imaging features were selected about MVI prediction: four LI-RADS features (corona enhancement, enhancing capsule, non-rim aterial phase hyperehancement, tumor size) and two non-LI-RADS features (internal arteries, non-smooth tumor margin). The radiological feature with the best accuracy was corona enhancement followed by internal arteries and tumor size. The accuracies of the radiological model and combined model were 0.725–0.714 and 0.802–0.732 in the training set, validation set, and test set, respectively. In the LR-4/5 subgroup, a sensitivity of 100% and an NPV of 100% were obtained by the high-sensitivity threshold. A specificity of 100% and a PPV of 100% were acquired through the high specificity threshold in the LR-M subgroup.

Conclusion

A combination of LI-RADS features and non-LI-RADS features and serum alpha-fetoprotein value could be applied as a preoperative biomarker for predicting MVI by the machine learning approach. Furthermore, its good performance in the subgroup by LI-RADS category may help optimize the management of HCC patients.