Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.

Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is crucial to define the extent of the disease and, when suitable, the consequent resective or ablative procedure, in order to guarantee a radical treatment without sacrificing an excessive volume of healthy liver. Once mainly performed manually, with notable cost in terms of time and human energies, vessel segmentation is currently realized through the application of artificial intelligence (AI), which has gained increased interest and development of the field. Many different AI-driven models adopted for this aim have been described and can be grouped into different categories: thresholding methods, edge- and region-based methods, model-based methods, and machine learning models. The latter includes neural network and deep learning models that now represent the principal algorithms exploited for vessel segmentation. The present narrative review describes how liver vessel segmentation can be realized through AI models, with a summary of model results in terms of accuracy, and an overview on the future progress of this topic.

Detection of hepatocellular carcinoma feeding vessels: MDCT angiography with 3D reconstruction versus digital subtraction angiography.

BACKGROUND: Accurate detection of Hepatocellular carcinoma (HCC) feeding vessels during transcatheter arterial chemoembolization (TACE) is important for an effective treatment, while limiting non-target embolization. This study aimed to investigate the feasibility and accuracy of pre-TACE three dimensional (3D) CT angiography for tumor-feeding vessels detection compared to DSA. METHODS: Sixty-nine consecutive patients referred for TACE from May 2022 to May 2023 were included. (3D) CT images were reconstructed from the pre-TACE diagnostic multiphasic contrast enhanced CT images and compared with non-selective digital subtraction angiography (DSA) images obtained during TACE for detection of HCC feeding vessels. A "Ground truth" made by consensus between observers after reviewing all available pre-TACE CT images, and DSA and CBCT images during TACE to detect the true feeding vessels was the gold standard. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), accuracy and ROC curve with AUC were calculated for each modality and compared. RESULTS: A total of 136 active HCCs were detected in the 69 consecutive patients included in the study. 185 feeding arteries were detected by 3D CT and DSA and included in the analysis. 3D CT detection of feeding arteries revealed mean sensitivity, specificity, PPV, NPV and accuracy of 91%, 71%, 98%, 36%, and 90%, respectively, with mean AUC = 0.81. DSA detection of feeding arteries revealed mean sensitivity, specificity, PPV, NPV, and accuracy of 80%, 58%, 96.5%, 16.5% and 78%, respectively, with mean AUC = 0.69. CONCLUSIONS: Pre-TACE 3D CT angiography has shown promise in improving the detection of HCC feeding vessels compared to DSA. However, further studies are required to confirm these findings across different clinical settings and patient populations. TRIAL REGISTRATION: This study was prospectively registered at Clinicaltrials.gov with ID NCT05304572; Date of registration: 2-4-2022.

The value of contrast-enhanced ultrasound combined with microflow imaging in predicting microvascular invasion of hepatocellular carcinoma before operation.

OBJECTIVE: To evaluate the preoperative predictive value of contrast-enhanced ultrasound (CEUS) combined with microflow imaging (MFI) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHODS: In our study, 80 patients with HCC were analyzed retrospectively. According to the gold standard of postoperative pathology, the patients were divided into MVI positive group (n = 39) and MVI negative group (n = 41). we were to analyze the correlation between CEUS and MVI in combination with MFI, to identify independent risk factors for the occurrence of MVI positive, and to analyze the predictive efficacy of every independent risk factor and their combination in preoperative prediction of MVI. RESULTS: In our study, 80 patients were enrolled, including 39 patients in the MVI-positive group and 41 patients in the MVI-negative group, with a MVI-positive rate of 48.8%. By univariate analysis and multivariate analysis, it was found that there were statistically significant differences in enhancement range extension, start time of wash out and CEUS-MFI between the two groups, which were independent risk factors for MVI-positive. The combination of three independent risk factors is more effective than single one in predicting MVI of HCC. CONCLUSIONS: CEUS combined with MFI is feasible for the preoperative prediction of MVI in HCC, and can provides meaningful help for individualized clinical treatment.

Vessel-targeted compensation of deformable motion in interventional cone-beam CT.

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm-1 to 0.233 mm-1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm-1 before to 0.205 mm-1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.

Clinical application of dual-layer spectral CT multi-parameter feature to predict microvascular invasion in hepatocellular carcinoma.

OBJECTIVE: This study aimed to investigate the feasibility of using dual-layer spectral CT multi-parameter feature to predict microvascular invasion of hepatocellular carcinoma. METHODS: This retrospective study enrolled 50 HCC patients who underwent multiphase contrast-enhanced spectral CT studies preoperatively. Combined clinical data, radiological features with spectral CT quantitative parameter were constructed to predict MVI. ROC was applied to identify potential predictors of MVI. The CT values obtained by simulating the conventional CT scans with 70 keV images were compared with those obtained with 40 keV images. RESULTS: 50 hepatocellular carcinomas were detected with 30 lesions (Group A) with microvascular invasion and 20 (Group B) without. There were significant differences in AFP,tumer size, IC, NIC,slope and effective atomic number in AP and ICrr in VP between Group A ((1000(10.875,1000),4.360±0.3105, 1.7750 (1.5350,1.8825) mg/ml, 0.1785 (0.1621,0.2124), 2.0362±0.2108,8.0960±0.1043,0.2830±0.0777) and Group B (4.750(3.325,20.425),3.190±0.2979,1.4700 (1.4500,1.5775) mg/ml, 0.1441 (0.1373,0.1490),1.8601±0.1595, 7.8105±0.7830 and 0.2228±0.0612) (all p < 0.05). Using 0.1586 as the threshold for NIC, one could obtain an area-under-curve (AUC) of 0.875 in ROC to differentiate between tumours with and without microvascular invasion. AUC was 0.625 with CT value at 70 keV and improved to 0.843 at 40 keV. CONCLUSION: Dual-layer spectral CT provides additional quantitative parameters than conventional CT to enhance the differentiation between hepatocellular carcinoma with and without microvascular invasion. Especially, the normalized iodine concentration (NIC) in arterial phase has the greatest potential application value in determining whether microvascular invasion exists, and can offer an important reference for clinical treatment plan and prognosis assessment.

Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT.

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.

Dual-energy computed tomography iodine quantification combined with laboratory data for predicting microvascular invasion in hepatocellular carcinoma: a two-centre study.

OBJECTIVES: Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction. METHODS: This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score. RESULTS: Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively. CONCLUSIONS: The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively. ADVANCES IN KNOWLEDGE: Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours.

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm.

BACKGROUND: CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice. PURPOSE: Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images. METHODS: This retrospective study was performed to develop a coarse-to-fine DL-based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast-enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL-based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared. RESULTS: Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52 mm. DL-segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p < 0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p < 0.001). CONCLUSIONS: The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.

MRI-based clinical-radiomics nomogram model for predicting microvascular invasion in hepatocellular carcinoma.

BACKGROUND: Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients. PURPOSE: Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHOD: The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC). RESULT: Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance. CONCLUSION: This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis.

Two-dimensional perfusion angiography permits direct visualization of redistribution of flow in hepatocellular carcinoma during b-TACE.

OBJECTIVES: To demonstrate in vivo redistribution of the blood flow towards HCC's lesions by utilizing two-dimensional perfusion angiography in b-TACE procedures. MATERIAL AND METHODS: In total, 30 patients with 35 HCC nodules treated in the period between January 2019 and November 2021. For each patient, a post-processing software leading to a two-dimensional perfusion angiography was applied on each angiography performed via balloon microcatheter, before and after inflation. On the colour map obtained, reflecting the evolution of contrast intensity change over time, five regions of interests (ROIs) were assessed: one on the tumour (ROI-t), two in the immediate peritumoural healthy liver parenchyma (ROI-ihl) and two in the peripheral healthy liver parenchyma (ROI-phl). The results have been interpreted with a novel in silico model that simulates the hemodynamics of the hepatic arterial system. RESULTS: Among the ROIs drawn inside the same segment of target lesion, the time-to-peak of the ROI-t and of the ROI-ihl have a significantly higher mean value when the balloon was inflated compared with the ROIs obtained with deflated balloon (10.33 ± 3.66 s vs 8.87 ± 2.60 s (p = 0.015) for ROI-t; 10.50 ± 3.65 s vs 9.23 ± 2.70 s (p = 0.047) for ROI-ihl). The in silico model prediction time-to-peak delays when balloon was inflated, match with those observed in vivo. The numerical flow analysis shows how time-to-peak delays are caused by the obstruction of the balloon-occluded artery and the opening of intra-hepatic collateral. CONCLUSION: The measurements identify predictively the flow redistribution in the hepatic arteries during b-TACE, supporting a proper positioning of the balloon microcatheter.

Prediction of vessels encapsulating tumor clusters pattern and prognosis of hepatocellular carcinoma based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid magnetic resonance imaging.

BACKGROUND: Vessels encapsulating tumor clusters (VETC) is a novel vascular pattern distinct from microvascular invasion that is significantly associated with poor prognosis in patients with hepatocellular carcinoma (HCC). This study aimed to predict the VETC pattern and prognosis of patients with HCC based on preoperative gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) magnetic resonance imaging (MRI). METHODS: Patients with HCC who underwent surgical resection and preoperative Gd-EOB-DTPA MRI between January 1, 2016 and August 31, 2022 were retrospectively included. The variables associated with VETC were evaluated using logistic regression. A nomogram model was constructed on the basis of independent risk factors. COX regression was used to determine the variables associated with recurrence-free survival (RFS). RESULTS: A total of 98 patients with HCC were retrospectively included. Peritumoral hypointensity on the hepatobiliary phase (HBP) (odd ratio [OR], 2.58; 95% CI, 1.05-6.33; P = .04), tumor-to-liver signal intensity ratio on HBP of ≤0.75 (OR, 27.80; 95% CI, 1.53-502.91; P = .02), and tumor-to-liver apparent diffusion coefficient ratio of ≤1.23 (OR, 4.65; 95% CI, 1.01-21.38; P = .04) were independent predictors of VETC pattern. A nomogram was constructed by combining the aforementioned 3 significant variables. The accuracy, sensitivity, and specificity were 69.79%, 71.74%, and 68.00%, respectively, with an area under the receiver operating characteristic curve of 0.75 (95% CI, 0.65-0.83). The variables significantly associated with RFS of patients with HCC after surgery were Barcelona Clinic Liver Cancer stage (hazard ratio [HR], 2.15; 95% CI, 1.09-4.22; P = .03) and VETC pattern (HR, 2.28; 95% CI, 1.29-4.02; P = .004). CONCLUSION: The preoperative imaging features based on Gd-EOB-DTPA MRI can be used to predict the VETC pattern, which has prognostic significance for postoperative RFS of patients with HCC.

Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-based graph convolutional network model integrated with nomogram.

OBJECTIVES: Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram. METHODS: We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram. RESULTS: 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927). CONCLUSIONS: The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options. ADVANCES IN KNOWLEDGE: GCN based on MRI could predict MVI on HCC.

Perfusion of hepatocellular carcinomas measured by diffusion-derived vessel density biomarker: Higher hepatocellular carcinoma perfusion than earlier intravoxel incoherent motion reports.

Diffusion-derived vessel density (DVDD) is a physiological surrogate of the area of microvessels per unit tissue area. DDVD is calculated according to DDVD(b0b2) = Sb0/ROIarea0 - Sb2/ROIarea2, where Sb0 and Sb2 refer to the liver signal when b is 0 or 2 s/mm2. Pathohistological studies and contrast-enhanced CT/MRI data showed higher blood volume in hepatocellular carcinoma (HCC) relative to native liver tissue. With intravoxel incoherent motion (IVIM) imaging, most authors paradoxically reported a decreased perfusion fraction of HCC relative to the adjacent liver. This study applied DDVD to assess the perfusion of HCC. MRI was performed with a 3.0-T magnet. Diffusion-weighted images with b-values of 0 and 2 s/mm2 were acquired in 72 HCC patients. Thirty-two patients had microvascular invasion (MVI(+)) and 40 patients did not have microvascular invasion (MVI(-)). Fifty-eight patients had Edmondson-Steiner grade I or II HCC, and 14 patients had Edmondson-Steiner grade III or IV HCC. DDVD measurement was conducted on the axial slice that showed the largest HCC size. DDVD(b0b2) T/L = HCC DDVD(b0b2)/liver DDVD(b0b2). DDVD(b0b2) T/L median (95% confidence interval) of all HCCs was 2.942 (2.419-3.522), of MVI(-) HCCs was 2.699 (2.030-3.522), of MVI(+) HCCs was 2.988 (2.423-3.990), of Edmondson-Steiner grade I/II HCCs was 2.873 (2.277-3.465), and of Edmondson-Steiner grade III/IV HCCs was 3.403 (2.008-4.485). DDVD(b0b2) T/L approximately agrees with contrast agent dynamically enhanced CT/MRI literature data, whereas it differs from earlier IVIM study results, where HCC perfusion fraction was paradoxically lower relative to native liver tissue. A weak trend was noted with MIV(+) HCCs had a higher DDVD(b0b2) T/L than that of MVI(-) HCCs, and a weak trend was noted with the poorly differentiated group of HCCs (Edmondson-Steiner grade III and IV) had a higher DDVD(b0b2) T/L than that of the better differentiated group of HCCs (Edmondson-Steiner grade I and II).

Influence of Early Versus Delayed Hepatic Artery Perfusion Scan on 90Y Selective Internal Radiation Therapy Planning.

Purpose: This study evaluated the effect of an increase in the time interval between hepatic intra-arterial injection of 99mTc-macroaggregated albumin (MAA) and hepatic artery perfusion scintigraphy (HAPS) on the lung shunt fraction (LSF) and perfused volume (PV) calculations in the treatment planning of selective internal radiation therapy (SIRT). Methods: The authors enrolled 51 HAPS sessions from 40 patients diagnosed with primary or metastatic liver malignancy. All patients underwent scan at the first and fourth hour after hepatic arterial injection of 99mTc-MAA. Based on single-photon emission computed tomography images, LSF values were measured from each patient's first and fourth hour images. PV1 and PV4 were also calculated based on three-dimensional images using 5% and 10% cutoff threshold values and compared with each other. Results: The authors found that the median of LSF4 was statistically significantly higher than LSF1 (3.05 vs. 4.14, p ≤ 0.01). There was no statistically significant difference between PV1 and PV4 on the 10% (p = 0.72) thresholds. Conclusions: LSF values can be overestimated in case of delayed HAPS, potentially leading to treatment cancellation due to incorrectly high results in patients who could benefit from SIRT. Threshold-based PV values do not significantly change over time; nevertheless, keeping the short interval time would be safer.

Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study.

PURPOSE: To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre. METHOD: A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre. RESULTS: Among the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774-0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700-0.888) and 0.883 (95% CI: 0.807-0.959), respectively. No statistical difference in AUC values among training set and testing sets was found. CONCLUSION: The proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability.

Comparison of contrast-enhanced ultrasonography and MRI results obtained by expert and novice radiologists indicating short-term response after transarterial chemoembolisation for hepatocellular carcinoma.

AIM: To evaluate inter-reader agreement between novice and expert radiologists in assessing contrast-enhanced ultrasonography (CEUS) and magnetic resonance imaging (MRI) images for detecting viable tumours with different sizes after conventional transarterial chemoembolisation (cTACE). MATERIALS AND METHODS: This prospective study included patients who had less than five hepatomas and who underwent cTACE. Hepatomas with one or two feeding arteries were selected as target lesions. CEUS and MRI were performed within 1 week after cTACE to evaluate viable tumours. RESULTS: The expert group had higher kappa values in evaluating all tumour sizes via CEUS compared with MRI. The novice group had similar kappa values. In patients with tumours measuring ≤3 cm, the expert group had higher kappa values in reading CEUS compared with MRI images; however, in the novice group, the kappa value was lower in evaluating CEUS compared with MRI images. In patients with tumours measuring >3 cm, the expert and novice groups had good to excellent kappa values. The confidence level of the two groups in reading MRI images was high; however, the novice group had a lower confidence level. CONCLUSION: CEUS is a convenient, cost-effective, and easy to apply imaging tool that can help interventionists perform early detection of viable hepatocellular carcinoma post-TACE. It has a higher inter-rater agreement in interpreting CEUS images compared with MRI images among expert radiologists even when they are extremely familiar with post-cTACE MRI images. In novice radiologists, there may be a learning curve to achieve good consistency in CEUS interpretation.

Extracellular contrast agent-enhanced MRI is as effective as gadoxetate disodium-enhanced MRI for predicting microvascular invasion in HCC.

PURPOSE: To compare the performances of gadoxetate disodium-enhanced MRI (EOB-MRI) and extracellular contrast agent-enhanced MRI (ECA-MRI) for predicting microvascular invasion (MVI) in HCC. MATERIALS AND METHODS: From November 2009 to December 2021, consecutive HCC patients who underwent preoperative contrast-enhanced MRI were retrospectively enrolled into either an ECA-MRI or EOB-MRI cohort. In the ECA-MRI cohort, a preoperative MVI score was constructed in the training dataset using a logistic regression model that evaluated pathological type. In a propensity score-matched testing dataset of the ECA-MRI cohort, the MVI score was validated and compared with a previously proposed EOB-MRI-based MVI score calculated in the EOB-MRI cohort. Time-to-early recurrence survival was evaluated by the Kaplan-Meier method with the log-rank test. RESULTS: A total of 536 patients were included (478 men; 53 years, interquartile range, 46-62 years), 322 (60.1 %) with pathologically confirmed MVI. Based on the training dataset, independent variables associated with MVI included serum alpha-fetoprotein > 400 ng/ml (odds ratio [OR] = 2.3), infiltrative appearance (OR = 4.9), internal artery (OR = 2.5) and nodule-in-nodule architecture (OR = 2.4), which were incorporated into the ECA-MRI-based MVI score. The testing dataset AUC of the ECA-MRI score was 0.720, which was comparable to that of the EOB-MRI-based MVI score (AUC = 0.721; P =.99). Patients from either the ECA-MRI or the EOB-MRI cohort with model-predicted MVI had significantly shorter time-to-early recurrence than those without MVI (P <.001). CONCLUSION: Based on the preoperative serum alpha-fetoprotein and three MRI features, ECA-MRI demonstrated comparable performance to EOB-MRI for predicting MVI in HCC.

Preoperative Contrast-Enhanced Ultrasound Predicts Microvascular Invasion in Hepatocellular Carcinoma as Accurately as Contrast-Enhanced MR.

OBJECTIVES: Both contrast-enhanced ultrasound (CEUS) and contrast-enhanced magnetic resonance (CEMR) are important imaging methods for hepatocellular carcinoma (HCC). This study aimed to establish a model using preoperative CEUS parameters to predict microvascular invasion (MVI) in HCC, and compare its predictive efficiency with that of CEMR model. METHODS: A total of 93 patients with HCC (39 cases in MVI positive group and 54 cases in MVI negative group) who underwent surgery in our hospital from January 2020 to June 2021 were retrospectively analyzed. Their clinical and imaging data were collected to establish CEUS and CEMR models for predicting MVI. The predictive efficiencies of both models were compared. RESULTS: By the univariate and multivariate regression analyses of patients' clinical information, preoperative CEUS static and dynamic images, we found that serrated edge and time to peak were independent predictors of MVI. The CEUS prediction model achieved a sensitivity of 92.3%, a specificity of 83.3%, and an accuracy of 84.6% (Az: 0.934). By analyzing the clinical and CEMR information, we found that tumor morphology, fast-in and fast-out, peritumoral enhancement, and capsule were independent predictors of MVI. The CEMR prediction model achieved a sensitivity of 97.4%, a specificity of 77.8%, and an accuracy of 83.2% (Az: 0.900). The combination of the two models achieved a sensitivity of 84.6%, a specificity of 87.0%, and an accuracy of 86.2% (Az: 0.884). There was no significant statistical difference in the areas under the ROC curve of the three models. CONCLUSION: The CEUS model and the CEMR model have similar predictive efficiencies for MVI of HCC. CEUS is also an effective method to predict MVI before operation.

Evaluation of qualitative and quantitative imaging features of hepatic hemangiomas with and without pseudo-washout sign on gadoxetic acid-enhanced MRI: a descriptive and comparative study.

BACKGROUND: Hepatic hemangiomas are the most common benign liver tumors. It is important to know the imaging features of hemangiomas on gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI). PURPOSE: To evaluate the qualitative and quantitative imaging features of hemangiomas on GA-enhanced MRI, and to compare imaging features of hemangiomas with and without pseudo-washout sign (PWS). MATERIAL AND METHODS: We retrospectively included 93 cases of hemangioma that underwent GA-enhanced MRI. The presence of an enhancement pattern in the arterial phase (AP) and PWSs in the transitional phase (TP) were evaluated. Signal-to-norm ratios (SINorm) of hemangiomas, liver parenchyma, and portal vein (PV) as well as contrast-to-norm ratio (CNorm) were assessed. Additionally, hemangiomas with and without PWSs were defined as two separate subgroups, and imaging features were compared. RESULTS: Of the 93 cases of hemangiomas, 49 (52.6%) had PWSs in the TP. The mean SINorms of hemangiomas showed the highest value in the AP (P < 0.05). The mean CNorms showed positive values in the AP, and gradually decreased (P < 0.05). Hemangiomas with PWSs were significantly rapidly enhanced and smaller in size (P < 0.05), and the mean SINorms was lower in the TP (P = 0.023). While the mean CNorms showed a significant difference in the AP between subgroups (P < 0.001), the enhancement pattern was equal to that of the PV. CONCLUSION: When evaluating GA-enhanced MRI, radiologists should utilize quantitative measures in addition to qualitative assessment and should be aware that SI matching with PV in all phases can be a distinguishing finding in the diagnosis of hemangioma.