Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Preoperative Gd-EOB-DTPA-Dynamic Enhanced MRI and Histopathological Correlation. Contrast Media Mol Imaging. 2018;2018:9674565. [PMID: 29606926 PMCID: PMC5828041 DOI: 10.1155/2018/9674565]

Radiogenomics nomogram based on MRI and microRNAs to predict microvascular invasion of hepatocellular carcinoma

Purpose

This study aimed to develop and validate a radiogenomics nomogram for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) on the basis of MRI and microRNAs (miRNAs).

Materials and methods

This cohort study included 168 patients (training cohort: n = 116; validation cohort: n = 52) with pathologically confirmed HCC, who underwent preoperative MRI and plasma miRNA examination. Univariate and multivariate logistic regressions were used to identify independent risk factors associated with MVI. These risk factors were used to produce a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic curve (ROC) analysis, sensitivity, specificity, accuracy, and F1-score. Decision curve analysis was performed to determine whether the nomogram was clinically useful.

Results

The independent risk factors for MVI were maximum tumor length, rad-score, and miRNA-21 (all P < 0.001). The sensitivity, specificity, accuracy, and F1-score of the nomogram in the validation cohort were 0.970, 0.722, 0.884, and 0.916, respectively. The AUC of the nomogram was 0.900 (95% CI: 0.808-0.992) in the validation cohort, higher than that of any other single factor model (maximum tumor length, rad-score, and miRNA-21).

Conclusion

The radiogenomics nomogram shows satisfactory predictive performance in predicting MVI in HCC and provides a feasible and practical reference for tumor treatment decisions.

Exosomal mRNA in plasma serves as a predictive marker for microvascular invasion in hepatocellular carcinoma

BACKGROUND AND AIM

There is a pressing need for non-invasive preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study investigates the potential of exosome-derived mRNA in plasma as a biomarker for diagnosing MVI.

METHODS

Patients with suspected HCC undergoing hepatectomy were prospectively recruited for preoperative peripheral blood collection. Exosomal RNA profiling was conducted using RNA sequencing in the discovery cohort, followed by differential expression analysis to identify candidate targets. We employed multiplexed droplet digital PCR technology to efficiently validate them in a larger sample size cohort.

RESULTS

A total of 131 HCC patients were ultimately enrolled, with 37 in the discovery cohort and 94 in the validation cohort. In the validation cohort, the expression levels of RSAD2, PRPSAP1, and HOXA2 were slightly elevated while CHMP4A showed a slight decrease in patients with MVI compared with those without MVI. These trends were consistent with the findings in the discovery cohort, although they did not reach statistical significance (P > 0.05). Notably, the expression level of exosomal PRPSAP1 in plasma was significantly higher in patients with more than 5 MVI than in those without MVI (0.147 vs 0.070, P = 0.035).

CONCLUSION

This study unveils the potential of exosome-derived PRPSAP1 in plasma as a promising indicator for predicting MVI status preoperatively.

Evaluation and Prognostication of Gd-EOB-DTPA MRI and CT in Patients With Macrotrabecular-Massive Hepatocellular Carcinoma

BACKGROUND

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is highly aggressive. Comparing the diagnosis ability of CT and gadoxetate disodium (Gd-EOB-DTPA) MRI for MTM-HCC are lacking.

PURPOSE

To compare the performance of Gd-EOB-DTPA MRI and CT for differentiating MTM-HCC from non-MTM-HCC, and determine the prognostic indicator.

STUDY TYPE

Retrospective.

SUBJECTS

Post-surgery HCC patients, divided into the training (N = 272) and external validation (N = 44) cohorts.

FIELD STRENGTH/SEQUENCE

3.0 T, T1-weighted imaging, in-opp phase, and T1-weighted volumetric interpolated breath-hold examination/liver acquisition with volume acceleration; enhanced CT.

ASSESSMENT

Three radiologists evaluated clinical characteristics (sex, age, liver disease, liver function, blood routine, alpha-fetoprotein [AFP] and prothrombin time international normalization ratio [PT-INR]) and imaging features (tumor length, intratumor fat, hemorrhage, arterial phase peritumoral enhancement, intratumor necrosis or ischemia, capsule, and peritumoral hepatobiliary phase [HBP] hypointensity). Compared the performance of CT and MRI for diagnosing MTM-HCC. Follow-up occurred every 3-6 months, and nomogram demonstrated the probability of MTM-HCC.

STATISTICAL TESTS

Fisher test, t-test or Wilcoxon rank-sum test, area under the curve (AUC), 95% confidence interval (CI), multivariable logistic regression, Kaplan-Meier curve, and Cox proportional hazards. Significance level: P < 0.05.

RESULTS

Gd-EOB-DTPA MRI (AUC: 0.793; 95% CI, 0.740-0.839) outperformed CT (AUC: 0.747; 95% CI, 0.691-0.797) in the training cohort. The nomogram, incorporating AFP, PT-INR, and MRI features (non-intratumor fat, incomplete capsule, intratumor necrosis or ischemia, and peritumoral HBP hypointensity) demonstrated powerful performance for diagnosing MTM-HCC with an AUC of 0.826 (95% CI, 0.631-1.000) in the external validation cohort. Median follow-up was 347 days (interquartile range [IQR], 606 days) for the training cohort and 222 days (IQR, 441 days) for external validation cohort. Intratumor necrosis or ischemia was an independent indicator for poor prognosis.

DATA CONCLUSION

Gd-EOB-DTPA MRI might assist in preoperative diagnosis of MTM-HCC, and intratumor necrosis or ischemia was associated with poor prognosis.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study

BACKGROUND

Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC.

AIM

To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC.

METHODS

This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR).

RESULTS

Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%.

CONCLUSION

Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.

Prediction of immunocyte infiltration and prognosis in postoperative hepatitis B virus-related hepatocellular carcinoma patients using magnetic resonance imaging

Background

The immune microenvironment (IME) is closely associated with prognosis and therapeutic response of hepatitis B virus-related hepatocellular carcinoma (HBV-HCC). Multi-parametric magnetic resonance imaging (MRI) enables non-invasive assessment of IME and predicts prognosis in HBV-HCC. We aimed to construct an MRI prediction model of the immunocyte-infiltration subtypes and explore its prognostic significance.

Methods

HBV-HCC patients at the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) with radical surgery (between 1 October and 30 December 2021) were prospectively enrolled. Patients with pathologically proven HCC (between 1 December 2013 and 30 October 2019) were retrospectively enrolled. Pearson correlation analysis was used to examine the relationship between the immunocyte-infiltration counts and MRI parameters. An MRI prediction model of immunocyte-infiltration subtypes was constructed in prospective cohort. Kaplan-Meier survival analysis was used to analyse its prognostic significance in the retrospective cohort.

Results

Twenty-four patients were prospectively enrolled to construct the MRI prediction model. Eighty-nine patients were retrospectively enrolled to determine its prognostic significance. MRI parameters (relative enhancement, ratio of the apparent diffusion coefficient value of tumoral region to peritumoral region [rADC], T1 value) correlated significantly with the immunocyte-infiltration counts (leukocytes, T help cells, PD1+Tc cells, B lymphocytes). rADC differed significantly between high and low immunocyte-infiltration groups (1.47 ± 0.36 vs 1.09 ± 0.25, P = 0.009). The area under the curve of the MRI model was 0.787 (95% confidence interval 0.587-0.987). Based on the MRI model, the recurrence-free time was longer in the high immunocyte-infiltration group than in the low immunocyte-infiltration group (P = 0.026).

Conclusions

MRI is a non-invasive method for assessing the IME and immunocyte-infiltration subtypes, and predicting prognosis in post-operative HBV-HCC patients.

Combined early dynamic 18F-FDG PET/CT and conventional whole-body 18F-FDG PET/CT in hepatocellular carcinoma

OBJECTIVE

To investigate the diagnostic value of early dynamic 18F-FDG PET/CT(ED 18F-FDG PET/CT) combined with conventional whole-body 18F-FDG PET/CT(WB 18F-FDG PET/CT) in hepatocellular carcinoma (HCC), as well as the difference of early dynamic blood flow parameters and maximum standardized uptake value (SUVmax) in HCC patients with/without liver cirrhosis or microvascular invasion (MVI).

METHODS

Twenty-two consecutive patients (mean age 57.8 years) with 28 established HCC lesions (mean size 4.5 cm) underwent a blood flow study with an 18F-FDG dynamic scan divided into 24 sequences of 5 s each and a standard PET/CT scan. On the ED PET/CT study, an experienced PET/CT physician obtained volumes of interest (VOIs) where three blood flow estimates (time to peak [TTP], blood flow [BF], and hepatic perfusion index [HPI]) were calculated. On the WB PET/CT study, a VOI was placed on the fused scan for each HCC and maximum standardized uptake value (SUVmax) was obtained. Comparison of blood flow estimates, SUVmax, and tumor/background ratio (TNR) was performed among HCCs with and without angioinvasion, as well as HCCs in cirrhotic and non-cirrhotic liver.

RESULTS

Compared with WB 18F-FDG PET/CT alone, ED combined with WB 18F-FDG PET/CT can significantly increase the detection rate of moderately differentiated and poorly differentiated HCCs (both P < 0.05). HPI was higher in HCCs in patients with liver cirrhosis than those without liver cirrhosis (P = 0.044). There was no significant difference in TTP, BF, SUVmax, or TNR between HCCs in patients with liver cirrhosis and those without liver cirrhosis. There was no significant difference in blood flow estimates or SUVmax in background liver parenchyma between patients with and those without cirrhosis. TTP was shorter in HCCs with MVI than without MVI (P = 0.046). There was no significant difference in BF, HPI, SUVmax, or TNR between HCCs with MVI and without MVI. There was no significant difference in blood flow estimates or SUVmax in background liver parenchyma between patients with and those without MVI.

CONCLUSION

ED combined with WB 18F-FDG PET/CT can significantly increase the detection rate of moderately differentiated and poorly differentiated HCCs. HPI was significantly higher in HCCs in patients with liver cirrhosis than those without liver cirrhosis. TTP was significantly shorter in HCCs with MVI than without MVI.

MRI Features for Predicting Microvascular Invasion and Postoperative Recurrence in Hepatocellular Carcinoma Without Peritumoral Hypointensity

Purpose

To identify MRI features of hepatocellular carcinoma (HCC) that predict microvascular invasion (MVI) and postoperative intrahepatic recurrence in patients without peritumoral hepatobiliary phase (HBP) hypointensity.

Patients and Methods

One hundred and thirty patients with HCC who underwent preoperative gadoxetate-enhanced MRI and curative hepatic resection were retrospectively reviewed. Two radiologists reviewed all preoperative MR images and assessed the radiological features of HCCs. The ability of peritumoral HBP hypointensity to identify MVI and intrahepatic recurrence was analyzed. We then assessed the MRI features of HCC that predicted the MVI and intrahepatic recurrence-free survival (RFS) in the subgroup without peritumoral HBP hypointensity. Finally, a two-step flowchart was constructed to assist in clinical decision-making.

Results

Peritumoral HBP hypointensity (odds ratio, 3.019; 95% confidence interval: 1.071-8.512; P=0.037) was an independent predictor of MVI. The sensitivity, specificity, positive predictive value, negative predictive value, and AUROC of peritumoral HBP hypointensity in predicting MVI were 23.80%, 91.04%, 71.23%, 55.96%, and 0.574, respectively. Intrahepatic RFS was significantly shorter in patients with peritumoral HBP hypointensity (P<0.001). In patients without peritumoral HBP hypointensity, the only significant difference between MVI-positive and MVI-negative HCCs was the presence of a radiological capsule (P=0.038). Satellite nodule was an independent risk factor for intrahepatic RFS (hazard ratio,3.324; 95% CI: 1.733-6.378; P<0.001). The high-risk HCC detection rate was significantly higher when using the two-step flowchart that incorporated peritumoral HBP hypointensity and satellite nodule than when using peritumoral HBP hypointensity alone (P<0.001).

Conclusion

In patients without peritumoral HBP hypointensity, a radiological capsule is useful for identifying MVI and satellite nodule is an independent risk factor for intrahepatic RFS.

Efficacy and safety of anlotinib as an adjuvant therapy in hepatocellular carcinoma patients with a high risk of postoperative recurrence

Objective

Hepatocellular carcinoma (HCC) has a high rate of postoperative recurrence and lacks an effective treatment to prevent recurrence. This study aims to investigate the efficacy and safety of anlotinib in postoperative adjuvant therapy for HCC patients with high-risk recurrence factors.

Methods

For this multicenter, retrospective study, we recruited 63 HCC patients who received either anlotinib (n=27) or transcatheter arterial chemoembolization (TACE) (n=36) from six research centers in China between March 2019 and October 2020. The primary endpoint was disease-free survival (DFS) and the secondary endpoints were overall survival (OS) and safety.

Results

In this study, the median follow-up time was 25.9 and 26.8 months in the anlotinib and TACE groups, respectively. There was no significant difference in the median DFS between the anlotinib [26.8 months, 95% confidence interval (95% CI): 6.8-NE] and TACE groups (20.6 months, 95% CI: 8.4-NE). The 12-month OS rates in the anlotinib and TACE groups were 96.3% and 97.2%, respectively. In the anlotinib group, 19 of 27 patients (70.4%) experienced treatment-emergent adverse events, with the most common events (≥10%) being hypertension (22.2%) and decreased platelet count (22.2%).

Conclusions

The results indicate that anlotinib, as a new, orally administered tyrosine kinase inhibitor, has the same efficacy as TACE, and side effects can be well controlled.

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.

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.

A novel predictive model of microvascular invasion in hepatocellular carcinoma based on differential protein expression

BACKGROUND

This study aims to construct and verify a nomogram model for microvascular invasion (MVI) based on hepatocellular carcinoma (HCC) tumor characteristics and differential protein expressions, and explore the clinical application value of the prediction model.

METHODS

The clinicopathological data of 200 HCC patients were collected and randomly divided into training set and validation set according to the ratio of 7:3. The correlation between MVI occurrence and primary disease, age, gender, tumor size, tumor stage, and immunohistochemical characteristics of 13 proteins, including GPC3, CK19 and vimentin, were statistically analyzed. Univariate and multivariate analyzes identified risk factors and independent risk factors, respectively. A nomogram model that can be used to predict the presence of MVI was subsequently constructed. Then, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were conducted to assess the performance of the model.

RESULTS

Multivariate logistic regression analysis indicated that tumor size, GPC3, P53, RRM1, BRCA1, and ARG were independent risk factors for MVI. A nomogram was constructed based on the above six predictors. ROC curve, calibration, and DCA analysis demonstrated the good performance and the clinical application potential of the nomogram model.

CONCLUSIONS

The predictive model constructed based on the clinical characteristics of HCC tumors and differential protein expression patterns could be helpful to improve the accuracy of MVI diagnosis in HCC patients.

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.

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.

The Value of CT Perfusion Parameters and Apparent Diffusion Coefficient Value of Magnetic Resonance Diffusion Weighted Imaging in Diagnosis of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma is one of the malignant tumors with the highest incidence in the world. According to the latest statistics of the National Cancer Center, the incidence of liver cancer ranks fifth in malignant tumors and its mortality rate ranks second in China, which seriously threatens people' s life and health.

Aim

To investigate the value of CT perfusion parameters and apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) in the diagnosis of hepatocellular carcinoma.

Methods

43 patients with hepatocellular carcinoma and 40 patients with hepatic hemangioma treated in our hospital from August 2018 to August 2021 were selected for CT perfusion imaging and MRI examination.

Results

The liver blood flow (BF), liver blood volume (BV), and hepatic artery perfusion (HAP) in the hepatocellular carcinoma group were (267.38 ± 35.59) ml/(min·100 g), (30.20 ± 8.82) ml/100 g, and (0.64 ± 0.10) ml/(min·ml), respectively, which were significantly higher than those in the hepatic hemangioma group (p < 0.05). The ADC value of hepatocellular carcinoma DWI sequence was (1.20 ± 0.17) ×10⁻³ mm², which was significantly lower than that of hepatic hemangioma (p < 0.05). The area under ROC curve of BF, BV, HAP, and ADC values for hepatocellular carcinoma was 0.860, 0.754, 0.804, and 0.890, respectively. The area under ROC curve of the four groups was compared (p > 0.05).

Conclusion

CT perfusion parameters BF, BV, HAP, and DWI sequence ADC values have certain application value in the diagnosis of hepatocellular carcinoma, and there is no significant difference between the diagnostic value of each parameter.

Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI

Objectives

We aimed to investigate the value of performing gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) radiomics for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on multiple sequences.

Methods

We randomly allocated 165 patients with HCC who underwent partial hepatectomy to training and validation sets. Stepwise regression and the least absolute shrinkage and selection operator algorithm were used to select significant variables. A clinicoradiological model, radiomics model, and combined model were constructed using multivariate logistic regression. The performance of the models was evaluated, and a nomogram risk-prediction model was built based on the combined model. A concordance index and calibration curve were used to evaluate the discrimination and calibration of the nomogram model.

Results

The tumour margin, peritumoural hypointensity, and seven radiomics features were selected to build the combined model. The combined model outperformed the radiomics model and the clinicoradiological model and had the highest sensitivity (90.89%) in the validation set. The areas under the receiver operating characteristic curve were 0.826, 0.755, and 0.708 for the combined, radiomics, and clinicoradiological models, respectively. The nomogram model based on the combined model exhibited good discrimination (concordance index = 0.79) and calibration.

Conclusions

The combined model based on radiomics features of Gd-EOB-DTPA enhanced MRI, tumour margin, and peritumoural hypointensity was valuable for predicting HCC microvascular invasion. The nomogram based on the combined model can intuitively show the probabilities of MVI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00855-w.

Contrast-enhanced magnetic resonance imaging perfusion can predict microvascular invasion in patients with hepatocellular carcinoma (between 1 and 5 cm)

PURPOSE

To evaluate the role of perfusion parameters with MR imaging of the liver in diagnosing MVI in hepatocellular carcinoma (HCC) (between 1 and 5 cm).

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board. In 80 patients with 43 MVI( +) and 42 MVI( -) HCC, whole-liver perfusion MR imaging with Cartesian k-space undersampling and compressed sensing reconstruction was performed after injection of 0.1 mmol/kg gadopentetate dimeglumine. Parameters derived from a dual-input single-compartment model of arterial flow (Fa), portal venous flow (Fp), total blood flow (Ft = Fa + Fp), arterial fraction (ART), distribution volume (DV), and mean transit time (MTT) were measured. The significant parameters between the two groups were included to correlate with the presence of MVI at simple and multiple regression analysis.

RESULTS

In MVI-positive HCC, Fp was significantly higher than in MVI-negative HCC, whereas the reverse was seen for ART (p < 0.001). Tumor size (β = 1.2, p = 0.004; odds ratio, 3.20; 95% CI 1.45, 7.06), Fp (β = 1.1, p = 0.004; odds ratio, 3.09; 95% CI 1.42, 6.72), and ART (β =  - 3.1, p = 0.001; odds ratio, 12.13; 95% CI 2.85, 51.49) were independent risk factors for MVI. The AUC value of the combination of all three metrics was 0.931 (95% CI 0.855, 0.975), with sensitivity of 97.6% and specificity of 76.2%.

CONCLUSION

The combination of Fp, ART, and tumor size demonstrated a higher diagnostic accuracy compared with each parameter used individually when evaluating MVI in HCC (between 1 and 5 cm).

Radiomics and nomogram of magnetic resonance imaging for preoperative prediction of microvascular invasion in small hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) of small hepatocellular carcinoma (sHCC) (≤ 3.0 cm) is an independent prognostic factor for poor progression-free and overall survival. Radiomics can help extract imaging information associated with tumor pathophysiology.

AIM

To develop and validate radiomics scores and a nomogram of gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in sHCC.

METHODS

In total, 415 patients were diagnosed with sHCC by postoperative pathology. A total of 221 patients were retrospectively included from our hospital. In addition, we recruited 94 and 100 participants as independent external validation sets from two other hospitals. Radiomics models of Gd-EOB-DTPA-enhanced MRI and diffusion-weighted imaging (DWI) were constructed and validated using machine learning. As presented in the radiomics nomogram, a prediction model was developed using multivariable logistic regression analysis, which included radiomics scores, radiologic features, and clinical features, such as the alpha-fetoprotein (AFP) level. The calibration, decision-making curve, and clinical usefulness of the radiomics nomogram were analyzed. The radiomic nomogram was validated using independent external cohort data. The areas under the receiver operating curve (AUC) were used to assess the predictive capability.

RESULTS

Pathological examination confirmed MVI in 64 (28.9%), 22 (23.4%), and 16 (16.0%) of the 221, 94, and 100 patients, respectively. AFP, tumor size, non-smooth tumor margin, incomplete capsule, and peritumoral hypointensity in hepatobiliary phase (HBP) images had poor diagnostic value for MVI of sHCC. Quantitative radiomic features (1409) of MRI scans) were extracted. The classifier of logistic regression (LR) was the best machine learning method, and the radiomics scores of HBP and DWI had great diagnostic efficiency for the prediction of MVI in both the testing set (hospital A) and validation set (hospital B, C). The AUC of HBP was 0.979, 0.970, and 0.803, respectively, and the AUC of DWI was 0.971, 0.816, and 0.801 (P < 0.05), respectively. Good calibration and discrimination of the radiomics and clinical combined nomogram model were exhibited in the testing and two external validation cohorts (C-index of HBP and DWI were 0.971, 0.912, 0.808, and 0.970, 0.843, 0.869, respectively). The clinical usefulness of the nomogram was further confirmed using decision curve analysis.

CONCLUSION

AFP and conventional Gd-EOB-DTPA-enhanced MRI features have poor diagnostic accuracies for MVI in patients with sHCC. Machine learning with an LR classifier yielded the best radiomics score for HBP and DWI. The radiomics nomogram developed as a noninvasive preoperative prediction method showed favorable predictive accuracy for evaluating MVI in sHCC.

Current Imaging Diagnosis of Hepatocellular Carcinoma

Simple Summary

The role of imaging in the management of hepatocellular carcinoma (HCC) has significantly evolved and expanded beyond the plain radiological confirmation of the tumor based on the typical appearance in a multiphase contrast-enhanced CT or MRI examination. The introduction of hepatobiliary contrast agents has enabled the diagnosis of hepatocarcinogenesis at earlier stages, while the application of ultrasound contrast agents has drastically upgraded the role of ultrasound in the diagnostic algorithms. Newer quantitative techniques assessing blood perfusion on CT and MRI not only allow earlier diagnosis and confident differentiation from other lesions, but they also provide biomarkers for the evaluation of treatment response. As distinct HCC subtypes are identified, their correlation with specific imaging features holds great promise for estimating tumor aggressiveness and prognosis. This review presents the current role of imaging and underlines its critical role in the successful management of patients with HCC.

Abstract

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer related death worldwide. Radiology has traditionally played a central role in HCC management, ranging from screening of high-risk patients to non-invasive diagnosis, as well as the evaluation of treatment response and post-treatment follow-up. From liver ultrasonography with or without contrast to dynamic multiple phased CT and dynamic MRI with diffusion protocols, great progress has been achieved in the last decade. Throughout the last few years, pathological, biological, genetic, and immune-chemical analyses have revealed several tumoral subtypes with diverse biological behavior, highlighting the need for the re-evaluation of established radiological methods. Considering these changes, novel methods that provide functional and quantitative parameters in addition to morphological information are increasingly incorporated into modern diagnostic protocols for HCC. In this way, differential diagnosis became even more challenging throughout the last few years. Use of liver specific contrast agents, as well as CT/MRI perfusion techniques, seem to not only allow earlier detection and more accurate characterization of HCC lesions, but also make it possible to predict response to treatment and survival. Nevertheless, several limitations and technical considerations still exist. This review will describe and discuss all these imaging modalities and their advances in the imaging of HCC lesions in cirrhotic and non-cirrhotic livers. Sensitivity and specificity rates, method limitations, and technical considerations will be discussed.

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma

BACKGROUND

The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning.

AIM

To develop a radiomic analysis model based on pre-operative magnetic resonance imaging (MRI) data to predict MVI in HCC.

METHODS

A total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation, among whom 73 were found to have MVI and 40 were not. All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy. We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the adjacent two images on MRI, namely, the regions of interest. Quantitative analyses included most discriminant factors (MDFs) developed using linear discriminant analysis algorithm and histogram analysis with MaZda software. Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis. Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic (ROC) curve analysis. Five-fold cross-validation was also applied via R software.

RESULTS

The area under the ROC curve (AUC) of the MDF (0.77-0.85) outperformed that of histogram parameters (0.51-0.74). After multivariate analysis, MDF values of the arterial and portal venous phase, and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI (P < 0.05). The AUC value of the model was 0.939 [95% confidence interval (CI): 0.893-0.984, standard error: 0.023]. The result of internal five-fold cross-validation (AUC: 0.912, 95%CI: 0.841-0.959, standard error: 0.0298) also showed favorable predictive efficacy.

CONCLUSION

Noninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC.

Peritumoral Imaging Manifestations on Gd-EOB-DTPA-Enhanced MRI for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Purpose

The aim was to investigate the association between microvascular invasion (MVI) and the peritumoral imaging features of gadolinium ethoxybenzyl DTPA-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) in hepatocellular carcinoma (HCC).

Methods

Up until Feb 24, 2022, the PubMed, Embase, and Cochrane Library databases were carefully searched for relevant material. The software packages utilized for this meta-analysis were Review Manager 5.4.1, Meta-DiSc 1.4, and Stata16.0. Summary results are presented as sensitivity (SEN), specificity (SPE), diagnostic odds ratios (DORs), area under the receiver operating characteristic curve (AUC), and 95% confidence interval (CI). The sources of heterogeneity were investigated using subgroup analysis.

Results

An aggregate of nineteen articles were remembered for this meta-analysis: peritumoral enhancement on the arterial phase (AP) was described in 13 of these studies and peritumoral hypointensity on the hepatobiliary phase (HBP) in all 19 studies. The SEN, SPE, DOR, and AUC of the 13 investigations on peritumoral enhancement on AP were 0.59 (95% CI, 0.41−0.58), 0.80 (95% CI, 0.75−0.85), 4 (95% CI, 3−6), and 0.73 (95% CI, 0.69−0.77), respectively. The SEN, SPE, DOR, and AUC of 19 studies on peritumoral hypointensity on HBP were 0.55 (95% CI, 0.45−0.64), 0.87 (95% CI, 0.81−0.91), 8 (95% CI, 5−12), and 0.80 (95% CI, 0.76−0.83), respectively. The subgroup analysis of two imaging features identified ten and seven potential factors for heterogeneity, respectively.

Conclusion

The results of peritumoral enhancement on the AP and peritumoral hypointensity on HBP showed high SPE but low SEN. This indicates that the peritumoral imaging features on Gd-EOB-DTPA-enhanced MRI can be used as a noninvasive, excluded diagnosis for predicting hepatic MVI in HCC preoperatively. Moreover, the results of this analysis should be updated when additional data become available. Additionally, in the future, how to improve its SEN will be a new research direction.

Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma

Background

Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC.

Methods

We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated, and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression.

Results

Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and a-fetoprotein (AFP) were independently associated with MVI: DL-MVI (odds ratio [OR] = 35.738; 95% confidence interval [CI] 14.027–91.056; p < 0.001), AFP (OR = 4.634, 95% CI 2.576–8.336; p < 0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824.

Conclusions

Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02645-8.

Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

PURPOSE

To assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

METHODS

Publications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models.

RESULTS

Twenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82-0.89), 0.87 (95% CI 0.83-0.92), and 0.74 (95% CI 0.67-0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region.

CONCLUSION

Radiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.

Preoperative Prediction of Microvascular Invasion Risk Grades in Hepatocellular Carcinoma Based on Tumor and Peritumor Dual-Region Radiomics Signatures

Objective

To predict preoperative microvascular invasion (MVI) risk grade by analyzing the radiomics signatures of tumors and peritumors on enhanced magnetic resonance imaging (MRI) images of hepatocellular carcinoma (HCC).

Methods

A total of 501 HCC patients (training cohort n = 402, testing cohort n = 99) who underwent preoperative Gd-EOB-DTPA-enhanced MRI and curative liver resection within a month were studied retrospectively. Radiomics signatures were selected using the least absolute shrinkage and selection operator (Lasso) algorithm. Unimodal radiomics models based on tumors and peritumors (10mm or 20mm) were established using the Logistic algorithm, using plain T1WI, arterial phase (AP), portal venous phase (PVP), and hepatobiliary phase (HBP) images. Multimodal radiomics models based on different regions of interest (ROIs) were established using a combinatorial modeling approach. Moreover, we merged radiomics signatures and clinico-radiological features to build unimodal and multimodal clinical radiomics models.

Results

In the testing cohort, the AUC of the dual-region (tumor & peritumor 20 mm)radiomics model and single-region (tumor) radiomics model were 0.741 vs 0.694, 0.733 vs 0.725, 0.667 vs 0.710, and 0.559 vs 0.677, respectively, according to AP, PVP, T1WI, and HBP images. The AUC of the final clinical radiomics model based on tumor and peritumoral 20mm incorporating radiomics features in AP&PVP&T1WI images for predicting MVI classification in the training and testing cohorts were 0.962 and 0.852, respectively.

Conclusion

The radiomics signatures of the dual regions for tumor and peritumor on AP and PVP images are of significance to predict MVI.

Dynamic Contrast-Enhanced Ultrasonography with Sonazoid for Diagnosis of Microvascular Invasion in Hepatocellular Carcinoma

The aim of the present study was to investigate the imaging features observed in pre-operative Sonazoid contrast-enhanced ultrasound (SZ-CEUS) and the correlations with the presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. In this single-center retrospective study, 31 patients with surgically and histopathologically confirmed HCC lesions were included. Patients were classified according to the presence of MVI into the MVI-positive group (n = 15) and MVI-negative group (n = 16). The CEUS examinations were performed within 2 or 3 d before surgery. Features, including tumor necrosis and ultrasound contrast agent (UCA) distribution characteristics in the arterial phase (AP), tumor types (single nodular [SN] or non-single nodular [non-SN]) in the post-vascular phase (PVP), wash-in time, wash-in slope, time to peak (TTP) and peak intensity (PI), were assessed. Univariate analysis revealed statistically significant differences between the two groups with respect to tumor necrosis (p = 0.002), inhomogeneous distribution of contrast agent in the AP (p = 0.001) and non-SN type in the PVP (p < 0.001). There was no significant difference in the quantitative parameters. Multivariate analysis revealed that non-SN type in the PVP was a significant independent risk factor for MVI of HCC (odds ratio = 30.51, 95% confidence interval [CI]: 2.335-398.731, p = 0.009). The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.873, 93.3%, 81.3%, 82.4% and 92.9%, respectively. Thus, SZ-CEUS can provide useful information for the diagnosis of MVI in HCC.

Radiomic Features of Multi-ROI and Multi-Phase MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma

Objectives

To develop and validate an MR radiomics-based nomogram to predict the presence of MVI in patients with solitary HCC and further evaluate the performance of predictors for MVI in subgroups (HCC ≤ 3 cm and > 3 cm).

Materials and Methods

Between May 2015 and October 2020, 201 patients with solitary HCC were analysed. Radiomic features were extracted from precontrast T₁WI, arterial phase, portal venous phase, delayed phase and hepatobiliary phase images in regions of the intratumoral, peritumoral and their combining areas. The mRMR and LASSO algorithms were used to select radiomic features related to MVI. Clinicoradiological factors were selected by using backward stepwise regression with AIC. A nomogram was developed by incorporating the clinicoradiological factors and radiomics signature. In addition, the radiomic features and clinicoradiological factors related to MVI were separately evaluated in the subgroups (HCC ≤ 3 cm and > 3 cm).

Results

Histopathological examinations confirmed MVI in 111 of the 201 patients (55.22%). The radiomics signature showed a favourable discriminatory ability for MVI in the training set (AUC, 0.896) and validation set (AUC, 0.788). The nomogram incorporating peritumoral enhancement, tumour growth type and radiomics signature showed good discrimination in the training (AUC, 0.932) and validation sets (AUC, 0.917) and achieved well-fitted calibration curves. Subgroup analysis showed that tumour growth type was a predictor for MVI in the HCC ≤ 3 cm cohort and peritumoral enhancement in the HCC > 3 cm cohort; radiomic features related to MVI varied between the HCC ≤ 3 cm and HCC > 3 cm cohort. The performance of the radiomics signature improved noticeably in both the HCC ≤ 3 cm (AUC, 0.953) and HCC > 3 cm cohorts (AUC, 0.993) compared to the original training set.

Conclusions

The preoperative nomogram integrating clinicoradiological risk factors and the MR radiomics signature showed favourable predictive efficiency for predicting MVI in patients with solitary HCC. The clinicoradiological factors and radiomic features related to MVI varied between subgroups (HCC ≤ 3 cm and > 3 cm). The performance of radiomics signature for MVI prediction was improved in both the subgroups.

Progress of MRI Radiomics in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively.

Objective

This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC.

Methods

A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis.

Results

Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients.

Conclusion

Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.

Pharmacokinetic analysis of different contrast agents on multiphase enhanced MRI for microvascular invasion: preoperative prediction in hepatocellular carcinoma

BACKGROUND

The preoperative diagnosis of microvascular invasion (MVI) for the solitary small hepatocellular carcinoma (sHCC) is crucial for the decision of surgical strategies.

PURPOSE

To compare the kinetic parameters and diagnostic effects of two contrast agents for preoperatively predicting MVI of sHCC on multiphase enhanced magnetic resonance imaging (MRI).

MATERIAL AND METHODS

Two groups of patients with known solitary sHCC underwent an enhanced MRI examination before hepatic resection: Data A (n = 61) patients underwent Gd-EOB-DTPA-enhanced MRI, and Data B (n = 41) patients had a normal contrast agent. The two sets of data were processed in the same way. Arterial peritumoral enhancement measured from multiphase enhanced MRI was analyzed using quantitative kinetic parameters, including initial signal enhancement (SE₁), peak signal enhancement (SE_peak), and calculation of the signal enhancement ratio (SE_R).

RESULTS

The statistical analysis showed that the average SE₁ and SE_R (Data A) for the MVI-positive group were significantly higher (P < 0.05) than those in the MVI-negative group. The SE_R (Data B) and SE_peak showed no significant difference for either group. In Data A, the receiver operating characteristic analysis between the two groups had an area under the curve of 0.74 and 0.71 for SE₁ and SE_R, respectively, which was higher than that of Data B. The different contrast agents had the same enhancement curve trend.

CONCLUSION

Gd-EOB-DTPA-enhanced MRI had a better quantitative kinetic parameter analysis effect for arterial peritumoral enhancement on predicting MVI of sHCC in clinical practice.

Imaging Features at the Periphery: Hemodynamics, Pathophysiology, and Effect on LI-RADS Categorization

Liver lesions have different enhancement patterns at dynamic contrast-enhanced imaging. The Liver Imaging Reporting and Data System (LI-RADS) applies the enhancement kinetic of liver observations in its algorithms for imaging-based diagnosis of hepatocellular carcinoma (HCC) in at-risk populations. Therefore, careful analysis of the spatial and temporal features of these enhancement patterns is necessary to increase the accuracy of liver mass characterization. The authors focus on enhancement patterns that are found at or around the margins of liver observations-many of which are recognized and defined by LI-RADS, such as targetoid appearance, rim arterial phase hyperenhancement, peripheral washout, peripheral discontinuous nodular enhancement, enhancing capsule appearance, nonenhancing capsule appearance, corona enhancement, and periobservational arterioportal shunts-as well as peripheral and periobservational enhancement in the setting of posttreatment changes. Many of these are considered major or ancillary features of HCC, ancillary features of malignancy in general, features of non-HCC malignancy, features associated with benign entities, or features related to treatment response. Distinction between these different patterns of enhancement can help with achieving a more specific diagnosis of HCC and better assessment of response to local-regional therapy. ^©RSNA, 2021.

MR Imaging Contrast Agents: Role in Imaging of Chronic Liver Diseases

Contrast-enhanced MR imaging plays an important role in the evaluation of patients with chronic liver disease, particularly for detection and characterization of liver lesions. The two most commonly used contrast agents for liver MR imaging are extracellular agents (ECAs) and hepatobiliary agents (HBAs). In patients with liver disease, the main advantage of ECA-enhanced MR imaging is its high specificity for the diagnosis of progressed HCCs. Conversely, HBAs have an additional contrast mechanism, which results in high liver-to-lesion contrast and highest sensitivity for lesion detection in the hepatobiliary phase. Emerging data suggest that features depicted on contrast-enhanced MR imaging scans are related to tumor biology and are predictive of patients' prognosis, likely to further expand the role of contrast-enhanced MR imaging in the clinical care of patients with chronic liver disease.

HCC: role of pre- and post-treatment tumor biology in driving adverse outcomes and rare responses to therapy

Liver cancer is the fastest-growing cause of cancer deaths in the United States and is a complex disease. The response of hepatocellular carcinoma (HCC) to treatment can be variable. Predicting response to determine the most effective therapy is an active area of research. Our understanding of underlying factors which drive response to therapy is continually increasing. As more therapies for the treatment of this disease evolve, it is crucial to identify and match the ideal therapy for a particular tumor and patient. The potential predicative imaging features of tumor behavior, while of research interest, have not been validated for clinical use and do not currently inform treatment planning. If further validated though, prognostic features may be used in the future to personalize treatment plans according to individual patients and tumors. Unexpected post-treatment responses such as potential tumor biology changes and abscopal effect which are important to be aware of. This review is intended for radiologists who routinely interpret post treatment HCC imaging and is designed to increase their cognizance about how HCC tumor biology drives response to therapy and explore rare responses to therapy.

Preoperative Prediction of Cytokeratin 19 Expression for Hepatocellular Carcinoma with Deep Learning Radiomics Based on Gadoxetic Acid-Enhanced Magnetic Resonance Imaging

Purpose

Cytokeratin 19 (CK19) expression is a proven independent prognostic predictor of hepatocellular carcinoma (HCC). This study aimed to develop and validate the performance of a deep learning radiomics (DLR) model for CK19 identification in HCC based on preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI).

Patients and Methods

A total of 141 surgically confirmed HCCs with preoperative gadoxetic acid-enhanced MRI from two institutions were included. Prediction models were established based on hepatobiliary phase (HBP) images using a training set (n=102) and validated using time-independent (n=19) and external (n=20) test sets. A receiver operating characteristic curve was used to evaluate the performance for CK19 prediction. Recurrence-free survival (RFS) was also analyzed by incorporating the CK19 expression and other factors.

Results

For predicting CK19 expression, the area under the curve (AUC) of the DLR model was 0.820 (95% confidence interval [CI]: 0.732–0.907, P<0.001) with sensitivity, specificity, accuracy of 0.800, 0.766, and 0.775, respectively, and reached 0.781 in the external test set. Combined with alpha fetoprotein, the AUC increased to 0.833 (95% CI: 0.753–0.912, P<0.001) and the sensitivity was 0.960. Intratumoral hemorrhage and peritumoral hypointensity on HBP were independent risk factors for HCC recurrence by multivariate analysis. Based on predicted CK19 expression and the independent risk factors, a nomogram was developed to predict RFS and achieved C-index of 0.707.

Conclusion

This study successfully established and verified an optimal DLR model for preoperative prediction of CK19-positive HCCs based on gadoxetic acid-enhanced MRI. The prediction of CK19 expression in HCC using a non-invasive method can help inform preoperative planning.

Study on correlation between two-dimensional ultrasound, contrast-enhanced ultrasound and microvascular invasion in hepatocellular carcinoma

OBJECTIVE

To explore the correlation between two-dimensional ultrasound (2D-US), contrast-enhanced ultrasound (CEUS) and microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

METHODS

In this retrospective study, 56 patients with surgically pathologically confirmed HCC lesions were included. Patients were classified according to the presence of MVI: MVI positive group (n = 17) and MVI negative group (n = 39). 2D-US and CEUS examinations were performed within two weeks before surgery. The 2D-US and CEUS features were analyzed for correlation with MVI. Statistically significant parameters of ultrasound characteristic were scored, and the results of the scores were analyzed by ROC curve.

RESULTS

There were statistically significant differences in tumor shape, boundary, capsule, CEUS portal phase and delayed phase enhancement pattern, time to wash out, and tumor margin after enhancement (P <  0.05), while there were no statistically significant differences in tumor location and size, CEUS arterial phase enhancement pattern, initial time, time to peak, and peritumor enhancement (P >  0.05). When diagnosing the presence of MVI in HCC patients with cut-off value of the score combined 2D-US and CEUS features≥3, the maximum Jorden index was 0.58, and its diagnostic sensitivity and specificity were 94.10%and 64.1%, respectively, meaning that the total score≥3 was highly suspicious of the presence of MVI.

CONCLUSIONS

2D-US and CEUS are feasible methods for preoperative prediction of MVI in HCC, and can provide some theoretical basis for individualized clinical treatment.

Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging

Background

Microvascular invasion (MVI) has a significant effect on the prognosis of hepatocellular carcinoma (HCC), but its preoperative identification is challenging. Radiomics features extracted from medical images, such as magnetic resonance (MR) images, can be used to predict MVI. In this study, we explored the effects of different imaging sequences, feature extraction and selection methods, and classifiers on the performance of HCC MVI predictive models.

Methods

After screening against the inclusion criteria, 69 patients with HCC and preoperative gadoxetic acid-enhanced MR images were enrolled. In total, 167 features were extracted from the MR images of each sequence for each patient. Experiments were designed to investigate the effects of imaging sequence, number of gray levels (Ng), quantization algorithm, feature selection method, and classifiers on the performance of radiomics biomarkers in the prediction of HCC MVI. We trained and tested these models using leave-one-out cross-validation (LOOCV).

Results

The radiomics model based on the images of the hepatobiliary phase (HBP) had better predictive performance than those based on the arterial phase (AP), portal venous phase (PVP), and pre-enhanced T1-weighted images [area under the receiver operating characteristic (ROC) curve (AUC) =0.792 vs. 0.641/0.634/0.620, P=0.041/0.021/0.010, respectively]. Compared with the equal-probability and Lloyd-Max algorithms, the radiomics features obtained using the Uniform quantization algorithm had a better performance (AUC =0.643/0.666 vs. 0.792, P=0.002/0.003, respectively). Among the values of 8, 16, 32, 64, and 128, the best predictive performance was achieved when the Ng was 64 (AUC =0.792 vs. 0.584/0.697/0.677/0.734, P<0.001/P=0.039/0.001/0.137, respectively). We used a two-stage feature selection method which combined the least absolute shrinkage and selection operator (LASSO) and recursive feature elimination (RFE) gradient boosting decision tree (GBDT), which achieved better stability than and outperformed LASSO, minimum redundancy maximum relevance (mRMR), and support vector machine (SVM)-RFE (stability =0.967 vs. 0.837/0.623/0.390, respectively; AUC =0.850 vs. 0.792/0.713/0.699, P=0.142/0.007/0.003, respectively). The model based on the radiomics features of HBP images using the GBDT classifier showed a better performance for the preoperative prediction of MVI compared with logistic regression (LR), SVM, and random forest (RF) classifiers (AUC =0.895 vs. 0.850/0.834/0.884, P=0.558/0.229/0.058, respectively). With the optimal combination of these factors, we established the best model, which had an AUC of 0.895, accuracy of 87.0%, specificity of 82.5%, and sensitivity of 93.1%.

Conclusions

Imaging sequences, feature extraction and selection methods, and classifiers can have a considerable effect on the predictive performance of radiomics models for HCC MVI.

MRI Features for Predicting Microvascular Invasion of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

PURPOSE

Microvascular invasion (MVI) is an important prognostic factor in patients with hepatocellular carcinoma (HCC). However, the reported results of magnetic resonance imaging (MRI) features for predicting MVI of HCC are variable and conflicting. Therefore, this meta-analysis aimed to identify the significant MRI features for MVI of HCC and to determine their diagnostic value.

METHODS

Original studies reporting the diagnostic performance of MRI for predicting MVI of HCC were identified in MEDLINE and EMBASE up until January 15, 2020. Study quality was assessed using QUADAS-2. A bivariate random-effects model was used to calculate the meta-analytic pooled diagnostic odds ratio (DOR) and 95% confidence interval (CI) for each MRI feature for diagnosing MVI in HCC. The meta-analytic pooled sensitivity and specificity were calculated for the significant MRI features.

RESULTS

Among 235 screened articles, we found 36 studies including 4,274 HCCs. Of the 15 available MRI features, 7 were significantly associated with MVI: larger tumor size (>5 cm) (DOR = 5.2, 95% CI [3.0-9.0]), rim arterial enhancement (4.2, 95% CI [1.7-10.6]), arterial peritumoral enhancement (4.4, 95% CI [2.8-6.9]), peritumoral hypointensity on hepatobiliary phase imaging (HBP) (8.2, 95% CI [4.4-15.2]), nonsmooth tumor margin (3.2, 95% CI [2.2-4.4]), multifocality (7.1, 95% CI [2.6-19.5]), and hypointensity on T1-weighted imaging (T1WI) (4.9, 95% CI [2.5-9.6]). Both peritumoral hypointensity on HBP and multifocality showed very high meta-analytic pooled specificities for diagnosing MVI (91.1% [85.4-94.8%] and 93.3% [74.5-98.5%], respectively).

CONCLUSIONS

Seven MRI features including larger tumor size, rim arterial enhancement, arterial peritumoral enhancement, peritumoral hypointensity on HBP, nonsmooth margin, multifocality, and hypointensity on T1WI were significant predictors for MVI of HCC. These MRI features predictive of MVI can be useful in the management of HCC.

Value of gadoxetic acid-enhanced MRI for microvascular invasion of small hepatocellular carcinoma: a retrospective study

Background

The objective of this study was to analyze the accuracy of gadolinium–ethoxybenzyl–diethylenetriamine penta–acetic acid enhanced magnetic resonance imaging (Gd–EOB–DTPA–MRI) for predicting microvascular invasion (MVI) in patients with small hepatocellular carcinoma (sHCC) preoperatively.

Methods

A total of 60 sHCC patients performed with preoperative Gd–EOB–DTPA–MRI in the Harbin Medical University Cancer Hospital from October 2018 to October 2019 were involved in the study. Univariate and multivariate analyses were performed by chi–square test and logistic regression analysis. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of Gd–EOB–DTPA–MRI were performed by receiver operating characteristic (ROC) curves.

Results

Univariate analysis indicated that alanine aminotransferase (≥ 39.00U/L), poorly differentiated pathology, and imaging features including grim enhancement, capsule enhancement, arterial halo sign and hepatobiliary features (tumor highly uptake, halo sign, spicule sign and brush sign) were associated with the occurrence of MVI (p < 0.05). Multivariate analysis revealed that rim enhancement and hepatobiliary spicule sign were independent predictors of MVI (p < 0.05). The area under the ROC curve was 0.917 (95% confidence interval 0.838–0.996), and the sensitivity was 94.74%.

Conclusions

The morphologies of hepatobiliary phase imaging, especially the spicule sign, showed high accuracy in diagnosing MVI of sHCC. Rim enhancement played a significant role in diagnosing MVI of sHCC.

Imaging features based on Gd-EOB-DTPA-enhanced MRI for predicting vessels encapsulating tumor clusters (VETC) in patients with hepatocellular carcinoma

OBJECTIVE

To investigate the non-invasive prediction of hepatocellular carcinoma (HCC) with vessels encapsulating tumor clusters (VETC) based on qualitative and quantitative imaging features of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI.

METHODS

109 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA enhanced MRI and immunochemical staining for CD34 were retrospectively evaluated in our institution (the first affiliated hospital of Soochow university). Pre-operative imaging features of Gd-EOB-DTPA-enhanced MRI were qualitatively and quantitatively reviewed by radiologists. Significant variables for differentiating the VETC-positive and VETC-negative HCCs were identified in univariate and multivariate analyses. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cut-off values for quantitative variables. The nomogram based on the coefficient of multivariate analysis was constructed to evaluate the probability of VETC-positive HCCs.

RESULTS

The multivariate analysis showed that the serum AST level >40 U  l^-1 (p = 0.007), non-rim diffuse and heterogeneous arterial phase hyperenhancement (p = 0.035), tumor-to-liver SI ratio of 1.135 or more on AP images (p = 0.001), and tumor-to-liver SI ratio of 0.585 or less on HBP images (p = 0.002) were significant predictors for predicting VETC-positive HCCs. Combing all four significant variables provided a diagnostic accuracy of 82.6%, sensitivity of 83.9%, specificity of 80.9% for identifying VETC status. The area under the receiver operating characteristics curve value of the logistical regression coefficient-based nomogram was 0.885 (95% confidence intervals, 0.824-0.946).

CONCLUSION

Qualitative and quantitative imaging features of Gd-EOB-DTPA-enhanced MRI integrating laboratory examination can provide good diagnostic performance.

ADVANCES IN KNOWLEDGE

VETC is a novel identified microvascular pattern; associations between imaging features and VETC status have not been investigated. Pre-operative diagnosis of VETC status in HCC is essential to help predict the outcome of patients and make a decision for the therapeutic schedule.

Identification of Glisson's Capsule Invasion During Hepatectomy for Colorectal Liver Metastasis by Contrast-Enhanced Ultrasonography Using Perflubutane

BACKGROUND

Glisson invasion by CLM is associated with a risk of margin-positive resection, leading to poor long-term outcomes after hepatectomy. This study was performed to evaluate the efficacy of intraoperative ultrasonography (IOUS) for the diagnosis of Glisson's capsule invasion by colorectal liver metastasis (CLM).

METHODS

This prospective study involved 50 consecutive patients undergoing hepatectomy for CLM. Preoperatively, all patients had undergone gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (EOB-MRI). During hepatectomy, a contrast agent (perflubutane) was intravenously injected and Glisson invasion was estimated based on three characteristic findings: a tumor thrombus, peripheral dilatation, and border irregularity/caliber change. The diagnostic abilities of the preoperative and intraoperative imaging studies were evaluated based on pathological examinations of resected specimens.

RESULTS

Among 187 CLMs resected, pathological examinations proved Glisson invasion in 24 tumors (13%). IOUS revealed a tumor thrombus in 3 tumors (1.6%), peripheral dilatation in 4 (2.1%), and border irregularity and/or caliber change in 24 (12.8%). The sensitivity and specificity of IOUS with any of the above three findings for diagnosis of Glisson invasion was 79% and 96%, respectively, while preoperative EOB-MRI detected Glisson invasion in only four tumors (sensitivity/specificity, 17%/100%). The cutoff value of caliber change for diagnosis of Glisson invasion was set at 140% by receiver operating characteristic analysis. The R0 resection rates were not significantly different between patients with (82%) and without (85%) Glisson invasion.

CONCLUSIONS

Identification of characteristic findings (tumor thrombus, peripheral dilatation, and border irregularity/caliber change) by contrast-enhanced IOUS is useful for the prediction of Glisson invasion by CLM.

The importance of a nonsmooth tumor margin and incomplete tumor capsule in predicting HCC microvascular invasion on preoperative imaging examination: a systematic review and meta-analysis

OBJECTIVES

Microvascular invasion (MVI) is a key factor affecting the prognosis of hepatocellular carcinoma (HCC). Preoperative imaging plays an important role in the diagnosis of HCC, treatment planning and treatment evaluation, but it is still difficult to detect MVI directly. Whether the appearance of the tumor margin and the capsule on radiological images can predict MVI is still controversial. The aim of this study is to explore the correlation of the presence of MVI with the smoothness of the tumor margin and the integrity of the capsule in HCC.

MATERIALS AND METHODS

The PubMed, Embase, Medline, SCI and Cochrane Library databases up to January 2020. Heterogeneity among studies was assessed by sensitivity analysis, subgroup analysis and meta-regression, and the influence of threshold effects was also analyzed.

RESULTS

Eleven studies with 1618 patients were included. The results of the meta-analysis indicated that there was a significant relationship between MVI and nonsmooth tumor margin (DOR = 4.62 [2.73, 7.81]) and between MVI and incomplete tumor capsule (DOR = 2.25 [1.22, 4.15]); the sensitivity and specificity of these two parameters were 0.757 [0.602, 0.865], 0.597 [0.450, 0.728] and 0.646 [0.455, 0.800], 0.552 [0.419, 0.678], respectively. We drew the receiver operating characteristic (ROC) curves, and the area under curve (AUC) of the nonsmooth tumor margin variable for predicting MVI was 0.72 [0.69, 0.77], and the AUC of the incomplete tumor capsule variable for predicting MVI was 0.62 [0.58, 0.66].

CONCLUSION

Nonsmooth tumor margins and incomplete tumor capsules observed by imaging are important for the preoperative prediction of MVI in HCC.

Impact of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging on the prognosis of hepatocellular carcinoma after surgery

Background and Aim

Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (EOB-MRI) has been recognized as a useful imaging technique to distinguish the biological behavior of hepatocellular carcinoma (HCC).

Methods

We analyzed 217 hepatectomy recipients with HCCs measuring 10 cm or less. We divided the patients into a decreased intensity (DI) group (n = 189, 87%) and an increased or neutral intensity (INI) group (n = 28, 13%) according to the ratio of tumor intensity to liver intensity during the hepatobiliary phase (HBP). According to the ratio of the maximum tumor diameter (including peritumoral hypointensity) between HBP images and precontrast T1-weighted images (RHBPP), we divided the patients as follows: The group whose RHBPP was ≥1.036 was the high RHBPP group (n = 60, 28%), and the group whose RHBPP was <1.036 was the low RHBPP group (n = 157, 72%). We investigated the prognoses and clinicopathological characteristics of these patients.

Results

DI versus INI was not a prognostic factor for either survival or recurrence; however, a high RHBPP was an independent predictor of unfavorable survival and recurrence in patients. In addition, the INI group showed significantly lower α-fetoprotein (AFP) levels and higher rates of well-differentiated HCC and ICGR15 ≥15% than the DI group. The high RHBPP group showed significantly higher rates of vascular invasion and poorly differentiated HCC than the low RHBPP group.

Conclusions

A high RHBPP by EOB-MRI is a preoperative predictor of vascular invasion and an unfavorable prognostic factor for survival and recurrence. These patients might be considered for highly curative operations such as anatomical liver resection.

Computational quantitative measures of Gd-EOB-DTPA enhanced MRI hepatobiliary phase images can predict microvascular invasion of small HCC

PURPOSE

This study was designed to preoperatively predict microvascular invasion (MVI) of solitary small hepatocellular carcinoma (sHCC) by quantitative analysis of Gd-EOB-DTPA enhanced hepatobiliary phase (HBP) magnetic resonance imaging (MRI).

METHOD

Sixty-one patients, 19 with and 42 without histologically confirmed MVI following hepatic resection for solitary sHCC (≤ 3 cm), were preoperatively examined with Gd-EOB-DTPA-enhanced MRI. The regions of interest (ROIs) of the hepatic lesions were manually delineated on the maximum cross-sectional area in the HBP images and used to calculate the lesion boundary index (LBI) and marginal gray changes (MGC). Histogram analysis was performed to measure standard deviations (STD) and coefficients of variation (CV). Correlations between quantitative parameters and MVI were evaluated and differences between MVI positive and negative groups were assessed.

RESULTS

The average LBI (0.85 ± 0.07) and MGC (0.48 ± 0.27) values of the negative group were significantly higher (p < 0.05) than the corresponding LBI (0.72 ± 0.07) and MGC (0.28 ± 0.18) values of the positive group. STDs and CVs in the negative group were significantly smaller (p < 0.05) than those of the positive group. Receiver operating characteristic (ROC) analysis revealed that LBI had the best predictive value with an AUC, sensitivity, and specificity of 0.91, 87 %, and 80 %, respectively.

CONCLUSIONS

Quantitative analysis of HBP images is useful for predicting MVI and beneficial to clinicians in making decisions before treatment.

CT Image-Based Texture Analysis to Predict Microvascular Invasion in Primary Hepatocellular Carcinoma

The objective of this study was to determine the clinical value of computed tomography (CT) image-based texture analysis in predicting microvascular invasion of primary hepatocellular carcinoma (HCC). CT images of patients with HCC from May 2017 to May 2019 confirmed by surgery and histopathology were retrospectively analyzed. Image features including tumor margin, tumor capsule, peritumoral enhancement, hypoattenuating halo, intratumoral arteries, and tumor-liver differences were assessed. All patients were divided into microvascular invasion (MVI)-negative group (n = 34) and MVI-positive group (n = 68). Preoperative CT images were further imported into MaZda software, where the regions of interest of the lesions were manually delineated. Texture features of lesions based on pre-contrast, arterial, portal, and equilibrium phase CT images were extracted. Thirty optimal texture parameters were selected from each phase by Fisher's coefficient (Fisher), classification error probability combined with average correlation coefficient (POE+ACC), and mutual information (MI). Finally, receiver operating characteristic curve analysis was performed. The results showed that the Edmonson-Steiner grades, tumor size, tumor margin, and intratumoral artery characteristics were significantly different between the two groups (P = 0.012, < 0.001, < 0.001, = 0.003, respectively). There were 58 parameters with significant differences between the MVI-negative and MVI-positive groups (P < 0.001 for all). Among them, 12, 14, 17, and 15 parameters were derived from the pre-contrast phase, arterial phase, portal phase, and equilibrium phase respectively. According to the ROC analysis, optimal texture parameters based on the pre-contrast, arterial, portal, and equilibrium phases were 135dr_GLevNonU (AUC, 0.766; the cutoff value, 1055.00), Vertl_RLNonUni (AUC, 0.764; the cutoff value, 5974.38), 45dgr_GLevNonU (AUC, 0.762; the cutoff value, 924.34), and Vertl_RLNonUni (AUC, 0.754; the cutoff value, 4868.80), respectively. Texture analysis of preoperative CT images may be used as a non-invasive method to predict microvascular invasion in patients with primary hepatocellular carcinomas, and further to guide the treatment and evaluate prognosis. The most valuable parameters were derived from the gray-level run-length matrix.

Preoperative Prediction Power of Imaging Methods for Microvascular Invasion in Hepatocellular Carcinoma: A Systemic Review and Meta-Analysis

Background: To compare the predictive power between radiomics and non-radiomics (conventional imaging and functional imaging methods) for preoperative evaluation of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Methods: Comprehensive publications were screened in PubMed, Embase, and Cochrane Library. Studies focusing on the discrimination values of imaging methods, including radiomics and non-radiomics methods, for MVI evaluation were included in our meta-analysis.

Results: Thirty-three imaging studies with 5,462 cases, focusing on preoperative evaluation of MVI status in HCC, were included. The sensitivity and specificity of MVI prediction in HCC were 0.78 [95% confidence interval (CI): 0.75–0.80; I² = 70.7%] and 0.78 (95% CI: 0.76–0.81; I² = 0.0%) for radiomics, respectively, and were 0.73 (95% CI: 0.71–0.75; I² = 83.7%) and 0.82 (95% CI: 0.80–0.83; I² = 86.5%) for non-radiomics, respectively. The areas under the receiver operation curves for radiomics and non-radiomics to predict MVI status in HCC were 0.8550 and 0.8601, respectively, showing no significant difference.

Conclusion: The imaging method is feasible to predict the MVI state of HCC. Radiomics method based on medical image data is a promising application in clinical practice and can provide quantifiable image features. With the help of these features, highly consistent prediction performance will be achieved in anticipation.

The Value of TTPVI in Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Purpose

The objective of our study was to evaluate the value of two-trait predictor of venous invasion (TTPVI) in the prediction of pathological microvascular invasion (pMVI) in patients with hepatocellular carcinoma (HCC) from preoperative computed tomography (CT) and magnetic resonance (MR).

Methods

A total of 128 preoperative patients with findings of HCC were enrolled. Tumor size, tumor margins, tumor capsule, peritumoral enhancement, and TTPVI was assessed on preoperative CT and MRI images. Histopathological features were reviewed: pathological tumor size, tumor differentiation, pMVI along with alpha-fetoprotein level (AFP). Significant imaging findings and histopathological features were determined with univariate and multivariate logistic regression analysis.

Results

Univariate analysis revealed that tumor size (p<0.01), AFP level (p=0.043), tumor differentiation (p<0.01), peritumoral enhancement (p=0.003), pathological tumor size (p<0.01), tumor margins (p<0.01) on CT and MRI, and TTPVI (p<0.01) showed statistically significant associations with pMVI. In multivariate logistic regression analysis, tumor size (odds ratio [OR] = 1.294; 95% confidence interval [CI]: 1.155, 1.451; p < 0.001), tumor differentiation (odds ratio [OR] =1.384; 95% confidence interval [CI]: 1.224, 1.564; p < 0.001), and TTPVI (odds ratio [OR] = 4.802; 95% confidence interval [CI]: 1.037, 22.233; p=0.045) were significant independent predictors of pMVI. Using 5.8 as the threshold for size, one could obtain an area-under-curve (AUC) of 0.793, 95% confidence interval [CI]: 0.715 to 0.857.

Conclusion

Tumor size, tumor differentiation, and TTPVI depicted in preoperative CT and MRI had a statistically significant correlation with pMVI. Hence, TTPVI detected on CT and MRI may be predictive of pMVI in HCC cases.

Direct Comparison of Four Presurgical Stratifying Schemes for Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced MRI

BACKGROUND

Microvascular invasion (MVI) is implicated in the poor prognosis of hepatocellular carcinoma (HCC). Presurgical stratifying schemes have been proposed for HCC-MVI but lack external validation.

PURPOSE

To perform external validation and comparison of four presurgical stratifying schemes for the prediction of MVI using gadoxetic acid-based MRI in a cohort of HCC patients.

STUDY TYPE

Retrospective.

SUBJECTS

Included were 183 surgically resected HCCs from patients who underwent pretreatment MRI.

FIELD STRENGTH/SEQUENCE

This includes 1.5-3.0 T with T₂ , T₁ , diffusion-weighted imaging (DWI), and dynamic gadoxetic acid contrast-enhancement imaging sequences.

ASSESSMENT

A two-trait predictor of venous invasion (TTPVI), Lei model, Lee model, and Xu model were compared. We relied on preoperative characteristics and imaging findings via four independent radiologists who were blinded to histologic results, as required by the tested tools.

STATISTICAL TEST

Tests of accuracy between predicted and observed HCC-MVI rates using receiver operating characteristic (ROC) curve and decision curve analysis. The intraclass correlation coefficient (ICC) and Cronbach's alpha statistics were used to evaluate reproducibility.

RESULTS

HCC-MVI was identified in 52 patients (28.4%). The average ROC curves (AUCs) for HCC-MVI predictions were 0.709-0.880, 0.714-0.828, and 0.588-0.750 for the Xu model, Lei model, and Lee model, respectively. The rates of accuracy were 60.7-81.4%, 69.9-75.9%, and 65.6-73.8%, respectively. Decision curve analyses indicated a higher benefit for the Xu and Lei models compared to the Lee model. The ICC and Cronbach's alpha index were highest in the Lei model (0.896/0.943), followed by the Xu model (0.882/0.804), and the Lee model (0.769/0.715). The TTPVI resulted in a Cronbach's alpha index of 0.606 with a sensitivity of 34.6-61.5% and a specificity of 76.3-91.6%.

DATA CONCLUSION

Stratifying schemes relying on gadoxetic acid-enhanced MRI provide an additional insight into the presence of preoperative MVI. The Xu model outperformed the other models in terms of accuracy when performed by an experienced radiologist. Conversely, the Lei model outperformed the other models in terms of reproducibility.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:433-447.

The Role of Preoperative Dynamic Contrast-enhanced 3.0-T MR Imaging in Predicting Early Recurrence in Patients With Early-Stage Hepatocellular Carcinomas After Curative Resection

Objectives: Liver resection is potentially curative for early-stage hepatocellular carcinoma (eHCC) in patients with well-preserved liver function. The prognosis of these patients after resection is still unsatisfactory because of frequent early recurrence (ER). Therefore, we investigated the role of preoperative dynamic contrast-enhanced 3.0-T MR imaging in predicting ER of eHCC after curative resection. Methods From May 2014 to October 2017, we retrospectively analyzed 82 patients with eHCC who underwent dynamic MR imaging and subsequently underwent curative resection. Liver Imaging Reporting and Data System (LI-RADS) v2018 major and ancillary imaging features, as well as two non-LI-RADS MR imaging features (irregular tumor margin and tumor number), were evaluated. A multivariate Cox regression analysis was used to identify independent predictors, and two models (preoperative and postoperative prediction models) were developed. Results ER was observed in 25 patients (25/82, 30.5%). In the univariate analyses, preoperative alpha-fetoprotein (AFP) level >200 ng/ml, three MR imaging features (multifocal tumors, corona enhancement, and irregular tumor margin), and microvascular invasion (MVI) were associated with ER. In the multivariate analysis, corona enhancement (hazard ratio [HR]: 2.970; p = 0.013) and irregular tumor margin (HR: 2.377; p = 0.048) were independent predictors in the preoperative prediction model, and preoperative AFP level >200 ng/ml (HR: 2.493; p = 0.044) plus corona enhancement (HR: 3.046; p = 0.014) were independent predictors in the postoperative prediction model (microvascular invasion [MVI] was not; p = 0.061). When combined with both predictors, the specificity for ER in the preoperative prediction model was 98.2% (56/57), which was comparable to that of the postoperative prediction model [96.7% (55/57)]. Conclusions Our results demonstrated that preoperative MR imaging features (corona enhancement and irregular tumor margin) have the potential to preoperatively identify high-risk ER patients with eHCC, with a specificity >90%.

Incomplete tumor capsule on preoperative imaging reveals microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

PURPOSE

Microvascular invasion (MVI), which is difficult to diagnose before surgery, is a major factor affecting postoperative recurrence in patients with hepatocellular carcinoma (HCC). The relationship between the radiological tumor capsule and MVI is controversial. This study aimed to evaluate the association between the tumor capsule and MVI.

METHODS

We searched Medline (by PubMed) and Embase (by OvidSP). Two review authors independently screened titles and abstracts, selected studies about MVI prediction with radiologic tumor capsule and studies with enough data for extracted, assessed the methodological quality and collected data. Summary results were presented as the diagnostic odds ratio (DOR), sensitivity, specificity, and 95% confidence interval.

RESULTS

Fifteen studies with 2038 patients were included; fourteen studies, including 1331 patients, with no significant heterogeneity indicated no relationship between absent tumor capsule and MVI [DOR = 0.90 (0.64, 1.26)]. Six studies, including 541 patients, with no significant heterogeneity showed incomplete capsule could be used to predict MVI of HCC preoperatively [DOR = 1.85 (1.13, 3.04)]. The overall sensitivity and specificity estimate were 0.50 (0.37, 0.64) and 0.64 (0.53, 0.74), respectively. Eight studies, including 1349 patients, with highly significant heterogeneity revealed that complete capsule could be a protective factor for MVI [DOR = 1.97 (1.01, 3.86)].

CONCLUSIONS

For MVI of HCC, incomplete tumor capsule is a risk factor, while a complete tumor capsule might be a protective factor. However, absent capsule doesn't show significant relationship with MVI. This might be due to combination of the risk and protective effects of present capsule in MVI.

Peritumoral ADC values in breast cancer: region of interest selection, associations with hyaluronan intensity, and prognostic significance

Objectives

We aimed to evaluate the differences in peritumoral apparent diffusion coefficient (ADC) values by four different ROI selection methods and to validate the optimal method. Furthermore, we aimed to evaluate if the peritumor-tumor ADC ratios are correlated with axillary lymph node positivity and hyaluronan accumulation.

Methods

Altogether, 22 breast cancer patients underwent 3.0-T breast MRI, histopathological evaluation, and hyaluronan assay. Paired t and Friedman tests were used to compare minimum, mean, and maximum values of tumoral and peritumoral ADC by four methods: (M1) band ROI, (M2) whole tumor surrounding ROI, (M3) clockwise multiple ROI, and (M4) visual assessment of ROI selection. Subsequently, peritumor/tumor ADC ratios were compared with hyaluronan levels and axillary lymph node status by the Mann-Whitney U test.

Results

No statistically significant differences were found among the four ROI selection methods regarding minimum, mean, or maximum values of tumoral and peritumoral ADC. Visual assessment ROI measurements represented the less time-consuming evaluation method for the peritumoral area, and with sufficient accuracy. Peritumor/tumor ADC ratios obtained by all methods except the clockwise ROI (M3) showed a positive correlation with hyaluronan content (M1, p = 0.004; M2, p = 0.012; M3, p = 0.20; M4, p = 0.025) and lymph node metastasis (M1, p = 0.001; M2, p = 0.007; M3, p = 0.22; M4, p = 0.015), which are established factors for unfavorable prognosis.

Conclusions

Our results suggest that the peritumor/tumor ADC ratio could be a readily applicable imaging index associated with axillary lymph node metastasis and extensive hyaluronan accumulation. It could be related to the biological aggressiveness of breast cancer and therefore might serve as an additional prognostic factor.

Key Points

• Out of four different ROI selection methods for peritumoral ADC evaluation, measurements based on visual assessment provided sufficient accuracy and were the less time-consuming method.

• The peritumor/tumor ADC ratio can provide an easily applicable supplementary imaging index for breast cancer assessment.

• A higher peritumor/tumor ADC ratio was associated with axillary lymph node metastasis and extensive hyaluronan accumulation and might serve as an additional prognostic factor.

Gadoxetic acid-enhanced magnetic resonance imaging can predict the pathologic stage of solitary hepatocellular carcinoma

BACKGROUND

Although important for determining long-term outcome, pathologic stage of hepatocellular carcinoma (HCC) is difficult to predict before surgery. Current state-of-the-art magnetic resonance imaging (MRI) using gadoxetic acid provides many imaging features that could potentially be used to classify single HCC as pT1 or pT2.

AIM

To determine which gadoxetic acid-enhanced MRI (EOB-MRI) findings predict pathologic stage T2 in patients with solitary HCC (cT1).

METHODS

Pre-operative EOB-MRI findings were reviewed in a retrospective cohort of patients with solitary HCC. The following imaging features were examined: Hyperintensity in unenhanced T2-weighted images, hypointensity in unenhanced T1-weighted images, arterial enhancement, corona enhancement, washout appearance, capsular appearance, hypointensity in the tumor tissue during the hepatobiliary (HB) phase, peritumoral hypointensity in the HB phase, hypointense rim in the HB phase, intratumoral fat, hyperintensity on diffusion-weighted imaging, hypointensity on apparent diffusion coefficient map, mosaic appearance, nodule-in-nodule appearance, and the margin (smooth or irregular). Surgical pathology was used as the reference method for tumor staging. Univariate and multivariate analyses were performed to identify predictors of microvascular invasion or satellite nodules.

RESULTS

There were 39 (34.2%; 39 of 114) and 75 (65.8%; 75 of 114) pathological stage T2 and T1 HCCs, respectively. Large tumor size (≥ 2.3 cm) and two MRI findings, i.e., corona enhancement [odds ratio = 2.67; 95% confidence interval: 1.101-6.480] and peritumoral hypointensity in HB phase images (odds ratio = 2.203; 95% confidence interval: 0.961-5.049) were associated with high risk of pT2 HCC. The positive likelihood ratio was 6.25 (95% confidence interval: 1.788-21.845), and sensitivity of EOB-MRI for detecting pT2 HCC was 86.2% when two or three of these MRI features were present. Small tumor size and hypointense rim in the HB phase were regarded as benign features. Small HCCs with hypointense rim but not associated with aggressive features were mostly pT1 lesions (specificity, 100%).

CONCLUSION

Imaging features on EOB-MRI could potentially be used to predict the pathologic stage of solitary HCC (cT1) as pT1 or pT2.

Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI

OBJECTIVES

Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients.

METHODS

This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients. All the patients underwent preoperative Gd-EOB-DTPA-enhanced MRI examination and curative hepatectomy. The volumes of interest (VOIs) around the hepatic lesions including intratumoural and peritumoural regions were manually delineated in the hepatobiliary phase of MRI images, from which quantitative features were extracted and analysed. In the training cohort, machine-learning method was applied for dimensionality reduction and selection of the extracted features.

RESULTS

The proportion of MVI-positive patients was 38.2% and 40.0% in the training and validation cohort, respectively. Supervised machine learning selected ten features to establish a predictive model for MVI. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity of the combined intratumoural and peritumoural radiomics model in the training and validation cohort were 0.85 (95% confidence interval (CI), 0.77-0.93), 88.2%, 76.2%, and 0.83 (95% CI, 0.71-0.95), 90.0%, 75.0%, respectively.

CONCLUSIONS

We evaluate quantitative Gd-EOB-DTPA-enhanced MRI image features of both intratumoural and peritumoural regions and provide an effective radiomics-based model for the prediction of MVI in HCC patients, and may therefore help clinicians make precise decisions regarding treatment before the surgery.

KEY POINTS

• An effective radiomics model for prediction of microvascular invasion in HCC patients is established. • The radiomics model is superior to the radiologist in prediction of MVI. • The radiomics model can help clinicians in pretreatment decision making.