Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma

Preoperative alkaline phosphatase-to-platelet count ratio as a prognostic factor for hepatocellular carcinoma with microvascular invasion

OBJECTIVES

The association between platelet status and hepatocellular carcinoma (HCC) prognoses remains controversial. Herein, we aimed to clarify the prognostic value of multiple platelet-related biomarkers, including platelet count, platelet/lymphocyte ratio (PLR), aspartate aminotransferase to platelet ratio index (APRI), and alkaline phosphatase-to-platelet count ratio index (APPRI) in HCC with microvascular invasion (MVI) after curative resection or liver transplantation.

MATERIALS AND METHODS

A retrospective review of 169 patients with solitary HCC and MVI who underwent resection or liver transplantation between January 2015 and December 2018 was conducted. Preoperative clinical, laboratory, pathologic, and imaging data were collected and analyzed. Overall survival (OS) and disease-free survival (DFS) were defined as the clinical endpoints. Univariate and multivariate Cox proportional hazards regression analyses were conducted to investigate potential predictors of DFS and OS.

RESULTS

Multivariate Cox regression analyses revealed that maximum tumor diameter, poor cell differentiation, and APPRI were independent predictors of DFS; while poor cell differentiation, APRI, APPRI, prothrombin time, and alpha-fetoprotein were independent prognostic factors for OS. The 1-, 3-, and 5-year DFS rates were 66.90%, 48.40%, and 37.40% for patients with APPRI ≤0.74 and 40.40%, 24.20%,and 24.20% for patients with APPRI>0.74. The corresponding rates of OS over 1, 3, and 5 years were 92.40%, 88.10% and 77.70%, and 72.30%, 38.20%, and 19.10%, respectively. The DFS and OS rates of patients whose APPRI was more than 0.74 were substantially lower than those of patients whose APPRI was less than or equal to 0.74 (p = 0.002 and p < 0.001, respectively).

CONCLUSION

Elevated preoperative APPRI is a noninvasive, simple, and easily assessable parameter linked to poor prognosis in individuals with single HCC and MVI after resection or liver transplantation.

Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma

Background & Aims

Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC.

Methods

Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n = 297) and externally validated (n = 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n = 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated.

Results

The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851-0.883 for MVI and 0.834-0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748-0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS (p <0.05). By contrast, anatomic resection had no impact on RFS in patients at model-predicted low or high risk (both p >0.05).

Conclusions

The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection.

Impact and implications

MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.

Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism

Preoperative prediction of microvascular invasion (MVI) is essential for management decision in hepatocellular carcinoma (HCC). Deep learning-based prediction models of MVI are numerous but lack clinical interpretation due to their "black-box" nature. Consequently, we aimed to use an attention-guided feature fusion network, including intra- and inter-attention modules, to solve this problem. This retrospective study recruited 210 HCC patients who underwent gadoxetate-enhanced MRI examination before surgery. The MRIs on pre-contrast, arterial, portal, and hepatobiliary phases (hepatobiliary phase: HBP) were used to develop single-phase and multi-phase models. Attention weights provided by attention modules were used to obtain visual explanations of predictive decisions. The four-phase fusion model achieved the highest area under the curve (AUC) of 0.92 (95% CI: 0.84-1.00), and the other models proposed AUCs of 0.75-0.91. Attention heatmaps of collaborative-attention layers revealed that tumor margins in all phases and peritumoral areas in the arterial phase and HBP were salient regions for MVI prediction. Heatmaps of weights in fully connected layers showed that the HBP contributed the most to MVI prediction. Our study firstly implemented self-attention and collaborative-attention to reveal the relationship between deep features and MVI, improving the clinical interpretation of prediction models. The clinical interpretability offers radiologists and clinicians more confidence to apply deep learning models in clinical practice, helping HCC patients formulate personalized therapies.

Focal Lesions of the Liver and Radiomics: What Do We Know?

Despite differences in pathological analysis, focal liver lesions are not always distinguishable in contrast-enhanced magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CT), and positron emission tomography (PET). This issue can cause problems of differential diagnosis, treatment, and follow-up, especially in patients affected by HBV/HCV chronic liver disease or fatty liver disease. Radiomics is an innovative imaging approach that extracts and analyzes non-visible quantitative imaging features, supporting the radiologist in the most challenging differential diagnosis when the best-known methods are not conclusive. The purpose of this review is to evaluate the most significant CT and MRI texture features, which can discriminate between the main benign and malignant focal liver lesions and can be helpful to predict the response to pharmacological or surgical therapy and the patient's prognosis.

Preoperative MRI-based multiparametric model for survival prediction in hepatocellular carcinoma patients with portal vein tumor thrombus following hepatectomy

PURPOSE

To develop a predictive model integrating clinical and MRI features for postoperative survival in patients with hepatocellular carcinoma (HCC) and portal vein tumor thrombus (PVTT).

METHOD

Between January 2008 and May 2021, consecutive HCC patients with PVTT who underwent preoperative contrast-enhanced MRI and surgical resection at a tertiary hospital were retrospectively enrolled. The MR images were independently reviewed by two blinded radiologists. Univariate and multivariate Cox regression analyses were performed to construct a prognostic score for overall survival (OS).

RESULTS

Ninety-four patients were included (mean age, 50.1 years; 84 men). During a median follow-up period of 15.3 months, 72 (76.6%) patients died (median OS, 15.4 months; median disease-free survival [DFS], 4.6 months). The sum size of the two largest tumors (hazard ratio [HR], 3.050; p < 0.001) and tumor growth subtype (HR, 1.928; p = 0.006) on MRI, serum albumin (HR, 0.948; p = 0.02), and age (HR, 0.978; p = 0.04) were associated with OS and incorporated in the prognostic score. Accordingly, patients were stratified into a high-risk or low-risk group, and the OS in the high-risk group was shorter than that in the low-risk group for the entire cohort (11.7 vs. 25.0 months, p < 0.001) and for patients with Cheng's type I (12.1 vs. 25.9 months, p = 0.002) and type II PVTT (11.7 vs. 25.0 months, p = 0.004). The DFS in the high-risk group was shorter than that in the low-risk group for the entire cohort (4.5 vs. 6.1 months, p = 0.001).

CONCLUSIONS

Based on the sum size of the two largest tumors, tumor growth subtype, albumin, and age, the prognostic score allowed accurate preoperative risk stratification in HCC patients with PVTT, independent of Cheng's PVTT classification.

A Reliable and Repeatable Model for Predicting Microvascular Invasion in Patients With Hepatocellular Carcinoma

RATIONALE AND OBJECTIVES

The reproducibility of imaging models for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains questionable due to inconsistent interpretation of image signs. Our aim was to screen for high-consensus MRI features to develop a repeatable model for predicting MVI.

MATERIALS AND METHODS

We included 219 patients with HCC who underwent surgical resection, and patients were divided into a training cohort (n = 145) and a validation cohort (n = 74). Morphological characteristics, signal features on hepatobiliary phases, and dynamic enhancement patterns were qualitatively interobserver evaluated. Interobserver agreement was assessed using Cohen's κ for selecting features with high interobserver agreement. Risk factors that were significant in stepwise multivariate analysis and that could be measured with good interobserver agreement were used to construct a predictive model, which was assessed in the validation cohort. The diagnostic performance of the model was evaluated based on area under the receiver operating characteristic curve (AUC).

RESULTS

Multivariate analysis identified nonsmooth tumor margin, absence of radiologic capsule, and intratumoral artery as independent risk factors of MVI. These MRI-based features showed good or nearly perfect interobserver agreement between radiologists (κ > 0.6). The predictive model predicted MVI well in the training (AUC 0.734) and validation cohorts (AUC 0.759) and fitted well to calibration curves.

CONCLUSION

MRI features included nonsmooth tumor margin, absence of radiologic capsule, and intratumoral artery that can be assessed with high interobserver agreement can predict MVI in HCC patients. The predictive model described here may be useful to radiologists, regardless of experience level.

Estimating Microvascular Invasion in Patients with Resectable Multinodular Hepatocellular Carcinoma by Using Preoperative Contrast-Enhanced MRI: Establishment and Validation of a Risk Score

Objective

To determine the preoperative clinicoradiological factors to predict microvascular invasion (MVI) in patients with resectable multinodular hepatocellular carcinoma (mHCC), and further to establish and validate a stratified risk scoring system.

Methods

Two hundred and seventy-three patients with pathologically confirmed mHCC (≥2 lesions) without major vascular invasion and biliary tract tumor thrombosis, who underwent preoperative contrast-enhanced MRI and hepatectomy, were consecutively enrolled (training/validation cohort=193/80). Preoperative clinicoradiological variables were collected and analyzed. The multivariable logistic regression was performed to determine the independent predictors of MVI and create a risk score system. The C-index, calibration curve and decision curve were used to evaluate the performance of the risk score. A risk score-based prognostic stratification system was performed in mHCC patients. The risk score system was further verified in the validation cohort.

Results

AFP > 400 ng/mL, presence of satellite nodule, mosaic architecture and increased total tumor diameter were independent predictors of MVI while fat in mass was an independent protective factor of MVI. The risk score yielded satisfactory C-index values (training/validation cohort: 0.777/0.758) and fitted well in calibration curves. Decision curve analysis further confirmed its clinical utility. Based on the risk score, mHCC patients were stratified into high-/low-MVI-risk subgroups with significantly different recurrence-free survival (both P < 0.001).

Conclusion

The presented risk score incorporating clinicoradiological parameters could stratify mHCC patients into high-risk and low-risk subgroups and predict prognosis in patients with resectable mHCC.

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy

OBJECTIVES

The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence.

METHODS

In this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction.

RESULTS

MVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram.

CONCLUSION

The proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy.

KEY POINTS

• Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.

Classification of microvascular invasion of hepatocellular carcinoma: correlation with prognosis and magnetic resonance imaging

BACKGROUND/AIMS

The microvascular invasion (MVI) of hepatocellular carcinoma (HCC) involves a wide histological spectrum, and it is unclear whether the degree of MVI correlates with patient prognosis or imaging findings. Here, we evaluate the prognostic value of MVI classification and analyze the radiologic features predictive of MVI.

METHODS

Using a retrospective cohort of 506 patients with resected solitary HCCs, the histological and imaging features of MVI were reviewed and correlated with clinical data.

RESULTS

MVI-positive HCCs invading ≥5 vessels or those with ≥50 invaded tumor cells were significantly associated with decreased overall survival (OS). The 5-year OS, recurrence-free survival (RFS), and beyond Milan criteria RFS rates were significantly poorer in patients with severe MVI compared with those with mild or no MVI. Severe MVI was a significant independent predictive factor for OS (odds ratio [OR], 2.962; p<0.001), RFS (OR, 1.638; p=0.002), and beyond Milan criteria RFS (OR, 2.797; p<0.001) on multivariable analysis. On MRI, non-smooth tumor margins (OR, 2.224; p=0.023) and satellite nodules (OR, 3.264; p<0.001) were independently associated with the severe-MVI group on multivariable analysis. Both non-smooth tumor margins and satellite nodules were associated with worse 5-year OS, RFS, and beyond Milan criteria RFS.

CONCLUSION

Histologic risk classification of MVI according to the number of invaded microvessels and invading carcinoma cells was a valuable predictor of prognosis in HCC patients. Non-smooth tumor margin and satellite nodules were significantly associated with severe MVI and poor prognosis.

Perfusion parameters of triphasic computed tomography hold preoperative prediction value for microvascular invasion in hepatocellular carcinoma

The purpose of this study was to evaluate perfusion parameters of triphasic computed tomography (CT) scans in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). All patients were pathologically diagnosed as HCC and underwent triple-phase enhanced CT imaging, which was used to calculate the blood perfusion parameters of hepatic arterial supply perfusion (HAP), portal vein blood supply perfusion (PVP), hepatic artery perfusion Index (HPI), and arterial enhancement fraction (AEF). Receiver operating characteristic (ROC) curve was used to evaluate the performance. The mean values of PVP(Min), AEF(Min), the difference in PVP, HPI and AEF related parameters, the relative PVP(Min) and AEF(Min) in MVI negative group were significantly higher than those in MVI positive group, while for the difference in HPI(Max), the relative HPI(Max) and AEF(Max), the value of MVI positive group significantly higher than that of negative group. The combination of PVP, HPI and AEF had the highest diagnostic efficacy. The two parameters related to HPI had the highest sensitivity, while the combination of PVP related parameters had higher specificity. A combination of perfusion parameters in patients with HCC derived from traditional triphasic CT scans can be used as a preoperative biomarker for predicting MVI.

Three-dimensional multifrequency magnetic resonance elastography improves preoperative assessment of proliferative hepatocellular carcinoma

BACKGROUND

To investigate the viscoelastic signatures of proliferative hepatocellular carcinoma (HCC) using three-dimensional (3D) magnetic resonance elastography (MRE).

METHODS

This prospective study included 121 patients with 124 HCCs as training cohort, and validation cohort included 33 HCCs. They all underwent preoperative conventional magnetic resonance imaging (MRI) and tomoelastography based on 3D multifrequency MRE. Viscoelastic parameters of the tumor and liver were quantified as shear wave speed (c, m/s) and loss angle (φ, rad), representing stiffness and fluidity, respectively. Five MRI features were evaluated. Multivariate logistic regression analyses were used to determine predictors of proliferative HCC to construct corresponding nomograms.

RESULTS

In training cohort, model 1 (Combining cirrhosis, hepatitis virus, rim APHE, peritumoral enhancement, and tumor margin) yielded an area under the curve (AUC), sensitivity, specificity, accuracy of 0.72, 58.73%,78.69%, 67.74%, respectively. When adding MRE properties (tumor c and tumor φ), established model 2, the AUC increased to 0.81 (95% CI 0.72-0.87), with sensitivity, specificity, accuracy of 71.43%, 81.97%, 75%, respectively. The C-index of nomogram of model 2 was 0.81, showing good performance for proliferative HCC. Therefore, integrating tumor c and tumor φ can significantly improve the performance of preoperative diagnosis of proliferative HCC (AUC increased from 0.72 to 0.81, p = 0.012). The same finding was observed in the validation cohort, with AUC increasing from 0.62 to 0.77 (p = 0.021).

CONCLUSIONS

Proliferative HCC exhibits low stiffness and high fluidity. Adding MRE properties (tumor c and tumor φ) can improve performance of conventional MRI for preoperative diagnosis of proliferative HCC.

CRITICAL RELEVANCE STATEMENT

We investigated the viscoelastic signatures of proliferative hepatocellular carcinoma (HCC) using three-dimensional (3D) magnetic resonance elastography (MRE), and find that adding MRE properties (tumor c and tumor φ) can improve performance of conventional MRI for preoperative diagnosis of proliferative HCC.

Prediction microvascular invasion of hepatocellular carcinoma based on tumour margin enhancing pattern in multiphase computed tomography images

PURPOSE

The presence of microvascular invasion of hepatocellular carcinoma has a significantly decreased outcome following hepatectomy or liver transplantation. Currently, it is still based on histological examination. Identification of microvascular invasion by using pre-operative imaging is important for the decision-making of surgeons and interventional radiologists. Aim of the study was to predict the microvascular invasion of hepatocellular carcinoma based on tumour margin enhancement of pre-operative multiphase computed tomography (CT) images.

MATERIAL AND METHODS

Fifty-three patients with hepatocellular carcinoma, who underwent pre-operative multiphase CT scans, were included in this study. Tumour margin enhancing patterns were analysed in the late arterial phase, portovenous phase, and delay phase. The CT features including peritumoral enhancement, arterial rim-enhancement, presence of daughter nodules, complete capsule enhancement in portovenous/delay phase, and nodular capsule enhancement in portovenous/delay phase were reviewed with calculations for sensitivity and specificity. Univariate analysis and multivariate analysis were used to identify predictive features for microvascular invasion (MVI).

RESULTS

In the late arterial phase, peritumoral enhancement or the presence of daughter nodules were not predictors for MVI. Nodular capsule enhancement in the portovenous phase and delay phase were independent predictors for MVI with odds ratios of 29.25 and 33.09, respectively. The sensitivity and specificity for incomplete/nodular capsule enhancement in the portovenous phase were 69.23% and 96.86%, respectively. The sensitivity and specificity for incomplete/nodular capsule enhancement in the delay phase were 71.79% and 96.86%, respectively.

CONCLUSION

Nodular capsule enhancement in the portovenous phase or delay phase was a good predictor for MVI.

Evaluation of Preoperative Microvascular Invasion in Hepatocellular Carcinoma Through Multidimensional Parameter Combination Modeling Based on Gd-EOB-DTPA MRI

BACKGROUND AND AIMS

The study established and compared the efficacy of the clinicoradiological model, radiomics model and clinicoradiological-radiomics hybrid model in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using gadolinium ethoxybenzyl diethylene triaminepentaacetic acid (Gd-EOB-DTPA) enhanced MRI.

METHODS

This was a study that enrolled 602 HCC patients from two institutions. Least absolute shrinkage and selection operator (Lasso) method was used to screen for the most important clinicoradiological and radiomics features that predict MVI pre-operatively. Three machine learning algorithms were used to establish the clinicoradiological, radiomics, and clinicoradiological-radiomics hybrid models. Area under the curve (AUC) of receiver operating characteristic (ROC) curves and Delong's test were used to compare and quantify the predictive performance of the models.

RESULTS

The AUCs of the clinicoradiological model in training and validation cohorts were 0.793 and 0.701, respectively. The radiomics signature of arterial phase (AP) images alone achieved satisfying predictive efficacy for MVI, with AUCs of 0.671 and 0.643 in training and validation cohort, respectively. The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images achieved AUCs of 0.824 and 0.801 in training and validation cohorts, 0.812 and 0.805 in prospective validation and external validation cohorts, respectively. The hybrid model provided the best prediction results. The results of the Delong test revealed that there were statistically significant differences among the clinicoradiological-radiomics hybrid model, clinicoradiological model, and radiomics model (p<0.05).

CONCLUSIONS

The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images based on Gd-EOB-DTPA-enhanced MRI can effectively predict MVI.

Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

Correlation between magnetic resonance images of peritumor margin enhancement and prognosis in hepatocellular carcinoma after drug-eluting bead transcatheter arterial chemoembolization

Purpose

The aim of this study is to investigate the morphological characteristics and clinical significance of magnetic resonance (MR) images of peritumor margin enhancement in hepatocellular carcinoma (HCC) after drug-eluting bead transcatheter arterial chemoembolization (DEB-TACE).

Methods

From January 2017 to December 2020, a total of 162 patients who received a diagnosis of HCC were included in our study. We began the follow-up with magnetic resonance imaging (MRI) for complete response assessment, and peritumor margin enhancements were classified as sharp and rough types according to morphology. During the follow-up, data such as progression or remission of the two enhancement modalities, morphological changes in terms of margin enhancements observed in MR images, and alpha-fetoprotein (AFP) levels were recorded.

Results

In the follow-up period of 36 months, 70 and 92 patients with sharp- and rough-type peritumor margins, respectively, were observed. At the end of the follow-up, patients with sharp-type margins had lower AFP levels and longer progression-free survival than those with rough-type margins (P &lt; 0.05). Furthermore, the sharp-type margin was thinner than the rough-type margin (all P &lt; 0.05). Moreover, the sharp-type group had a high incidence of tumors with a diameter of &lt; 5 cm, whereas the rough-type group had a high incidence of tumors with a diameter of ≥ 5 cm. Continuous enhancements of peritumor margins in MRI were greater in the sharp-type group than in the rough-type group. Most of the patients with a sharp-type margin achieved disease remission (94.3%, P &lt; 0.05), whereas most of those with a rough-type margin experienced disease progression (84.8%, P &lt; 0.05).

Conclusions

Patients with HCC with a sharp-type margin enhancement on MRI after DEB-TACE mostly demonstrated benign lesions with a good prognosis, whereas those with a rough-type margin mostly demonstrated malignant growth.

VICT2 Trait: Prognostic Alternative to Peritumoral Hepatobiliary Phase Hypointensity in HCC

Background Peritumoral hepatobiliary phase (HBP) hypointensity is an established prognostic imaging feature in hepatocellular carcinoma (HCC), often associated with microvascular invasion (MVI). Similar prognostic features are needed for non-HBP MRI. Purpose To propose a non-hepatobiliary-specific MRI tool with similar prognostic value to peritumoral HBP hypointensity. Materials and Methods From December 2011 to November 2021, consecutive patients with HCC who underwent preoperative contrast-enhanced MRI were retrospectively enrolled and followed up until recurrence. All MRI scans were reviewed by two blinded radiologists with 7 and 10 years of experiences with liver MRI. A scoring system based on non-hepatobiliary-specific features that highly correlated with peritumoral HBP hypointensity was identified in a stratified sampling-derived training set of the gadoxetate disodium (EOB) group by means of multivariable logistic regression, and its values to predict MVI and recurrence-free survival (RFS) were assessed. Results There were 660 patients (551 men; median age, 53 years; IQR, 45-61 years) enrolled. Peritumoral portal venous phase hypoenhancement (odds ratio [OR] = 8.8), incomplete "capsule" (OR = 3.3), corona enhancement (OR, 2.6), and peritumoral mild-moderate T2 hyperintensity (OR, 2.2) (all P < .001) were associated with peritumoral HBP hypointensity and constituted the "VICT2 trait" (test set area under the receiver operating characteristic curve = 0.84; 95% CI: 0.78, 0.90). For the EOB group, both peritumoral HBP hypointensity (OR for MVI = 2.5, P = .02; hazard ratio for RFS = 2.5, P < .001) and the VICT2 trait (OR for MVI = 5.1, P < .001; hazard ratio for RFS = 2.3, P < .001) were associated with MVI and RFS, despite a higher specificity of the VICT2 trait for MVI (89% vs 80%, P = .01). These values of the VICT2 trait were confirmed in the extracellular contrast agent group (OR for MVI = 4.0; hazard ratio for RFS = 1.7; both P < .001). Conclusion Based on four non-hepatobiliary-specific MRI features, the VICT2 trait was comparable to peritumoral hepatobiliary phase hypointensity in predicting microvascular invasion and postoperative recurrence of hepatocellular carcinoma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Harmath in this issue.

Significance of anatomical resection and resection margin status in patients with HBV-related hepatocellular carcinoma and microvascular invasion: a multicenter propensity score-matched study

BACKGROUND

Microvascular invasion (MVI) is a risk factor for postoperative survival outcomes for patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to evaluate the impact of anatomical resection (AR) versus nonanatomical resection (NAR) combined with resection margin (RM) (narrow RM <1 cm vs. wide RM ≥1 cm) on long-term prognosis in hepatitis B virus-related HCC patients with MVI.

MATERIALS AND METHODS

Data from multicenters on HCC patients with MVI who underwent hepatectomy was analyzed retrospectively. Propensity score matching analysis was performed in these patients.

RESULTS

The 1965 enrolled patients were divided into four groups: AR with wide RM ( n =715), AR with narrow RM ( n =387), NAR with wide RM ( n =568), and NAR with narrow RM ( n =295). Narrow RM ( P <0.001) and NAR ( P <0.001) were independent risk factors for both overall survival and recurrence-free survival in these patients based on multivariate analyses. For patients in both the AR and NAR groups, wide RM resulted in significantly lower operative margin recurrence rates than those patients in the narrow RM groups after propensity score matching ( P =0.002 and 0.001). Patients in the AR with wide RM group had significantly the best median overall survival (78.9 vs. 51.5 vs. 48.0 vs. 36.7 months, P <0.001) and recurrence-free survival (23.6 vs. 14.8 vs. 17.8 vs. 9.0 months, P <0.001) than those in the AR with narrow RM, NAR with wide RM or with narrow RM groups, respectively.

CONCLUSIONS

If technically feasible and safe, AR combined with wide RM should be the recommended therapeutic strategy for HCC patients who are estimated preoperatively with a high risk of MVI.

Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma

Radiomics is a discipline that involves studying medical images through their digital data. Using “artificial intelligence” algorithms, radiomics utilizes quantitative and high-throughput analysis of an image’s textural richness to obtain relevant information for clinicians, from diagnosis assistance to therapeutic guidance. Exploitation of these data could allow for a more detailed characterization of each phenotype, for each patient, making radiomics a new biomarker of interest, highly promising in the era of precision medicine. Moreover, radiomics is non-invasive, cost-effective, and easily reproducible in time. In the field of oncology, it performs an analysis of the entire tumor, which is impossible with a single biopsy but is essential for understanding the tumor’s heterogeneity and is known to be closely related to prognosis. However, current results are sometimes less accurate than expected and often require the addition of non-radiomics data to create a performing model. To highlight the strengths and weaknesses of this new technology, we take the example of hepatocellular carcinoma and show how radiomics could facilitate its diagnosis in difficult cases, predict certain histological features, and estimate treatment response, whether medical or surgical.

Comparison of preoperative ultrasound and MRI in the diagnosis of microvascular invasion in hepatocellular carcinoma

Ultrasound has few reports on its application in prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). The purpose of this study was to explore the diagnostic efficacies of preoperative ultrasound and magnetic resonance imaging (MRI) for HCC MVI and compare these two imaging methods for the diagnosis of this condition. The clinical and preoperative ultrasound and MR imaging data of 26 patients with newly diagnosed HCC were collected between October 2020 and October 2021. According to the gold standard (postoperative pathology), the patients were divided into MVI-positive and MVI-negative groups, and the efficacies of ultrasound and MRI in diagnosing HCC MVI and the consistency between the two imaging modalities were analyzed. For the preoperative diagnosis of MVI using ultrasound, the sensitivity was 93.33%, the specificity was 81.82%, and the accuracy was 88.46%. For preoperative MRI, the sensitivity was 66.67%, the specificity was 100%, and the accuracy was 80.77%. In diagnosing MVI, the two methods had significantly different efficacy (P = 0.031). Ultrasound and MRI have high diagnostic efficiency for MVI, but the accuracy of preoperative MRI was lower than that of preoperative ultrasound. These results indicate that ultrasound has a certain guiding significance in the diagnosis of HCC MVI.

Radiologic features of hepatocellular carcinoma related to prognosis

The cross-sectional imaging findings play a crucial role in the diagnosis of hepatocellular carcinoma (HCC). Recent studies have shown that imaging findings of HCC are not only relevant for the diagnosis of HCC, but also for identifying genetic and pathologic characteristics and determining prognosis. Imaging findings such as rim arterial phase hyperenhancement, arterial phase peritumoral hyperenhancement, hepatobiliary phase peritumoral hypointensity, non-smooth tumor margin, low apparent diffusion coefficient, and the LR-M category of the Liver Imaging-Reporting and Data System have been reported to be associated with poor prognosis. In contrast, imaging findings such as enhancing capsule appearance, hepatobiliary phase hyperintensity, and fat in mass have been reported to be associated with a favorable prognosis. Most of these imaging findings were examined in retrospective, single-center studies that were not adequately validated. However, the imaging findings can be applied for deciding the treatment strategy for HCC, if their significance can be confirmed by a large multicenter study. In this literature, we would like to review imaging findings related to the prognosis of HCC as well as their associated clinicopathological characteristics.

A model incorporating histopathology and preoperative gadoxetic acid-enhanced MRI to predict early recurrence of hepatocellular carcinoma without microvascular invasion after curative hepatectomy

OBJECTIVES

To assess the predictive value of preoperative gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI) features and postoperative histopathological grading for early recurrence of hepatocellular carcinoma (HCC) without microvascular invasion (MVI) after curative hepatectomy.

METHODS

A total of 85 MVI-negative HCC cases were retrospectively analyzed. Cox analyses were used to identify the independent predictors of early recurrence (within a 24 months span). The clinical prediction Model-1 or Model-2 was established without or with postoperative pathological factor, respectively. Nomogram models were constructed and receiver operating characteristic (ROC) curve analysis was used to assess the models' predictive ability. Internal validation of the prediction models for early HCC recurrence was performed using a bootstrap re-sampling approach.

RESULTS

In the multivariate cox regression analysis, Edmondson-Steiner grade, peritumoral hypointensity on hepatobiliary phase (HBP), and relative intensity ratio (RIR) in HBP were identified as independent variables associated with early recurrence. The C-index of the nomogram models and internal validation were both between 0.7 and 0.8, showing good model fitting and calibration effects. The area under the ROC curve (AUC) was 0.781 for Model-1 based on the two preoperative MRI factors. When a third factor, the Edmondson-Steiner grade, was included (Model-2), the AUC increased to 0.834, and the sensitivity increased from 71.4 to 96.4%.

CONCLUSIONS

Edmondson-Steiner grade, peritumoral hypointensity on HBP, and RIR on HBP can help predict early recurrence of MVI-negative HCC. In comparison with Model-1 (only imaging features), Model-2 (imaging features + histopathological grades) increases the sensitivity in predicting early recurrence of HCC without MVI.

ADVANCES IN KNOWLEDGE

Preoperative GA-enhanced MRI signs are of great value in predicting early postoperative recurrence of HCC without MVI, and a combined pathological model was established to evaluate the feasibility and effectiveness of this technique.

Pre-operative MRI features predict early post-operative recurrence of hepatocellular carcinoma with different degrees of pathological differentiation

PURPOSE

To investigate the value of pre-operative gadoxetate disodium (Gd-EOB-DTPA) enhanced MRI predicting early post-operative recurrence (< 2 years) of hepatocellular carcinoma (HCC) with different degrees of pathological differentiation.

METHODS

Retrospective analysis of pre-operative MR imaging features of 177 patients diagnosed as suffering from HCC and that underwent radical resection. Multivariate logistic regression assessment was adopted to assess predictors for HCC recurrence with different degrees of pathological differentiation. The area under the curve (AUC) of receiver operating characteristics (ROC) was utilized to assess the diagnostic efficacy of the predictors.

RESULTS

Among the 177 patients, 155 (87.5%) were males, 22 (12.5%) were females; the mean age was 49.97 ± 10.71 years. Among the predictors of early post-operative recurrence of highly-differentiated HCC were an unsmooth tumor margin and an incomplete/without tumor capsule (p = 0.037 and 0.033, respectively) whereas those of early post-operative recurrence of moderately-differentiated HCC were incomplete/without tumor capsule, peritumoral enhancement along with peritumoral hypointensity (p = 0.006, 0.046 and 0.004, respectively). The predictors of early post-operative recurrence of poorly-differentiated HCC were peritumoral enhancement, peritumoral hypointensity, and tumor thrombosis (p = 0.033, 0.006 and 0.021, respectively). The AUCs of the multi-predictor diagnosis of early post-operative recurrence of highly-, moderately-, and poorly-differentiated HCC were 0.841, 0.873, and 0.875, respectively. The AUCs of the multi-predictor diagnosis were each higher than for those predicted separately.

CONCLUSIONS

The imaging parameters for predicting early post-operative recurrence of HCC with different degrees of pathological differentiation were different and combining these predictors can improve the diagnostic efficacy of early post-operative HCC recurrence.

Development of a deep learning-based auto-segmentation algorithm for hepatocellular carcinoma (HCC) and application to predict microvascular invasion of HCC using CT texture analysis: preliminary results

BACKGROUND

Automatic segmentation has recently been developed to yield objective data. Prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using radiomics has been reported.

PURPOSE

To develop a deep learning-based auto-segmentation algorithm (DL-AS) for the detection of HCC and to predict MVI using computed tomography (CT) texture analysis.

MATERIAL AND METHODS

We retrospectively collected training data from 249 patients with HCC and validation set from 35 patients. Lesions of the training set were manually drawn by radiologist, in the delayed phase. 2D U-Net was selected as the DL architecture. Using the validation set, one radiologist manually drew 2D and 3D regions of interest twice, and the developed DL-AS was performed twice with a one-month time interval. The reproducibility was calculated using intraclass correlation coefficients (ICC). Logistic regression was performed to predict MVI.

RESULTS

ICC was in the range of 0.190-0.998/0.341-0.997 in the manual 3D/2D segmentation. In contrast, it was perfect in 3D/2D using DL-AS, with a success rate of 88.6% for the detection of HCC. For predicting MVI, sphericity was a significant parameter (odds ratio <0.001; 95% confidence interval <0.001-0.206; P = 0.020) for predicting MVI using 2D DL-AS. However, 3D DL-AS segmentation did not yield a predictive parameter.

CONCLUSION

The auto-segmentation of HCC using DL-AS provides perfect reproducibility, although it failed to detect 11.4% (4/35). However, the extracted parameters yielded different important predictors of MVI in HCC. Sphericity was a significant predictor in 2D DL-AS and 3D manual segmentation, while discrete compactness was a significant predictor in 2D manual segmentation.

Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma

PURPOSE

This study aimed to analyze imaging features based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the identification of vessels encapsulating tumor clusters (VETC)-microvascular invasion (MVI) in hepatocellular carcinoma (HCC), VM-HCC pattern.

METHODS

Patients who underwent hepatectomy and preoperative DCE-MRI between January 2015 and March 2021 were retrospectively analyzed. Clinical and imaging features related to VM-HCC (VETC + /MVI-, VETC-/MVI +, VETC + /MVI +) and Non-VM-HCC (VETC-/MVI-) were determined by multivariable logistic regression analyses. Early and overall recurrence were determined using the Kaplan-Meier survival curve. Indicators of early and overall recurrence were identified using the Cox proportional hazard regression model.

RESULTS

In total, 221 patients (177 men, 44 women; median age, 60 years; interquartile range, 52-66 years) were evaluated. The multivariable logistic regression analyses revealed fetoprotein > 400 ng/mL (odds ratio [OR] = 2.17, 95% confidence interval [CI] 1.07, 4.41, p = 0.033), intratumor vascularity (OR 2.15, 95% CI 1.07, 4.31, p = 0.031), and enhancement pattern (OR 2.71, 95% CI 1.17, 6.03, p = 0.019) as independent predictors of VM-HCC. In Kaplan-Meier survival analysis, intratumor vascularity was associated with early and overall recurrence (p < 0.05).

CONCLUSION

Based on DCE-MRI, intratumor vascularity can be used to characterize VM-HCC and is of prognostic significance for recurrence in patients with HCC.

Transarterial chemoembolization failure in patients with hepatocellular carcinoma: Incidence, manifestation and risk factors

BACKGROUND

To identify the incidence, manifestation and risk factors of transarterial chemoembolization (TACE) failure defined as untreatable progression (UP) in patients with hepatocellular carcinoma (HCC) on short-term observation.

METHODS

Patients from two hospitals with HCC treated with TACE were considered. According to the definition of UP, TACE failure was considered to be present in at least one of the following situations: situation I, failure to achieve objective response in the targeted tumor after at least two initial TACE treatments; situation II, failure to achieve objective response in local tumor progression or new intrahepatic tumor after another TACE session; situation III, presence of major progression; and situation IV, presence of impaired liver function or performance status that contraindicates TACE treatment. Patients were assessed for TACE failure on follow-up visits after two or three TACE sessions. Risk factors for TACE failure were evaluated with logistic regression analysis.

RESULTS

A total of 206 patients were included. TACE failure occurred in 42 (42/206, 20.4%) patients, of whom 21, 1, 4, 0 and 16 patients manifested as situation I, II, III, IV alone, and combination of situation I with the others, respectively. Multivariate analysis showed that tumor without complete capsule (P < .001) and non-smooth margin (P = .004) were independent predictors of the presence of TACE failure.

CONCLUSIONS

TACE failure was uncommon in patients with HCC, which manifested predominantly as failure of treatment response of the initial intrahepatic tumor. Non-smooth tumor margin and tumors without complete capsule were associated with the presence of TACE failure.

MRI features associated with HCC histologic subtypes: a western American and European bicenter study

OBJECTIVES

To evaluate if preoperative MRI can predict the most frequent HCC subtypes in North American and European patients treated with surgical resection.

METHODS

A total of 119 HCCs in 97 patients were included in the North American group and 191 HCCs in 176 patients were included in the European group. Lesion subtyping was based on morphologic features and immuno-histopathological analysis. Two radiologists reviewed preoperative MRI and evaluated the presence of imaging features including LI-RADS major and ancillary features to identify clinical, biologic, and imaging features associated with the main HCC subtypes.

RESULTS

Sixty-four percent of HCCs were conventional. The most frequent subtypes were macrotrabecular-massive (MTM-15%) and steatohepatitic (13%). Necrosis (OR = 3.32; 95% CI: 1.39, 7.89; p = .0064) and observation size (OR = 1.011; 95% CI: 1.0022, 1.019; p = .014) were independent predictors of MTM-HCC. Fat in mass (OR = 15.07; 95% CI: 6.57, 34.57; p < .0001), tumor size (OR = 0.97; 95% CI: 0.96, 0.99; p = .0037), and absence of chronic HCV infection (OR = 0.24; 95% CI: 0.084, 0.67; p = .0068) were independent predictors of steatohepatitic HCC. Independent predictors of conventional HCCs were viral C hepatitis (OR = 3.20; 95% CI: 1.62, 6.34; p = .0008), absence of fat (OR = 0.25; 95% CI: 0.12, 0.52; p = .0002), absence of tumor in vein (OR = 0.34; 95% CI: 0.13, 0.84; p = .020), and higher tumor-to-liver ADC ratio (OR = 1.96; 95% CI: 1.14, 3.35; p = .014) CONCLUSION: MRI is useful in predicting the most frequent HCC subtypes even in cohorts with different distributions of liver disease etiologies and tumor subtypes which might have future treatment and management implications.

KEY POINTS

• Representation of both liver disease etiologies and HCC subtypes differed between the North American and European cohorts of patients. • Retrospective two-center study showed that liver MRI is useful in predicting the most frequent HCC subtypes.

Dynamic CT and Gadoxetic Acid-enhanced MRI Characteristics of P53-mutated Hepatocellular Carcinoma

Background Imaging markers of hepatocellular carcinoma (HCC) on the basis of molecular classification are important for predicting malignancy grade and prognosis. P53-mutated HCC is a major aggressive subtype; however, its imaging characteristics have not been clarified. Purpose To clarify the imaging characteristics of P53-mutated HCC at dynamic CT and gadoxetic acid-enhanced MRI that are correlated with its clinical features, pathologic findings, and prognosis. Materials and Methods In this retrospective single-center study, patients with surgically resected HCC between January 2015 and May 2018 in a university hospital were evaluated. HCC was classified into P53-mutated HCC and non-P53-mutated HCC using immunostaining. Dynamic CT and gadoxetic acid-enhanced MRI findings, clinical features, pathologic findings, and prognosis were compared using Mann-Whitney test, χ² test, multivariable regression analysis, receiver operating characteristic analysis, Kaplan-Meier method, and log-rank test. Immunohistochemical expression of P53, organic anion transporting polypeptide 1B3 (OATP1B3), and CD34 were evaluated, and the correlations were analyzed using the Pearson correlation test. Results In total, 149 patients (mean age, 67 years ± 9 [SD]; 103 men) with 173 HCCs were evaluated. P53-mutated HCC (n = 28) demonstrated higher serum α-fetoprotein (median, 127.5 ng/mL vs 5.5 ng/mL; P < .001), larger size (40.4 mm ± 29.7 vs 26.4 mm ± 20.5; P = .001), and higher rates of poorly differentiated HCC (22 of 28 [79%] vs 24 of 145 [17%]; P < .001). Dilated vasculature in the arterial phase of dynamic CT (odds ratio, 14; 95% CI: 3, 80; P = .002) and a lower relative enhancement ratio in the hepatobiliary phase (odds ratio, 0.05; 95% CI: 0.01, 0.34; cutoff value, 0.69; P = .002) independently predicted P53-mutated HCC. OATP1B3 expression and P53 expression were inversely correlated (P = .002; R = -0.24). Five-year overall survival was worse for P53-mutated HCC (50.0% vs 72.6%; P = .02). Conclusion Dilated vasculature at the arterial phase of dynamic CT and a lower relative enhancement ratio at the hepatobiliary phase of gadoxetic acid-enhanced MRI were useful markers for P53-mutated hepatocellular carcinoma with poor prognosis. © RSNA, 2022 Online supplemental material is available for this article.

The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis

Objective

The aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Method

The databases of PubMed, Cochrane library, Embase, Web of Science, Ovid MEDLINE, Springer, and Science Direct were searched for original studies from their inception to 20 August 2022. The quality of each study included was assessed according to the Quality Assessment of Diagnostic Accuracy Studies 2 and the radiomics quality score. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was plotted and the area under the curve (AUC) was calculated to evaluate the diagnostic accuracy. Sensitivity analysis and subgroup analysis were performed to explore the source of the heterogeneity. Deeks' test was used to assess publication bias.

Results

A total of 15 studies involving 981 patients were included. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.79 (95%CI: 0.72-0.85), 0.81 (95%CI: 0.73-0.87), 4.1 (95%CI:2.9-5.9), 0.26 (95%CI: 0.19-0.35), 16 (95%CI: 9-28), and 0.87 (95%CI: 0.84-0.89), respectively. The results showed great heterogeneity among the included studies. Sensitivity analysis indicated that the results of this study were statistically reliable. The results of subgroup analysis showed that hepatocyte-specific contrast media (HSCM) had equivalent sensitivity and equivalent specificity compared to the other set. The least absolute shrinkage and selection operator method had high sensitivity and specificity than other methods, respectively. The investigated area of the region of interest had high specificity compared to the volume of interest. The imaging-to-surgery interval of 15 days had higher sensitivity and slightly low specificity than the others. Deeks' test indicates that there was no publication bias (P=0.71).

Conclusion

Radiomics-based MRI has high accuracy in predicting MVI in HCC, and it can be considered as a non-invasive method for assessing MVI in HCC.

Prognostic factors for long-term outcome in bifocal hepatocellular carcinoma after resection

OBJECTIVES

This study aimed to evaluate whether the radiological similarity and clinicopathological factors determine the prognosis in bifocal hepatocellular carcinoma (bHCC) stratified by the Milan criteria.

METHODS

Consecutive patients with pathologically confirmed bHCC examined between January 2016 and December 2018 were retrospectively enrolled and grouped based on the Milan criteria. Two radiologists independently evaluated whether the imaging features of both tumors were consistent or not, which was defined as the radiological similarity. The clinicopathological data were also collected. The multivariable Cox regression was applied to separately identify the independent factors for recurrence-free survival (RFS) and overall survival (OS) in bHCC within and beyond the Milan criteria.

RESULTS

A total of 193 patients were evaluated and divided into the within the Milan criteria group (n = 72) and the beyond the Milan criteria group (n = 121). bHCC within the Milan criteria showed a significantly better prognosis than those beyond the criteria. In the within the Milan criteria group, HBV-DNA load  >10⁴ IU/mL, microvascular invasion (MVI), and different enhancement patterns were independently associated with poor RFS. MVI was an independent prognostic factor for poor OS. In the beyond the Milan criteria group, HBV infection, MVI, increased ratio of the larger to the smaller tumor diameter (RLSD) value, and low comprehensive similarity were associated with shorter RFS, whereas MVI and increased RLSD value were independent predictors for poor OS.

CONCLUSIONS

Our study revealed that in addition to MVI- and HBV-related factors, similarity in imaging features between lesions of bHCC is associated with the long-term prognosis.

KEY POINTS

• The prognosis of bifocal HCC patients within the Milan criteria is significantly better than those beyond the criteria. • The similarity in imaging features between lesions of bHCC was an independent prognostic factor. • The more similar the bifocal lesions are in imaging features, the better the prognosis is.

Risk Group Stratification for Recurrence-Free Survival and Early Tumor Recurrence after Radiofrequency Ablation for Hepatocellular Carcinoma

PURPOSE

Although the prognosis after radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC) may vary according to different risk levels, there is no standardized follow-up protocol according to each patient's risk. This study aimed to stratify patients according to their risk of recurrence-free survival (RFS) and early (≤2 years) tumor recurrence (ETR) after RFA for HCC based on predictive models and nomograms and to compare the survival times of the risk groups derived from the models.

METHODS

Patients who underwent RFA for a single HCC (≤3 cm) between January 2012 and March 2014 (n = 152) were retrospectively reviewed. Patients were classified into low-, intermediate-, and high-risk groups based on the total nomogram points for RFS and ETR, respectively, and compared for each outcome. Restricted mean survival times (RMSTs) in the three risk groups were evaluated for both RFS and ETR to quantitatively evaluate the difference in survival times.

RESULTS

Predictive models for RFS and ETR were constructed with c-indices of 0.704 and 0.730, respectively. The high- and intermediate-risk groups for RFS had an 8.5-fold and 2.9-fold higher risk of events than the low-risk group (both p < 0.001), respectively. The high- and intermediate-risk groups for ETR had a 17.7-fold and 7.0-fold higher risk than the low-risk group (both p < 0.001), respectively. The RMST in the high-risk group was significantly lower than that in the other two groups 9 months after RFA, and that in the intermediate-risk group became lower than that in the low-risk group after 21 months with RFS and 24 months with ETR.

CONCLUSION

Our predictive models were able to stratify patients into three groups according to their risk of RFS and ETR after RFA for HCC. Differences in RMSTs may be used to establish different follow-up protocols for the three risk groups.

Predictive models and early postoperative recurrence evaluation for hepatocellular carcinoma based on gadoxetic acid-enhanced MR imaging

BACKGROUND

The prognosis of hepatocellular carcinoma (HCC) is still poor largely due to the high incidence of recurrence. We aimed to develop and validate predictive models of early postoperative recurrence for HCC using clinical and gadoxetic acid-enhanced magnetic resonance (MR) imaging-based findings.

METHODS

In this retrospective case-control study, 209 HCC patients, who underwent gadoxetic acid-enhanced MR imaging before curative-intent resection, were enrolled. Boruta algorithm and backward stepwise selection with Akaike information criterion (AIC) were used for variables selection Random forest, Gradient-Boosted decision tree and logistic regression model analysis were used for model development. The area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis were used to evaluate model's performance.

RESULTS

One random forest model with Boruta algorithm (RF-Boruta) was developed consisting of preoperative serum ALT and AFP levels and six MRI findings, while preoperative serum AST and AFP levels and four MRI findings were included in one logistic regression model with backward stepwise selection method (Logistic-AIC).The two predictive models demonstrated good discrimination performance in both the training set (RF-Boruta: AUC, 0.820; Logistic-AIC: AUC, 0.853), internal validation set (RF-Boruta: AUC, 0.857, Logistic-AIC: AUC, 0.812) and external validation set(RF-Boruta: AUC, 0.805, Logistic-AIC: AUC, 0.789). Besides, in both the internal validation and external validation sets, the RF-Boruta model outperformed Barcelona Clinic Liver Cancer (BCLC) stage (p < 0.05).

CONCLUSIONS

The RF-Boruta and Logistic-AIC models with good prediction performance for early postoperative recurrence may lead to optimal and comprehensive treatment approaches, and further improve the prognosis of HCC after resection.

Prospective validation of the Eastern Staging in predicting survival after surgical resection for patients with hepatocellular carcinoma: a multicenter study from China

BACKGROUND

The Eastern Staging System, which was specially developed for patients undergoing surgical resection for hepatocellular carcinoma (HCC), has been proposed for more than ten years. To prospectively validate the predictive accuracy of the Eastern staging on long-term survival after HCC resection.

METHODS

Patients who underwent hepatectomy for HCC from 2011 to 2020 at 10 Chinese hospitals were identified from a prospectively collected database. The survival predictive accuracy was evaluated and compared between the Eastern Staging with six other staging systems, including the JIS, BCLC, Okuda, CLIP, 8th AJCC TNM, and HKLC staging.

RESULTS

Among 2365 patients, the 1-, 3-, and 5-year overall survival rates were 84.2%, 64.5%, and 52.6%, respectively. Among these seven staging systems, the Eastern staging was associated with the best monotonicity of gradients (linear trend χ2: 408.5) and homogeneity (likelihood ratio χ2: 447.3), and the highest discriminatory ability (the areas under curves for 1-, 3-, and 5-year mortality: 0.776, 0.787, and 0.768, respectively). In addition, the Eastern staging was the most informative staging system in predicting survival (Akaike information criterion: 2982.33).

CONCLUSION

Using a large multicenter prospectively collected database, the Eastern Staging was found to show the best predictive accuracy on long-term overall survival in patients with resectable HCC than the other 6 commonly-used staging systems.

Corona enhancement combined with microvascular invasion for prognosis prediction of macrotrabecular-massive hepatocellular carcinoma subtype

Objectives

The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is aggressive and associated with an unfavorable prognosis. This study aimed to characterize MTM-HCC features based on contrast-enhanced MRI and to evaluate the prognosis of imaging characteristics combined with pathology for predicting early recurrence and overall survival after surgery.

Methods

This retrospective study included 123 patients with HCC that underwent preoperative contrast-enhanced MRI and surgery, between July 2020 and October 2021. Multivariable logistic regression was performed to investigate factors associated with MTM-HCC. Predictors of early recurrence were determined with a Cox proportional hazards model and validated in a separate retrospective cohort.

Results

The primary cohort included 53 patients with MTM-HCC (median age 59 years; 46 male and 7 females; median BMI 23.5 kg/m2) and 70 subjects with non-MTM HCC (median age 61.5 years; 55 male and 15 females; median BMI 22.6 kg/m2) (All P>0.05). The multivariate analysis identified corona enhancement (odds ratio [OR]=2.52, 95% CI: 1.02-6.24; P=0.045) as an independent predictor of the MTM-HCC subtype. The multiple Cox regression analysis identified corona enhancement (hazard ratio [HR]=2.56, 95% CI: 1.08-6.08; P=0.033) and MVI (HR=2.45, 95% CI: 1.40-4.30; P=0.002) as independent predictors of early recurrence (area under the curve=0.790, P<0.001). The prognostic significance of these markers was confirmed by comparing results in the validation cohort to those from the primary cohort. Corona enhancement combined with MVI was significantly associated with poor outcomes after surgery.

Conclusions

A nomogram for predicting early recurrence based on corona enhancement and MVI could be used to characterize patients with MTM-HCC and predict their prognosis for early recurrence and overall survival after surgery.

Radiomics on Gadoxetate Disodium-enhanced MRI: Non-invasively Identifying Glypican 3-Positive Hepatocellular Carcinoma and Postoperative Recurrence

RATIONALE AND OBJECTIVES

To investigate the impact of preoperative gadoxetate disodium (EOB) MRI-based radiomics on predicting glypican 3 (GPC3)-positive expression and the relevant recurrence-free survival (RFS) of HCC ≤ 5 cm.

MATERIALS AND METHODS

Between January 2014 and October 2018, 259 patients with solitary HCC ≤ 5 cm who underwent hepatectomy and preoperative EOB-MRI were retrieved. Multivariate logistic regression was implemented to identify independent predictors for GPC3. By combining five feature selection strategies and three classifiers, 15 GPC3-oriented radiomics models could be constructed, the best of which with independent clinicoradiologic predictors was integrated into the comprehensive nomogram.

RESULTS

GPC3 was an independent risk factor of postoperative recrudescence for HCC. Alpha-fetoprotein >20 ng/mL, homogenous T2 signal and hypointensity on hepatobiliary phase were independently related to GPC3-positive expression in the clinicoradiologic model. With 10 features selected by support vector machines-recursive feature elimination, logistic regression-based classifier achieved the best performance among 15 radiomics models. After five-fold cross-validation, our comprehensive nomogram acquired better average area under receiver operating characteristic curves (training and validation cohorts: 0.931 vs. 0.943) than the clinicoradiologic algorithm (0.738 vs. 0.739) and the optimal radiomics model (0.943 vs. 0.931). Net reclassification indexes further demonstrated the superiority of GPC3 nomogram over clinicoradiologic and radiomics algorithms (46.54%, p < 0.001; 7.84%, p = 0.207). Meanwhile, higher radiomics score significantly shortened the median RFS (from >77.9 to 48.2 months, p = 0.044), which was analogue to that of the histological GPC3-positive phenotype (from >73.9 to 43.2 months, p < 0.001).

CONCLUSIONS

Preoperative EOB-MRI radiomics-based nomogram satisfactorily distinguished GPC3 status and outcomes of solitary HCC ≤ 5 cm.

A Synopsis of Artificial Intelligence and its Applications in Surgery

Artificial intelligence (AI) has made steady in-roads into the healthcare scenario over the last decade. While widespread adoption into clinical practice remains elusive, the outreach of this discipline has progressed beyond the physician scientist, and different facets of this technology have been incorporated into the care of surgical patients. New AI applications are developing at rapid pace, and it is imperative that the general surgeon be aware of the broad utility of AI as applicable in his or her day-to-day practice, so that healthcare continues to remain up-to-date and evidence based. This review provides a broad account of the tip of the AI iceberg and highlights it potential for positively impacting surgical care.

Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence

Microvascular invasion of hepatocellular carcinoma is an important factor affecting tumor recurrence after liver resection and liver transplantation. There are many ways to classify microvascular invasion, however, an international consensus is urgently needed. Recently, artificial intelligence has emerged as an important tool for improving the clinical management of hepatocellular carcinoma. Many studies about microvascular invasion currently focus on preoperative and prognosis prediction of microvascular invasion using artificial intelligence. In this paper, we review the definition and staging of microvascular invasion, especially the diagnosis of it by using artificial intelligence. In preoperative prediction, deep learning based on multimodal data modeling of radiomics-screened features, clinical features, and medical images is currently the most effective means. In prognostic prediction, pathology is the gold standard, and the techniques used should more effectively utilize the global features of the pathology images.

Predicting hepatocellular carcinoma early recurrence after ablation based on magnetic resonance imaging radiomics nomogram

BACKGROUND

The aim of this study is to investigate a model for predicting the early recurrence of hepatocellular carcinoma (HCC) after ablation.

METHODS

A total of 181 patients with HCC after ablation (train group was 119 cases; validation group was 62 cases) were enrolled. The cases of early recurrence in the set of train and validation were 63 and 31, respectively. Radiomics features were extracted from the enhanced magnetic resonance imaging scanning, including pre-contrast injection, arterial phase, late arterial phase, portal venous phase, and delayed phase. The least absolute shrinkage and selection operator cox proportional hazards regression after univariate and multivariate analysis was used to screen radiomics features and build integrated models. The nomograms predicting recurrence and survival of patients of HCC after ablation were established based on the clinical, imaging, and radiomics features. The area under the curve (AUC) of the receiver operating characteristic curve and C-index for the train and validation group was used to evaluate model efficacy.

RESULTS

Four radiomics features were selected out of 34 texture features to formulate the rad-score. Multivariate analyses suggested that the rad-score, number of lesions, integrity of the capsule, pathological type, and alpha-fetoprotein were independent influencing factors. The AUC of predicting early recurrence at 1, 2, and 3 years in the train group was 0.79 (95% CI: 0.72-0.88), 0.72 (95% CI: 0.63-0.82), and 0.71 (95% CI: 0.61-0.83), respectively. The AUC of predicting early recurrence at 1, 2, and 3 years in the validation group was 0.72 (95% CI: 0.58-0.84), 0.61 (95% CI: 0.45-0.78) and 0.64 (95% CI: 0.40-0.87).

CONCLUSION

The model for early recurrence of HCC after ablation based on the clinical, imaging, and radiomics features presented good predictive performance. This may facilitate the early treatment of patients.

Prediction for Aggressiveness and Postoperative Recurrence of Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

To investigate the predictive value of gadoxetic acid-enhanced magnetic resonance imaging (MRI) features on the pathologic grade, microvascular invasion (MVI), and cytokeratin-19 (CK19) expression in hepatocellular carcinomas (HCC), and to evaluate their association with postoperative recurrence of HCC.

MATERIALS AND METHODS

This retrospective study included 147 patients with surgically confirmed HCCs who underwent gadoxetic-enhanced MRI. The lesions were evaluated quantitatively in terms of the relative enhancement ratio (RER), and qualitatively based on imaging features and clinical parameters. Logistic regression analyses were performed to investigate the value of these parameters in predicting the pathologic grade, MVI, and CK19 in HCC. Predictive factors for postoperative recurrence were determined using a Cox proportional hazards model.

RESULTS

Peritumoral enhancement (odds ratio [OR], 3.396; p = 0.025) was an independent predictor of high pathologic grades. Serum protein induced by vitamin K absence or antagonist (PIVKA) level > 40 mAU/mL (OR, 3.763; p = 0.018) and peritumoral hypointensity (OR, 4.343; p = 0.003) were independent predictors of MVI. Predictors of CK19 included serum alpha-fetoprotein (AFP) level > 400 ng/mL (OR, 4.576; p = 0.005), rim enhancement (OR, 5.493; p = 0.024), and lower RER (OR, 0.013; p = 0.011). Peritumoral hypointensity (hazard ratio [HR], 1.957; p = 0.027) and poor pathologic grades (HR, 2.339; p = 0.043) were independent predictors of recurrence.

CONCLUSION

We demonstrated the value of preoperative gadoxetic-enhanced MRI in predicting aggressive pathological features of HCC. Poor pathologic grades and peritumoral hypointensity may independently predict the recurrence of HCC.

Systemic analysis identifying PVT1 / DUSP13 axis for microvascular invasion in hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is an independent detrimental risk factor for tumor recurrence and poor survival in hepatocellular carcinoma (HCC). Competitive endogenous RNA (ceRNA) networks play a pivotal role in the modulation of carcinogenesis and progression among diverse tumor types. However, whether the ceRNA mechanisms are engaged in promoting the MVI process in patients with HCC remains unknown.

METHODS

A ceRNA regulatory network was constructed based on RNA-seq data of patients with HCC from The Cancer Genome Atlas (TCGA) database. In total, 10 hub genes of the ceRNA network were identified using four algorithms: "MCC," "Degree," "Betweenness," and "Stress." Transcriptional expressions were verified by in situ hybridization using clinical samples. Interactions between ceRNA modules were validated by luciferase reporting assay. Logistic regression analysis, correlation analysis, enrichment analysis, promoter region analysis, methylation analysis, and immune infiltration analysis were performed to further investigate the molecular mechanisms and clinical transformation value.

RESULTS

The ceRNA regulatory network featuring a tumor invasion phenotype consisting of 3 long noncoding RNAs, 3 microRNAs, and 93 mRNAs was constructed using transcriptional data from the TCGA database. Systemic analysis and experimentally validation identified a ceRNA network (PVT1/miR-1258/DUSP13 axis) characterized by lipid regulatory potential, immune properties, and abnormal methylation states in patients with HCC and MVI. Meanwhile, 28 transcriptional factors were identified as potential promotors of PVT1 with 3 transcriptional factors MXD3, ZNF580, and KDM1A promising as therapeutic targets in patients with HCC and MVI. Furthermore, miR-1258 was an independent predictor for MVI in patients with HCC.

CONCLUSION

The PVT1/DUSP13 axis is significantly associated with MVI progression in HCC patients. This study provides new insight into mechanisms related to lipids, immune phenotypes, and abnormal epigenetics in oncology research.

Global research status and frontiers on microvascular invasion of hepatocellular carcinoma: A bibliometric and visualized analysis

Introduction

Over the past decade, several studies on the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) have been published. However, they have not quantitatively analyzed the remarkable impact of MVI. Therefore, a more comprehensive understanding of the field is now needed. This study aims to analyze the evolution of HCC-MVI research and to systematically evaluate the scientific outputs using bibliometric citation analysis.

Methods

A systematic search was conducted on the Web of Science Core Collection on 2 May 2022 to retrieve studies on HCC-MVI published between 2013 and 2022. Then, a bibliometric analysis of the publications was performed using CiteSpace, VOSviewer, and other visualization tools.

Results

A total of 1,208 articles on HCC MVI were identified. Of these, China (n = 518) was the most prolific country, and Fudan University (n = 90) was the most notable institution. Furthermore, we observed that Lau Wan Yee participated in most studies (n = 26), and Frontiers in Oncology (IF2020:6.24) published the highest number of documents (n = 49) on this subject, with 138 publications. The paper "Bray F, 2018, CA-CANCER J CLIN, V68, P394" has the highest number of co-cited references, with 119 citations. In addition, the top three keywords were "survival", "recurrence", and "microvascular invasion". Moreover, the research hot spots and frontiers of HCC-MVI for the last 3 years included imaging characteristics and transarterial chemoembolization (TACE) therapy studies.

Conclusions

This study comprehensively summarized the most significant HCC-MVI documents from past literature and highlighted key contributions made to the advancement of this subject and the advancement of this field over the past decade. The trend of MVI research will gradually shift from risk factors and prognosis studies to imaging characteristics and TACE therapy studies.

Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction

BACKGROUND AND AIM

Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers.

METHODS

A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients' plasma samples and 24 healthy plasma samples were used for genome-wide methylation sequencing and subsequent methylation haplotype block (MHB) analysis. Predictive models were constructed based on selected MHB markers and 3-cross validation was used.

RESULTS

We grouped 35 HCC patients into 2 categories, including the MVI- group with 17 tissue and plasma samples, and MVI + group with 18 tissue and plasma samples. We identified a tissue DNA methylation signature with an AUC of 98.0% and a circulating free DNA (cfDNA) methylation signature with an AUC of 96.0% for HCC detection. Furthermore, we established a tissue DNA methylation signature for MVI status prediction, and achieved an AUC of 85.9%. Based on the MVI status predicted by the DNA methylation signature, the recurrence-free survival (RFS) and overall survival (OS) were significantly better in the predicted MVI- group than that in the predicted MVI + group.

CONCLUSIONS

In this study, we identified a cfDNA methylation signature for HCC detection and a tissue DNA methylation signature for MVI status prediction with high accuracy.

Clinical and imaging features preoperative evaluation of histological grade and microvascular infiltration of hepatocellular carcinoma

Background

To predict the histological grade and microvascular invasion (MVI) in patients with HCC.

Methods

A retrospective analysis was conducted on 175 patients who underwent MRI enhancement scanning (from September 2016.9 to October 2020). They were divided into MVI positive, MVI negative, Grade-high and Grade-low groups.

Results

The AFP of 175 HCC patients distributed in MVI positive and negative groups, Grade-low and Grade-high groups were statistically significant (P = 0.002 and 0.03, respectively). Multiple HCC lesions were more common in MVI positive and Grade-high groups. Correspondingly, more single lesions were found in MVI negative and Grade-low groups (P = 0.005 and 0.019, respectively). Capsule on MRI was more common in MVI negative and Grade-high groups, and the difference was statistically significant (P = 0.02 and 0.011, respectively). There were statistical differences in the distribution of three MRI signs: artistic rim enhancement, artistic peripheral enhancement, and tumor margin between MVI positive and MVI negative groups (P = 0.001, < 0.001, and < 0.001, respectively). Tumor hypointensity on HBP was significantly different between MVI positive and negative groups (P < 0.001).

Conclusions

Our research shows that preoperative enhanced imaging can be used to predict MVI and tumor differentiation grade of HCC. The prognosis of MVI-negative group was better than that of MVI-positive group.

The value of quantitative MR elastography-based stiffness for assessing the microvascular invasion grade in hepatocellular carcinoma

OBJECTIVES

To evaluate the potential diagnostic value of MR elastography (MRE)-based stiffness to noninvasively predict the microvascular invasion (MVI) grade in hepatocellular carcinoma (HCC).

METHODS

One hundred eighty-five patients with histopathology-proven HCC who underwent MRI and MRE examinations before hepatectomy were retrospectively enrolled. According to the three-tiered MVI grading system, the MVI was divided into negative-MVI (n = 89) and positive-MVI (n = 96) groups, and the latter group was categorized into mild-MVI (n = 49) and severe-MVI (n = 47) subgroups. Logistic regression and area under the receiver operating characteristic curve (AUC) analyses were used to determine the predictors associated with MVI grade and analyze their performances, respectively.

RESULTS

Among the 185 patients, tumor size ≥ 50 mm (p = 0.031), tumor stiffness (TS)/liver stiffness (LS) > 1.47 (p = 0.001), TS > 4.33 kPa (p < 0.001), and nonsmooth tumor margin (p = 0.006) were significant independent predictors for positive-MVI. Further analyzing the subgroups, tumor size ≥ 50 mm (p < 0.001), TS > 5.35 kPa (p = 0.001), and AFP level > 400 ng/mL (p = 0.044) were independently associated with severe-MVI. The models incorporating MRE and clinical-radiological features together performed better for evaluating positive-MVI (AUC: 0.846) and severe-MVI (AUC: 0.802) than the models using clinical-radiological predictors alone (AUC: positive-/severe-MVI, 0.737/0.743). Analysis of recurrence-free survival and overall survival showed the predicted positive-MVI/severe-MVI groups based on combined models had significantly poorer prognoses than predicted negative-MVI/mild-MVI groups, respectively (all p < 0.05).

CONCLUSIONS

MRE-based stiffness was an independent predictor for both the positive-MVI and severe-MVI. The combination of MRE and clinical-radiological models might be a useful tool for evaluating HCC patients' prognoses underwent hepatectomy by preoperatively predicting the MVI grade.

KEY POINTS

• The severe-microvascular invasion (MVI) grade had the highest tumor stiffness (TS), followed by mild-MVI and non-MVI, and there were significances among the three different MVI grades. • MR elastography (MRE)-based stiffness value was an independent predictor of positive-MVI and severe-MVI in hepatocellular carcinoma (HCC) preoperatively. • When combined with clinical-radiological models, MRE could significantly improve the predictive performance for MVI grade. Patients with predicted positive-MVI/severe-MVI based on the combined models had worse recurrence-free survival and overall survival than those with negative-MVI/mild-MVI, respectively.

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.

The growth pattern of liver metastases on MRI predicts early recurrence in patients with colorectal cancer: a multicenter study

OBJECTIVES

The multicenter study aimed to explore the relationship between the growth pattern of liver metastases on preoperative MRI and early recurrence in patients with colorectal cancer liver metastases (CRCLM) after surgery.

METHODS

A total of 348 CRCLM patients from 3 independent centers were enrolled, including 130 patients with 339 liver metastases in the primary cohort and 218 patients in validation cohorts. Referring to the gross classification of hepatocellular carcinoma (HCC), the growth pattern of each liver metastasis on MRI was classified into four types: rough, smooth, focal extranodular protuberant (FEP), and nodular confluent (NC). Disease-free survival (DFS) curve was constructed using the Kaplan-Meier method.

RESULTS

In primary cohort, 42 (12.4%) of the 339 liver metastases were rough type, 237 (69.9%) were smooth type, 29 (8.6%) were FEP type, and 31 (9.1%) were NC type. Those patients with FEP- and/or NC-type liver metastases had shorter DFS than those without such metastases (p < 0.05). However, there were no significant differences in DFS between patients with rough- and smooth-type liver metastases and those without such metastases. The patients with FEP- and/or NC-type liver metastases also had shorter DFS than those without such metastases in two external validation cohorts. In addition, 40.5% of high-risk-type (FEP and NC) liver metastases converted to low-risk types (rough and smooth) after neoadjuvant chemotherapy.

CONCLUSION

The FEP- and NC-type liver metastases were associated with early recurrence, which may facilitate the clinical treatment of CRCLM patients.

KEY POINTS

• In the primary cohort, patients with FEP- and NC-type metastases had shorter disease-free survival (DFS) and a higher intrahepatic recurrence rate than patients without such metastases in the liver. • In the primary cohort, there were no significant differences in DFS or intrahepatic recurrence rate between patients with rough- and smooth-type metastases and those without such metastases in the liver. • High-risk patients had shorter DFS and a higher intrahepatic recurrence rate than low-risk patients in primary and external validation cohorts.

Preoperative Evaluation of Gd-EOB-DTPA-Enhanced MRI Radiomics-Based Nomogram in Small Solitary Hepatocellular Carcinoma (≤3 cm) With Microvascular Invasion: A Two-Center Study

BACKGROUND

Preoperative evaluation of microvascular invasion (MVI) in small solitary hepatocellular carcinoma (HCC; maximum lesion diameter ≤ 3 cm) is important for treatment decisions.

PURPOSE

To apply gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI to develop and validate a nomogram for preoperative evaluation of MVI in small solitary HCC and to compare the effectiveness of radiomics evaluation models based on different volumes of interest (VOIs).

STUDY TYPE

Retrospective.

POPULATION

A total of 196 patients include 62 MVI-positive and 134 MVI-negative patients were enrolled (training cohort, n = 105; testing cohort, n = 45; external validation cohort, n = 46).

FIELD STRENGTH/SEQUENCE

3.0 T, fat suppressed fast-spin-echo T2-weighted and Gd-EOB-DTPA-enhanced T1-weighted magnetization-prepared rapid gradient-echo sequences.

ASSESSMENT

Radiomics features were extracted on T2-weighted, arterial phase (AP), and hepatobiliary phase (HBP) images from different VOIs (VOI_intratumor and VOI_{intratumor+peritumor} ) and filtered by the least absolute shrinkage selection operator (LASSO) regression. From VOI_intratumor and VOI_{intratumor+peritumor} , eight radiomics models were constructed based on three MRI sequences (T2-weighted, AP, and HBP) and fused sequences (combined of three sequences). Nomograms were constructed of a clinical-radiological (CR) model and a clinical-radiological-radiomics (CRR) model.

STATISTICAL TESTS

One-way analysis of variance, independent t-test, Chi-square test or Fisher's exact test, Wilcoxon rank-sum test, LASSO, logistic regression analysis, area under the curve (AUC), nomograms, decision curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI) analyses, and DeLong test.

RESULTS

Among eight radiomics models, the fused sequences-based VOI_{intratumor+peritumor} radiomics model showed the best performance. The CRR model containing the best performance radiomics model and CR model with the AUC values were 0.934, 0.889, and 0.875, respectively. NRI and IDI analyses showed that the CRR model improved evaluation efficacy over the CR model for all three cohorts (all P-value <0.05).

DATA CONCLUSION

The CRR model nomogram could preoperatively evaluate MVI in small solitary HCC. The radiomics model based on VOI_{intratumor+peritumor} might achieve better evaluation results.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY STAGE: 2.

Prognostic implications of CT/MRI LI-RADS in hepatocellular carcinoma: State of the art and future directions

Hepatocellular carcinoma (HCC) is the fourth most lethal malignancy with an increasing incidence worldwide. Management of HCC has followed several clinical staging systems that rely on tumour morphologic characteristics and clinical variables. However, these algorithms are unlikely to profile the full landscape of tumour aggressiveness and allow accurate prognosis stratification. Noninvasive imaging biomarkers on computed tomography (CT) or magnetic resonance imaging (MRI) exhibit a promising prospect to refine the prognostication of HCC. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, techniques, interpretation, reporting and data collection of liver imaging. At present, it has been widely accepted as an effective diagnostic system for HCC in at-risk patients. Emerging data have provided new insights into the potential of CT/MRI LI-RADS in HCC prognostication, which may help refine the prognostic paradigm of HCC that promises to direct individualized management and improve patient outcomes. Therefore, this review aims to summarize several prognostic imaging features at CT/MRI for patients with HCC; the available evidence regarding the use of LI-RDAS for evaluation of tumour biology and clinical outcomes, pitfalls of current literature, and future directions for LI-RADS in the management of HCC.