Histological differentiation grade and microvascular invasion of hepatocellular carcinoma predicted by dynamic contrast-enhanced MRI

Advancing microvascular invasion diagnosis: a multi-center investigation of novel MRI-based models for precise detection and classification in early-stage small hepatocellular carcinoma (≤ 3 cm)

PURPOSE

This study aimed to develop two preoperative magnetic resonance imaging (MRI) based models for detecting and classifying microvascular invasion (MVI) in early-stage small hepatocellular carcinoma (sHCC) patients.

METHODS

MVI is graded as M0 (no invasion), M1 (invasion of five or fewer vessels located within 1 cm of the tumor's peritumoral region), and M2 (invasion of more than five vessels or those located more than 1 cm from the tumor's surface). This study enrolled 395 early-stage sHCC (≤ 3 cm) patients from three centers who underwent preoperative gadopentetate-enhanced MRI. From the first two centers, 310 patients were randomly divided into training (n = 217) and validation (n = 93) cohorts in a 7:3 ratio to develop the first model for predicting MVI presence. Among these, 153 patients with identified MVI were further divided into training (n = 112) and validation (n = 41) cohorts, using the same ratio, to construct the second model for MVI classification. An independent test cohort of 85 patients from the third center to validate both models. Univariate and multivariate logistic regression analyses identified independent predictors of MVI and its classification in the training cohorts. Based on these predictors, two nomograms were developed and assessed for their discriminative ability, calibration, and clinical usefulness. Besides, considering the two models are supposed applied in a serial fashion in the real clinical setting, we evaluate the performance of the two models together on the test cohorts by applying them simultaneously. Kaplan-Meier survival curve analysis was employed to assess the correlation between predicted MVI status and early recurrence, similar to the association observed with actual MVI status and early recurrence.

RESULTS

The MVI detection nomogram, with platelet count (PLT), activated partial thromboplastin time (APTT), rim arterial phase hyperenhancement (Rim APHE) and arterial peritumoral enhancement, achieved area under the curve (AUC) of 0.827, 0.761 and 0.798 in the training, validation, and test cohorts, respectively. The MVI classification nomogram, integrating Total protein (TP), Shape, Arterial peritumoral enhancement and enhancement pattern, achieved AUC of 0.824, 0.772, and 0.807 across the three cohorts. When the two models were applied on the test cohorts in a serial fashion, they both demonstrated good performance, which means the two models had good clinical applicability. Calibration and decision curve analysis (DCA) results affirmed the model's reliability and clinical utility. Notably, early recurrence was more prevalent in the MVI grade 2 (M2) group compared to the MVI-absent and M1 groups, regardless of the actual or predicted MVI status.

CONCLUSIONS

The nomograms exhibited excellent predictive performance for detecting and classifying MVI in patients with early-stage sHCC, particularly identifying high-risk M2 patients preoperatively.

Multiparametric assessment of microvascular invasion in hepatocellular carcinoma using gadoxetic acid-enhanced MRI

PURPOSE

To elucidate how precisely microvascular invasion (MVI) in hepatocellular carcinoma (HCC) can be predicted using multiparametric assessment of gadoxetic acid-enhanced MRI.

METHODS

In this retrospective single-center study, patients who underwent liver resection or transplantation of HCC were evaluated. Data obtained in patients who underwent liver resection were used as the training set. Nine kinds of MR findings for predicting MVI were compared between HCCs with and without MVI by univariate analysis, followed by multiple logistic regression analysis. Using significant findings, a predictive formula for diagnosing MVI was obtained. The diagnostic performance of the formula was investigated in patients who underwent liver resection (validation set 1) and in patients who underwent liver transplantation (validation set 2) using a receiver operating characteristic curve analysis. The area under the curves (AUCs) of these three groups were compared.

RESULTS

A total of 345 patients with 356 HCCs were selected for analysis. Tumor diameter (D) (P = 0.021), tumor washout (TW) (P < 0.01), and peritumoral hypointensity in the hepatobiliary phase (PHH) (P < 0.01) were significantly associated with MVI after multivariate analysis. The AUCs for predicting MVI of the predictive formula were as follows: training set, 0.88 (95% confidence interval (CI) 0.82,0.93); validation set 1, 0.81 (95% CI 0.73,0.87); validation set 2, 0.67 (95% CI 0.51,0.80). The AUCs were not significantly different among three groups (training set vs validation set 1; P = 0.15, training set vs validation set 2; P = 0.09, validation set 1 vs validation set 2; P = 0.29, respectively).

CONCLUSION

Our multiparametric assessment of gadoxetic acid-enhanced MRI performed quite precisely and with good reproducibility for predicting MVI.

CT-based radiomics for predicting pathological grade in hepatocellular carcinoma

Objective

To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT).

Methods

Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency.

Conclusions

Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.

Dynamic radiomics based on contrast-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma

OBJECTIVE

To exploit the improved prediction performance based on dynamic contrast-enhanced (DCE) MRI by using dynamic radiomics for microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

METHODS

We retrospectively included 175 and 75 HCC patients who underwent preoperative DCE-MRI from September 2019 to August 2022 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Static radiomics features were extracted from the mask, arterial, portal venous, and equilibrium phase images and used to construct dynamic features. The static, dynamic, and dynamic-static radiomics (SR, DR, and DSR) signatures were separately constructed based on the feature selection method of LASSO and classification algorithm of logistic regression. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each signature.

RESULTS

In the three radiomics signatures, the DSR signature performed the best. The AUCs of the SR, DR, and DSR signatures in the training set were 0.750, 0.751 and 0.805, respectively, while in the external validation set, the corresponding AUCs were 0.706, 0756 and 0.777. The DSR signature showed significant improvement over the SR signature in predicting MVI status (training cohort: P = 0.019; validation cohort: P = 0.044). After external validation, the AUC value of the SR signature decreased from 0.750 to 0.706, while the AUC value of the DR signature did not show a decline (AUCs: 0.756 vs. 0.751).

CONCLUSIONS

The dynamic radiomics had an improved effect on the MVI prediction in HCC, compared with the static DCE MRI-based radiomics models.

Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma

Background

The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Methods

A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models.

Results

The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively.

Conclusions

Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.

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

PURPOSE

To compare the performances of gadoxetate disodium-enhanced MRI (EOB-MRI) and extracellular contrast agent-enhanced MRI (ECA-MRI) for predicting microvascular invasion (MVI) in HCC.

MATERIALS AND METHODS

From November 2009 to December 2021, consecutive HCC patients who underwent preoperative contrast-enhanced MRI were retrospectively enrolled into either an ECA-MRI or EOB-MRI cohort. In the ECA-MRI cohort, a preoperative MVI score was constructed in the training dataset using a logistic regression model that evaluated pathological type. In a propensity score-matched testing dataset of the ECA-MRI cohort, the MVI score was validated and compared with a previously proposed EOB-MRI-based MVI score calculated in the EOB-MRI cohort. Time-to-early recurrence survival was evaluated by the Kaplan-Meier method with the log-rank test.

RESULTS

A total of 536 patients were included (478 men; 53 years, interquartile range, 46-62 years), 322 (60.1 %) with pathologically confirmed MVI. Based on the training dataset, independent variables associated with MVI included serum alpha-fetoprotein > 400 ng/ml (odds ratio [OR] = 2.3), infiltrative appearance (OR = 4.9), internal artery (OR = 2.5) and nodule-in-nodule architecture (OR = 2.4), which were incorporated into the ECA-MRI-based MVI score. The testing dataset AUC of the ECA-MRI score was 0.720, which was comparable to that of the EOB-MRI-based MVI score (AUC = 0.721; P =.99). Patients from either the ECA-MRI or the EOB-MRI cohort with model-predicted MVI had significantly shorter time-to-early recurrence than those without MVI (P <.001).

CONCLUSION

Based on the preoperative serum alpha-fetoprotein and three MRI features, ECA-MRI demonstrated comparable performance to EOB-MRI for predicting MVI in HCC.

Understanding the role of radiologists in complex treatment decisions for patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver and represents a significant global health burden. Management of HCC can be challenging due to multiple factors, including variable expectations for treatment outcomes. Several treatment options are available, each with specific eligibility and ineligibility criteria, and are provided by a multidisciplinary team of specialists. Radiologists should be aware of the types of treatment options available, as well as the criteria guiding the development of individualized treatment plans. This awareness enables radiologists to contribute effectively to patient-centered multidisciplinary tumor boards for HCC and play a central role in reassessing care plans when the treatment response is deemed inadequate. This comprehensive review aims to equip radiologists with an overview of HCC staging systems, treatment options, and eligibility criteria. The review also discusses the significance of imaging in HCC diagnosis, treatment planning, and monitoring treatment response. Furthermore, we highlight the crucial branch points in the treatment decision-making process that depend on radiological interpretation.

Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.

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.

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

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

Radiologic features of hepatocellular carcinoma related to prognosis

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

Predicting histologic differentiation of solitary hepatocellular carcinoma up to 5 cm on gadoxetate disodium-enhanced MRI

BACKGROUND

To establish a preoperative score based on gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI) and clinical indicators for predicting histologic differentiation of solitary HCC up to 5 cm.

METHODS

From July 2015 to January 2022, consecutive patients with surgically proven solitary HCC measuring ≤ 5 cm at preoperative EOB-MRI were retrospectively enrolled. All MR images were independently evaluated by two radiologists who were blinded to all clinical and pathologic information. Univariate and multivariate logistic regression analyses were performed to identify significant clinicoradiological features associated with poorly differentiated (PD) HCC, which were then incorporated into the predictive score. The predictive score was validated in an independent validation set by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

A total of 182 patients were included, 42 (23%) with PD HCC. According to the multivariate analysis, marked hepatobiliary phase hypointensity (odds ratio [OR], 9.98), LR-M category (OR, 5.60), and serum alpha-fetoprotein (AFP) level > 400 ng/mL (OR, 3.58) were incorporated into the predictive model; the predictive score achieved an AUC of 0.802 and 0.830 on the training and validation sets, respectively. The sensitivity, specificity, and accuracy of the predictive score were 66.7%, 85.7%, and 81.3%, respectively, on the training set and 66.7%, 81.0%, and 77.8%, respectively, on the validation set.

CONCLUSION

The proposed score integrating two EOB-MRI features and AFP level can accurately predict PD HCC in the preoperative setting.

DLNLF-net: Denoised local and non-local deep features fusion network for malignancy characterization of hepatocellular carcinoma

INTRODUCTION

Hepatocellular carcinoma (HCC) is a primary liver cancer with high mortality rate. The degree of HCC malignancy is an important prognostic factor for predicting recurrence and survival after surgical resection or liver transplantation in clinical practice. Currently, deep features obtained from data-driven machine learning algorithms have demonstrated superior performance in characterising lesion features in medical imaging processing. However, previous convolutional neural network (CNN)-based studies on HCC lesion characterisation were based on traditional local deep features. The aim of this study was to propose a denoised local and non-local deep features fusion network (DLNLF-net) for grading HCC.

METHODS

Gadolinium-diethylenetriaminepentaacetic-acid-enhanced magnetic resonance imaging data of 117 histopathologically proven HCCs were collected from 112 patients with resected HCC between October 2012 and October 2018. The proposed DLNLF-net primarily consists of three modules: feature denoising, non-local feature extraction, and bilinear kernel fusion. First, local feature maps were extracted from the original tumour images using convolution operations, followed by a feature denoising block to generate denoised local features. Simultaneously, a non-local feature extraction block was employed on the local feature maps to generate non-local features. Finally, the two generated features were fused using a bilinear kernel model to output the classification results. The dataset was divided into a training set (77 HCC images) and an independent test set (40 HCC images). Training and independent testing were repeated five times to reduce measurement errors. Accuracy, sensitivity, specificity, and area under the curve (AUC) values in the five repetitive tests were calculated to evaluate the performance of the proposed method.

RESULTS

Denoised local features (AUC 89.19%) and non-local features (AUC 88.28%) showed better performance than local features (AUC 86.21%) and global average pooling features (AUC 87.1%) that were derived from a CNN for malignancy characterisation of HCC. Furthermore, the proposed DLNFL-net yielded superior performance (AUC 94.89%) than a typical 3D CNN (AUC 86.21%), bilinear CNN (AUC 90.46%), recently proposed local and global diffusion method (AUC 93.94%), and convolutional block attention module method (AUC 93.62%) for malignancy characterisation of HCC.

CONCLUSION

The non-local operation demonstrated a better capability of yielding global representation, and feature denoising based on the non-local operation achieved performance gains for lesion characterisation. The proposed DLNLF-net, which integrates denoised local and non-local deep features, evidently outperforms conventional CNN-based methods in the malignancy characterisation of HCC.

Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI

Objective: To investigate the role of prediction microvascular invasion (mVI) in hepatocellular carcinoma (HCC) by ¹⁸F-FDG PET image texture analysis and hybrid criteria combining PET/CT and multi-parameter MRI.

Materials and methods: Ninety-seven patients with HCC who received the examinations of MRI and ¹⁸F-FDG PET/CT were retrospectively included in this study and were randomized into training and testing cohorts. The lesion image texture features of ¹⁸F-FDG PET were extracted using MaZda software. The optimal predictive texture features of mVI were selected, and the classification procedure was conducted. The predictive performance of mVI by radiomics classier in training and testing cohorts was respectively recorded. Next, the hybrid model was developed by integrating the ¹⁸F-FDG PET image texture, metabolic parameters, and MRI parameters to predict mVI through logistic regression. Furthermore, the diagnostic performance of each time was recorded.

Results: The ¹⁸F-FDG PET image radiomics classier showed good predicted performance in both training and testing cohorts to discriminate HCC with/without mVI, with an AUC of 0.917 (95% CI: 0.824–0.970) and 0.771 (95% CI: 0.578, 0.905). The hybrid model, which combines radiomics classier, SUVmax, ADC, hypovascular arterial phase enhancement pattern on contrast-enhanced MRI, and non-smooth tumor margin, also yielded better predictive performance with an AUC of 0.996 (95% CI: 0.939, 1.000) and 0.953 (95% CI: 0.883, 1.000). The differences in AUCs between radiomics classier and hybrid classier were significant in both training and testing cohorts (DeLong test, both p < 0.05).

Conclusion: The radiomics classier based on ¹⁸F-FDG PET image texture and the hybrid classier incorporating ¹⁸F-FDG PET/CT and MRI yielded good predictive performance, which might provide a precise prediction of HCC mVI preoperatively.

Preoperative gadoxetic acid-enhanced MRI predicts aggressive pathological features in LI-RADS category 5 hepatocellular carcinoma

AIM

To investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features and non-LI-RADS imaging features can predict aggressive pathological features in adult patients with hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

From February 2018 to September 2021, 236 adult patients with cirrhosis or hepatitis B virus infection in which liver cancer was suspected underwent MRI within 1 month before surgery. Significant MRI findings and alpha-fetoprotein (AFP) level predicted high-grade HCC and microvascular invasion (MVI) by univariate and multivariate logistic regression models.

RESULTS

The study included 112 patients with histopathologically confirmed liver cancer (≤5 cm), 35 of whom (31.3%) high-grade HCC and 42 of 112 (37.5%) patients had MVI. Mosaic architecture (odds ratio [OR] = 6.031; 95% confidence interval [CI]: 1.366, 26.626; p=0.018), coronal enhancement (OR=5.878; 95% CI: 1.471, 23.489; p=0.012), and intratumoural vessels (OR=5.278; 95% CI: 1.325, 21.020; p=0.018) were significant independent predictors of high-grade HCC. A non-smooth tumour margin (OR=10.237; 95% CI: 1.547, 67.760; p=0.016), coronal enhancement (OR=3.800; 95% CI: 1.152, 12.531; p=0.028), and peritumoural hypointensity on the hepatobiliary phase (HBP; OR=10.322; 95% CI: 2.733, 38.986; p=0.001) were significant independent predictors of MVI.

CONCLUSION

In high-risk adult patients with single LR-5 HCC (≤5 cm), mosaic architecture, coronal enhancement, and intratumoural vessels are independent predictors of high-grade HCC. Non-smooth tumour margin, coronal enhancement, and peritumoural hypointensity on HBP independently predicted MVI.

Predicting microvascular invasion in hepatocellular carcinoma: A dual-institution study on gadoxetate disodium-enhanced MRI

BACKGROUND & AIMS

Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but its diagnosis mandates postoperative histopathologic analysis. We aimed to develop and externally validate a predictive scoring system for MVI.

METHODS

From July 2015 to November 2020, consecutive patients underwent surgery for HCC with preoperative gadoxetate disodium (EOB)-enhanced MRI was retrospectively enrolled. All MR images were reviewed independently by two radiologists who were blinded to the outcomes. In the training centre, a radio-clinical MVI score was developed via logistic regression analysis against pathology. In the testing centre, areas under the receiver operating curve (AUCs) of the MVI score and other previous MVI schemes were compared. Overall survival (OS) and recurrence-free survival (RFS) were analysed by the Kaplan-Meier method with the log-rank test.

RESULTS

A total of 417 patients were included, 195 (47%) with pathologically-confirmed MVI. The MVI score included: non-smooth tumour margin (odds ratio [OR] = 4.4), marked diffusion restriction (OR = 3.0), internal artery (OR = 3.0), hepatobiliary phase peritumoral hypointensity (OR = 2.5), tumour multifocality (OR = 1.6), and serum alpha-fetoprotein >400 ng/mL (OR = 2.5). AUCs for the MVI score were 0.879 (training) and 0.800 (testing), significantly higher than those for other MVI schemes (testing AUCs: 0.648-0.684). Patients with model-predicted MVI had significantly shorter OS (median 61.0 months vs not reached, P < .001) and RFS (median 13.0 months vs. 42.0 months, P < .001) than those without.

CONCLUSIONS

A preoperative MVI score integrating five EOB-MRI features and serum alpha-fetoprotein level could accurately predict MVI and postoperative survival in HCC. Therefore, this score may aid in individualized treatment decision making.

Development of Models to Predict Postoperative Complications for Hepatitis B Virus-Related Hepatocellular Carcinoma

Background

Surgical treatment remains the best option for patients with hepatocellular carcinoma (HCC) caused by chronic hepatitis B virus (HBV) infection. However, there is no optimal tool based on readily accessible clinical parameters to predict postoperative complications. Herein, our study aimed to develop models that permitted risk of severe complications to be assessed before and after liver resection based on conventional variables.

Methods

A total of 1,047 patients treated by hepatectomy for HCC with HBV infection at three different centers were recruited retrospectively between July 1, 2014, and July 1, 2018. All surgical complications were recorded and scored by the Comprehensive Complication Index (CCI). A CCI ≥26.2 was used as a threshold to define patients with severe complications. We built two models for the CCI, one using preoperative and one using preoperative and postoperative data. Besides, CCI and other potentially relevant factors were evaluated for their ability to predict early recurrence and metastasis. All the findings were internally validated in the Hangzhou cohort and then externally validated in the Lanzhou and Urumqi cohorts.

Results

Multivariable analysis identified National Nosocomial Infections Surveillance (NNIS) index, tumor number, gamma-glutamyltransferase (GGT), total cholesterol (TC), potassium, and thrombin time as the key preoperative parameters related to perioperative complications. The nomogram based on the preoperative model [preoperative CCI After Surgery for Liver tumor (CCIASL-pre)] showed good discriminatory performance internally and externally. A more accurate model [postoperative CCI After Surgery for Liver tumor (CCIASL-post)] was established, combined with the other four postoperative predictors including leukocyte count, basophil count, erythrocyte count, and total bilirubin level. No significant association was observed between CCI and long-term complications.

Conclusion

Based on the widely available clinical data, statistical models were established to predict the complications after hepatectomy in patients with HBV infection. All the findings were extensively validated and shown to be applicable nationwide. Such models could be used as guidelines for surveillance follow-up and the design of post-resection adjuvant therapy.

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.

Pathologic, Molecular, and Prognostic Radiologic Features of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a malignancy with variable biologic aggressiveness based on the tumor grade, presence or absence of vascular invasion, and pathologic and molecular classification. Knowledge and understanding of the prognostic implications of different pathologic and molecular phenotypes of HCC are emerging, with therapeutics that promise to provide improved outcomes in what otherwise remains a lethal cancer. Imaging has a central role in diagnosis of HCC. However, to date, the imaging algorithms do not incorporate prognostic features or subclassification of HCC according to its biologic aggressiveness. Emerging data suggest that some imaging features and further radiologic, pathologic, or radiologic-molecular phenotypes may allow prediction of the prognosis of patients with HCC. An invited commentary by Bashir is available online. Online supplemental material is available for this article. ^©RSNA, 2021.

United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI

Simultaneous segmentation and detection of liver tumors (hemangioma and hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it is still a challenging task due to: (1) the HCC information on NCMRI is insufficient makes extraction of liver tumors feature difficult; (2) diverse imaging characteristics in multi-modality NCMRI causes feature fusion and selection difficult; (3) no specific information between hemangioma and HCC on NCMRI cause liver tumors detection difficult. In this study, we propose a united adversarial learning framework (UAL) for simultaneous liver tumors segmentation and detection using multi-modality NCMRI. The UAL first utilizes a multi-view aware encoder to extract multi-modality NCMRI information for liver tumor segmentation and detection. In this encoder, a novel edge dissimilarity feature pyramid module is designed to facilitate the complementary multi-modality feature extraction. Secondly, the newly designed fusion and selection channel is used to fuse the multi-modality feature and make the decision of the feature selection. Then, the proposed mechanism of coordinate sharing with padding integrates the multi-task of segmentation and detection so that it enables multi-task to perform united adversarial learning in one discriminator. Lastly, an innovative multi-phase radiomics guided discriminator exploits the clear and specific tumor information to improve the multi-task performance via the adversarial learning strategy. The UAL is validated in corresponding multi-modality NCMRI (i.e. T1FS pre-contrast MRI, T2FS MRI, and DWI) and three phases contrast-enhanced MRI of 255 clinical subjects. The experiments show that UAL gains high performance with the dice similarity coefficient of 83.63%, the pixel accuracy of 97.75%, the intersection-over-union of 81.30%, the sensitivity of 92.13%, the specificity of 93.75%, and the detection accuracy of 92.94%, which demonstrate that UAL has great potential in the clinical diagnosis of liver tumors.

Comparative study of evaluating the microcirculatory function status of primary small HCC between the CE (DCE-MRI) and Non-CE (IVIM-DWI) MR Perfusion Imaging

PURPOSE

To compare the difference of evaluating the microcirculatory function status of primary small HCC between DCE-MRI with two-compartmental pharmacokinetic model and IVIM-DWI.

METHODS

27 patients (22 men, 5 women; mean age, 49 years; range 36-65 years) with primary single sHCC who underwent IVIM-DWI and DCE-MRI before the operation were included in this retrospective study. The MR perfusion parameters are Ktrans, Ve, Kep, D, D* and f. Pathological results include pathological grade (low grade ≤ II, high grade > II) and MVD. The perfusion parameters and pathological results of sHCC were analyzed and compared in their relevance, sensitivity and specificity. Statistical methods included Spearman and ROC curve analysis.

RESULTS

The perfusion parameters (Ktrans, Kep, D*, f) were significantly positive correlated (r = 0.892, 0.808, 0.589 and 0.543, P = 0.000, 0.000, 0.001 and 0.003 with MVD of sHCC. The parameter Ve and D values were negatively correlated (r = - 0.454 and - 0.399, P = 0.017 and 0.039, respectively) with the pathological grade. Regarding the evaluation MVD of sHCC, the evaluation of the sensitivity and specificity performance was present in descending order: Ktrans > Kep > PF > D*. In the evaluation pathological grade of sHCC, the sensitivity and specificity were better by parameters D than Ve.

CONCLUSION

DCE-MRI is better than IVIM-DWI for evaluation microcirculation functional status of sHCC. But for evaluating the pathological grade, IVIM-DWI is better than DCE-MRI. Combination of the two imaging techniques may provide more comprehensive evaluation in microcirculation functional status of the sHCC.

Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI

OBJECTIVES

Pretreatment evaluation of tumor biology and microenvironment is important to predict prognosis and plan treatment. We aimed to develop nomograms based on gadoxetic acid-enhanced MRI to predict microvascular invasion (MVI), tumor differentiation, and immunoscore.

METHODS

This retrospective study included 273 patients with HCC who underwent preoperative gadoxetic acid-enhanced MRI. Patients were assigned to two groups: training (N = 191) and validation (N = 82). Univariable and multivariable logistic regression analyses were performed to investigate clinical variables and MRI features' associations with MVI, tumor differentiation, and immunoscore. Nomograms were developed based on features associated with these three histopathological features in the training cohort, then validated, and evaluated.

RESULTS

Predictors of MVI included tumor size, rim enhancement, capsule, percent decrease in T1 images (T1_D%), standard deviation of apparent diffusion coefficient, and alanine aminotransferase levels, while capsule, peritumoral enhancement, mean relaxation time on the hepatobiliary phase (T1_E), and alpha-fetoprotein levels predicted tumor differentiation. Predictors of immunoscore included the radiologic score constructed by tumor number, intratumoral vessel, margin, capsule, rim enhancement, T1_D%, relaxation time on plain scan (T1_P), and alpha-fetoprotein and alanine aminotransferase levels. Three nomograms achieved good concordance indexes in predicting MVI (0.754, 0.746), tumor differentiation (0.758, 0.699), and immunoscore (0.737, 0.726) in the training and validation cohorts, respectively.

CONCLUSION

MRI-based nomograms effectively predict tumor behaviors in HCC and may assist clinicians in prognosis prediction and pretreatment decisions.

KEY POINTS

• This study developed and validated three nomograms based on gadoxetic acid-enhanced MRI to predict MVI, tumor differentiation, and immunoscore in patients with HCC. • The pretreatment prediction of tumor microenvironment may be useful to guide accurate prognosis and planning of surgical and immunological therapies for individual patients with HCC.

Preoperative prediction of pathologic grade of HCC on gadobenate dimeglumine-enhanced dynamic MRI

PURPOSE

To evaluate the value of gadobenate dimeglumine-enhanced MRI in predicting the pathologic grade of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Patients with pathologically proven HCC who underwent preoperative gadobenate dimeglumine-enhanced dynamic MRI were included. Two radiologists blinded to pathology results evaluated images in consensus. Lesions were evaluated quantitatively in terms of ratio of enhancement (RE), and qualitatively based on image features related to tumor aggressiveness. Logistic regression and ROC analyses were used to determine the value of these parameters to predict pathologic grade.

RESULTS

In total, 221 patients (194 males, 27 females, aged 52.9 ± 11.7 years) with 49 poorly differentiated HCCs and 172 well/moderately differentiated HCCs were evaluated. Features significantly related to poorer pathologic grade at univariate analysis included lower RE in the early arterial phase (EAP) (p = 0.001), nonsmooth margins (p = 0.001), absence of capsule (p < 0.001), arterial peritumoral hyperenhancement (p < 0.001), higher AFP (p = 0.004), multiple tumors (p = 0.026), and larger tumor size (p = 0.028). At multivariate analysis, lower RE (EAP) (OR = 0.144, p = 0.002), absence of capsule (OR = 0.281, p = 0.004), and arterial peritumoral hyperenhancement (OR = 4.117, p < 0.001) were independent predictive factors for poorer pathologic grade. ROC analysis showed lower RE (EAP) was predictive of poorer pathologic grade (AUC = 0.667). AUC increased to 0.797 when combined with absence of capsule and presence of peritumoral hyperenhancement.

CONCLUSIONS

Lower RE (EAP), absence of capsule, and arterial peritumoral hyperenhancement were predictive biomarkers for poorer pathologic grade of HCC on gadobenate dimeglumine-enhanced dynamic MRI.

KEY POINTS

• Gadobenate dimeglumine-enhanced dynamic MRI was a useful quantitative biomarker for preoperative prediction of pathologic grade in patients with HCC. • Lower RE in the early arterial phase, absence of capsule, and arterial peritumoral hyperenhancement were potential imaging indicators for preoperative prediction of poorer pathologic grade of HCC on gadobenate dimeglumine-enhanced MRI. • A lower RE in the early arterial phase was effective at predicting poorer pathologic grade of HCCs but prediction is improved when combined with absence of capsule and presence of peritumoral hyperenhancement.

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.

Preoperative MR imaging for predicting early recurrence of solitary hepatocellular carcinoma without microvascular invasion

OBJECTIVES

This study aimed to identify preoperative MR imaging features for predicting early recurrence after curative resection of solitary hepatocellular carcinoma (HCC) without microvascular invasion (MVI).

METHODS

124 patients with MVI-negative HCC who underwent preoperative dynamic contrast-enhanced 1.5-T MR imaging before surgical resection were included. Liver Imaging Reporting and Data System (LI-RADS v2018) imaging features and three non-LI-RADS MR imaging features for predicting early recurrence (intrahepatic recurrence<2 years) were identified by univariable and multivariable analyses. A nomogram was constructed for individualized risk estimation, and its predictive accuracy and discriminative ability were identified by concordance index (C-index) and calibration curve.

RESULTS

In multivariable analysis, tumor size (p = 0.045), nonsmooth tumor margin (p = 0.013), and presence of mosaic architecture (p = 0.035) were independent significant variables associated with early recurrence. These were all incorporated to establish the nomogram. The C-index of the nomogram was 0.743 (95 % CI: 0.697-0.788).

CONCLUSION

At dynamic contrast-enhanced MR imaging, tumor size, nonsmooth tumor margin, and presence of mosaic architecture may be helpful to predict early recurrence of solitary HCC without MVI after curative resection.

Development and validation of a nomogram combining hematological and imaging features for preoperative prediction of microvascular invasion in hepatocellular carcinoma patients

Background

Microvascular invasion (MVI) is a significant hazard factor that influences the recurrence and survival of hepatocellular carcinoma (HCC) patients after undergoing hepatectomy. This study aimed to develop and validate a nomogram that combines hematological and imaging features of HCC patients to preoperatively predict MVI, and investigate the effect of wide resection margin (≥1 cm) on the prognosis of MVI-positive HCC patients.

Methods

A total of 709 HCC patients who underwent hepatectomy at the Liver Cancer Institute of Zhongshan Hospital, Fudan University between June 1, 2015 and December 30, 2016 were included in this study and divided into training (496 patients) and validation cohort (213 patients). Least absolute shrinkage and selection operator (Lasso) regression and multivariable logistic regression were used for variables’ selection and development of the predictive model. The model was presented as a nomogram, and its performance was assessed in terms of discrimination, calibration and clinical usefulness.

Results

Independent prognostic factors such as alkaline phosphatase (ALP, >125 U/L), alpha-fetoprotein (AFP, within 20–400 or >400 ng/mL), protein induced by vitamin K absence-II (PVIKA-II, within 40–400 or >400 mAU/mL), tumor number, diameter, pseudo-capsule, tumor growth pattern and intratumor hemorrhage were incorporated in the nomogram. The model showed good discrimination and calibration, with a concordance index (0.82, 95% CI, 0.782–0.857) in the training cohort and C-index (0.80, 95% CI, 0.772–0.837) in the validation cohort. Decision curve analysis (DCA) also showed that this model is clinically useful. Moreover, HCC patients with wide resection margin had a significantly lower 3-year recurrence rate than those with narrower resection margin (0.5–1 cm).

Conclusions

This study presents an optimal model for preoperative prediction of MVI and shows that wide resection margin for MVI-positive HCC patients has a better prognosis. This model can help surgeons choose the best treatment options for HCC patients before and after the operation.

Can LI-RADS imaging features at gadoxetic acid-enhanced MRI predict aggressive features on pathology of single hepatocellular carcinoma?

PURPOSE

To investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features at preoperative gadoxetic acid-enhanced MRI can predict microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) and to evaluate their associations with recurrence after curative resection of single HCC.

MATERIALS AND METHODS

From July 2015 to September 2018, 111 consecutive patients with pathologically confirmed HCC who underwent gadoxetic acid-enhanced MRI within 1 month before surgery were included in this retrospective study. Significant MRI findings and clinical parameters for predicting MVI, high-grade HCCs and postoperative recurrence were identified by logistic regression model and Cox proportional hazards model.

RESULTS

Twenty-six of 111 (23.4 %) patients had MVI and 36 of 111 (32.4 %) patients had high-grade HCCs, whereas 44 of 95 (46.3 %) patients experienced recurrence. Tumor size > 5 cm (OR = 9.852; p < 0.001) and absence of nodule-in-nodule architecture (OR = 8.302; p = 0.001) were independent predictors of MVI. Enhancing capsule (OR = 4.396; p = 0.004) and corona enhancement (OR = 3.765; p = 0.021) were independent predictors of high-grade HCCs. Blood products in mass (HR = 2.275; p = 0.009), corona enhancement (HR = 4.332; p < 0.001), and serum AFP level > 400 ng/mL (HR = 2.071; p = 0.023) were independent predictors of recurrence.

CONCLUSION

LI-RADS imaging features can be used as potential biomarkers for predicting aggressive pathologic features and recurrence of HCC. The identification of prognostic LI-RADS imaging features may facilitate the selection of surgical candidates and optimize the management of HCC patients.

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.

Radiomics in liver diseases: Current progress and future opportunities

Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.

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.

Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma: Radiomics Algorithm Based on Ultrasound Original Radio Frequency Signals

Background: To evaluate the accuracy of radiomics algorithm based on original radio frequency (ORF) signals for prospective prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) lesions.

Methods: In this prospective study, we enrolled 42 inpatients diagnosed with HCC from January 2018 to December 2018. All HCC lesions were proved by surgical resection and histopathology results, including 21 lesions with MVI. Ultrasound ORF data and grayscale ultrasound images of HCC lesions were collected before operation for further radiomics analysis. Three ultrasound feature maps were calculated using signal analysis and processing (SAP) technology in first feature extraction. The diagnostic accuracy of model based on ORF signals was compared with the model based on grayscale ultrasound images.

Results: A total of 1,050 radiomics features were extracted from ORF signals of each HCC lesion. The performance of MVI prediction model based on ORF was better than those based on grayscale ultrasound images. The best area under curve, accuracy, sensitivity, and specificity of ultrasound radiomics in prediction of MVI were 95.01, 92.86, 85.71, and 100%, respectively.

Conclusions: Radiomics algorithm based on ultrasound ORF data combined with SAP technology can effectively predict MVI, which has potential clinical application value for non-invasively preoperative prediction of MVI in HCC patients.

Microvascular invasion in hepatocellular carcinoma: is it predictable with quantitative computed tomography parameters?

Objective

To investigate whether quantitative computed tomography (CT) measurements can predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Materials and Methods

This was a retrospective analysis of 200 cases of surgically proven HCCs in 125 consecutive patients evaluated between March 2010 and November 2017. We quantitatively measured regions of interest in lesions and adjacent areas of the liver on unenhanced CT scans, as well as in the arterial, portal venous, and equilibrium phases on contrast-enhanced CT scans. Enhancement profiles were analyzed and compared with histopathological references of MVI. Univariate and multivariate logistic regression analyses were used in order to evaluate CT parameters as potential predictors of MVI.

Results

Of the 200 HCCs, 77 (38.5%) showed evidence of MVI on histopathological analysis. There was no statistical difference between HCCs with MVI and those without, in terms of the percentage attenuation ratio in the portal venous phase (114.7 vs. 115.8) and equilibrium phase (126.7 vs. 128.2), as well as in terms of the relative washout ratio, also in the portal venous and equilibrium phases (15.0 vs. 8.2 and 31.4 vs. 26.3, respectively).

Conclusion

Quantitative dynamic CT parameters measured in the preoperative period do not appear to correlate with MVI in HCC.

IVIM improves preoperative assessment of microvascular invasion in HCC

PURPOSE

To prospectively evaluate the potential role of intravoxel incoherent motion (IVIM) and conventional radiologic features for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).

METHODS

Institutional review board approval and written informed consent were obtained for this study. A cohort comprising 115 patients with 135 newly diagnosed HCCs between January 2016 and April 2017 were evaluated. Two radiologists independently reviewed the radiologic features and the apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion component fraction (f) were also measured. Interobserver agreement was checked and univariate and multivariate logistic regressions were used for screening the risk factors. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance.

RESULTS

Features significantly related to MVI of HCC at univariate analysis were reduced ADC (odds ratio, 0.341; 95% CI, 0.211-0.552; p < 0.001), D (odds ratio, 0.141; 95% CI, 0.067-0.299; p < 0.001), and irregular circumferential enhancement (odds ratio, 9.908; 95% CI, 3.776-25.996; p < 0.001). At multivariate analysis, only D value (odds ratio, 0.096; 95% CI, 0.025-0.364; p < 0.001) was the independent risk factor for MVI of HCC. The mean D value for MVI of HCC showed an area under ROC curves of 0.815 (95% CI, 0.740-0.877).

CONCLUSION

IVIM model-derived D value is superior to ADC measured with mono-exponential model for evaluating the MVI of HCC. Among MR imaging features, tumor margin, enhancement pattern, tumor capsule, and peritumoral enhancement were not predictive for MVI.

KEY POINTS

• Diffusion MRI is useful for non-invasively evaluating the microvascular invasion of hepatocellular carcinoma. • IVIM model is advantageous over mono-exponential model for assessing the microvascular invasion of hepatocellular carcinoma. • Decreased D value was the independent risk factor for predicting MVI of HCC.

A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma

Background

We aimed to develop and validate a nomogram combining bi-regional radiomics features from multimodal magnetic resonance imaging (MRI) and clinicoradiological characteristics to preoperatively predict microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

Methods

A total of 267 HCC patients were divided into training (n=194) and validation (n=73) cohorts according to MRI data. Bi-regional features were extracted from whole tumors and peritumoral regions in multimodal MRI. The minimum redundancy maximum relevance (mRMR) algorithm was applied to select features and build signatures. The predictive performance of the optimal radiomics signature was further evaluated within subgroups defined by tumor size and alpha fetoprotein (AFP) level. Then, a radiomics nomogram including the optimal radiomics signature, radiographic descriptors, and clinical variables was developed using multivariable regression. The nomogram performance was evaluated based on its discrimination, calibration, and clinical utility.

Results

The fusion radiomics signature derived from triphasic dynamic contrast-enhanced (DCE) MR images can effectively classify MVI and non-MVI HCC patients, with an AUC of 0.784 (95% CI: 0.719-0.840) in the training cohort and 0.820 (95% CI: 0.713-0.900) in the validation cohort. The fusion radiomics signature also performed well in the subgroups defined by the two risk factors, respectively. The nomogram, consisting of the fusion radiomics signature, arterial peritumoral enhancement, and AFP level, outperformed the clinicoradiological prediction model in the validation cohort (AUCs: 0.858 vs. 0.729; P=0.022), fitting well in the calibration curves (P>0.05). Decision curves confirmed the clinical utility of the nomogram.

Conclusions

The radiomics nomogram can serve as a visual predictive tool for MVI in HCCs, and thus assist clinicians in selecting optimal treatment strategies to improve clinical outcomes.

Comparison Between Low Mechanical Index and High Mechanical Index Contrast Modes of Contrast-Enhanced Ultrasonography: Evaluation of Perfusion Defects of Hypervascular Hepatocellular Carcinomas During the Post-Vascular Phase

OBJECTIVES

We evaluated the detection rates for perfusion defects in hypervascular hepatocellular carcinomas comparing the low mechanical index (MI) and high MI contrast modes during the post-vascular phase (PVP) of contrast-enhanced ultrasonography.

METHODS

Seventy-eight patients with 84 hypervascular hepatocellular carcinomas (mean diameter, 23.4 ± 11.2 mm) were selected for this retrospective study. All the patients underwent whole-liver scanning using conventional ultrasonography before injection of a perflubutane-based contrast agent (Sonazoid), and all the detected nodules were classified as either hypoechoic or hyperechoic nodules. Next, hypoechoic and hyperechoic nodules were evaluated using contrast-enhanced ultrasonography, and the presence of a perfusion defect was assessed for each nodule using both the low MI (0.2-0.3) and the high MI (0.7-1.2) contrast modes during the PVP (10 minutes after injection). The data were analyzed using the McNemar test.

RESULTS

Forty-four nodules were classified as hypoechoic nodules, and the remaining 40 nodules were classified as hyperechoic nodules using conventional ultrasonography. The detection rate for perfusion defects determined using the high MI contrast mode was higher than that determined using the low MI contrast mode in hyperechoic nodules during the PVP (low MI, 58% [23 of 40]; high MI, 90% [36 of 40]; P < .0001). However, no significant difference was observed between the low MI and the high MI contrast modes in hypoechoic nodules (low MI, 80% [35 of 44]; high MI, 89% [39 of 44]; P = .125).

CONCLUSION

Compared with the low MI contrast mode, the high MI contrast mode was more sensitive for detecting perfusion defects in hypervascular hepatocellular carcinomas in patients with hyperechoic nodules during the PVP.

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.

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.

Imaging biomarkers for well and moderate hepatocellular carcinoma: preoperative magnetic resonance image and histopathological correlation

Background

Our aim of the study is to investigate the feasibility of preoperative prediction for hepatocellular carcinoma (HCC) histological grading using gadoxetic acid-enhanced magnetic resonance imaging (MRI).

Methods

This study included one hundred and fifty-six patients with solitary HCC. Preoperative gadoxetic acid-enhanced MRI findings were retrospectively analyzed. MRI qualitative features such as tumor size, margin, capsule status, signal homogeneity, intratumoral vessels, peritumoral enhancement during mid-arterial phase, peritumoral hypointensity during the hepatobiliary phase (HBP) were investigated. Apparent diffusion coefficients (ADCs), T1 reduction ratio of pre- and post-contrast enhanced images of the tumors were calculated. HCC histological grading in surgical specimens were confirmed by Edmonson’s criteria. Correlations between these MRI features and HCC histological grading were analyzed using multivariate logistic regression. The receiver operating characteristic (ROC) curve was used to assess the predictive efficacy of the model.

Results

Univariate analysis showed that maximum tumor diameter (p = 0.004), tumor margin (p = 0.006), intratumoral vessels (p = 0.001) and peritumoral hypointensity during HBP (p = 0.000), were significantly correlated with HCC histological grading. There was no relationship between capsule, tumor signal, venous thrombosis, peritumoral enhancement during mid-arterial phase, ADC value, T1 reduction ratio, and HCC histological grading. Multivariate logistic regression analysis demonstrated that the maximum tumor diameter (p = 0.012, odds ratio = 1.002, 95% confidence interval: 1.007–1.046)) was an independent risk factor for high grade HCC.

Conclusions

Greater tumor size, a more irregular margin, presence of intratumoral vessels, and peritumoral hypointensity during HBP were indicators for high grade HCC. The maximum tumor diameter was an independent risk factor for high grade HCC.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5574-8) contains supplementary material, which is available to authorized users.

A wide-margin liver resection improves long-term outcomes for patients with HBV-related hepatocellular carcinoma with microvascular invasion

BACKGROUND

The impact of the resection margin on survival outcomes in patients with hepatocellular carcinoma remains to be determined. This study aimed to examine the association between the width of resection margin and the presence of microvascular invasion in hepatitis B virus-related hepatocellular carcinoma.

METHODS

We reviewed data on 2,508 consecutive patients who underwent liver resection for a solitary, hepatitis B virus-related hepatocellular carcinoma for operative morbidity, tumor recurrence, and overall survival.

RESULTS

Microvascular invasion was identified histologically in 929 patients (37.0%). A wide margin of resection (≥1 cm, n = 384) resulted in better 5-year recurrence and overall survival versus a narrow margin of resection (<1 cm, n = 545) among patients with microvascular invasion (71.1% versus 85.9%; 44.9% versus 25.0%; both P < .001), but not in patients without microvascular invasion (P = .131, .182). Similar results were identified after propensity-score matching. A wide margin resection also had a lesser incidence of early recurrence developed within the first postoperative 24 months (58.1% versus 72.7%; P < .001). Compared with a wide resection margin, a narrow margin was associated with worse recurrence and overall survival in patients with microvascular invasion (hazard ratio: 1.50 and 1.75). In addition, a wide or a narrow resection margin had differences in the rate of grade I-III, but not grade IV complications (31.0% versus 21.7%; P = .017; 3.5% versus 1.6%; P = .147) among cirrhotic patients with microvascular invasion.

CONCLUSION

The presence of microvascular invasion was associated with a worse prognosis after resection. A wide resection margin resulted in better long-term prognoses versus a narrow resection margin among patients with hepatitis B virus-related hepatocellular carcinoma with microvascular invasion.

A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma

PURPOSE

We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).

METHODS

A total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and selection operator algorithm, the training set was processed to reduce data dimensions, feature selection, and construction of a radiomics signature. Then, a prediction model including the radiomics signature, radiologic features, and alpha-fetoprotein (AFP) level, as presented in a radiomics nomogram, was developed using multivariable logistic regression analysis. The radiomics nomogram was analyzed based on its discrimination ability, calibration, and clinical usefulness. Internal cohort data were validated using the radiomics nomogram.

RESULTS

The radiomics signature was significantly associated with MVI status (P < 0.001, both cohorts). Predictors, including the radiomics signature, nonsmooth tumor margin, hypoattenuating halos, internal arteries, and alpha-fetoprotein level were reserved in the individualized prediction nomogram. The model exhibited good calibration and discrimination in the training and validation cohorts (C-index [95% confidence interval]: 0.846 [0.787-0.905] and 0.844 [0.774-0.915], respectively). Its clinical usefulness was confirmed using a decision curve analysis.

CONCLUSION

The radiomics nomogram, as a noninvasive preoperative prediction method, shows a favorable predictive accuracy for MVI status in patients with HBV-related HCC.

ADC similarity predicts microvascular invasion of bifocal hepatocellular carcinoma

PURPOSE

This study aimed to investigate whether ADC similarity can predict microvascular invasion (MVI) in patients with bifocal hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Between January 2015 and September 2015, 51 patients with two HCC lesions were included. All patients underwent conventional magnetic resonance imaging including diffusion-weighted imaging (DWI) before the HCC lesions were surgically resected; the tumor specimens were examined histopathologically. Similarity between two HCC lesions regarding DWI signal intensity (SI) and ADC value was calculated as the difference between the two lesions: Value Similarity = [1-(|value_{large lesion}-value_{small lesion}|)/(value_{large lesion} + value_{small lesion})] × 100%. Univariate and multivariate logistic regression analyses were performed to assess the presence of MVI.

RESULTS

Risk factors significantly related to MVI of bifocal HCC in univariate analysis were cirrhosis (P = 0.010), histological grade (P = 0.040), DWI SI similarity (P = 0.027) and ADC similarity (P = 0.003). In multivariate analysis, cirrhosis (odds ratio 0.068, P = 0.022) and ADC similarity (odds ratio 1.204, P = 0.008) were independent risk factors for MVI of bifocal HCC.

CONCLUSION

In patients with two HCC lesions, highly similar ADC values for the two HCC lesions may be a preoperative predictor of MVI.

Can microvascular invasion in hepatocellular carcinoma be predicted by diagnostic imaging? A critical review

Imaging still has a limited capacity to detect microvascular invasion (mVI). The objective of this critical review is the evaluation of the most significant predictors of mVI in hepatocellular carcinoma (HCC) detectable by computed tomography, PET/computed tomography and MRI using a mathematical model. We systematically reviewed 15 observational studies from 2008 to 2018 to analyze factors with most impact on mVI detection. The most significant predictors of mVI correlating with imaging techniques were considered. From 1902 patients considered, we individuated 30 total predictors of mVI in a multivariate analysis. The most frequent predictors related to the highest presence with mVI in HCC were: α-fetoprotein (p < 0.0001), tumor size (p < 0.0001) and number of HCC nodules (p = 0.0020).

Image quality of arterial phase and parenchymal blood volume (PBV) maps derived from C-arm computed tomography in the evaluation of transarterial chemoembolization

Background

To evaluate the benefits of arterial phase imaging and parenchymal blood volume (PBV) maps acquired by C-arm computed tomography during TACE procedure in comparison to cross-sectional imaging (CSI) using CT or MRI.

Methods

From January 2014 to December 2016, a total of 29 patients with HCC stage A or B (mean age 65 years; range 47 to 81 years, 86% male) were included in this study. These patients were referred to our department for TACE treatment and received peri-interventional C-arm CT. Dual phase findings of each lesion in terms of overall image quality, conspicuity, tumor size and feeding arteries were compared between arterial phase imaging and PBV using 5-point semi-quantitative Likert-scale, whereby pre-interventional CSI served as reference standard.

Results

A significantly higher overall image quality of the PBV maps compared to arterial phase C-arm CT acquisitions (4.34 (±0.55) vs. 3.93 (±0.59), p = 0.0032) as well as a higher conspicuity of HCC lesions (4.27 ± 0.74 vs. 3.83 ± 1.08, p < 0.0001) was observed. Arterial phase imaging led to an overestimation of tumor size (mean size, 26.5 ± 15.9 mm) compared to PBV (24.9 ± 15.2 mm, p = 0.0004) as well as CSI (25.2 ± 15.1 mm), p = 0.021). Regarding detectability of tumor feeding arterial vessels, significantly more feeding vessels were detected in arterial phase C-arm CT (n = 1.67 ± 0.92 vessels) compared to PBV maps (n = 1.27 ± 0.63 vessels) (p = 0.0001). One lesion was missed in pre-interventional CT imaging, but detected by C-arm CT.

Conclusion

The combination of PBV maps and arterial phase images acquired by C-arm CT during TACE procedure enables precise detection of the majority of HCC lesions and tumor feeding arteries and has therefore the potential to improve patient outcome.

A non-smooth tumor margin on preoperative imaging assesses microvascular invasion of hepatocellular carcinoma: A systematic review and meta-analysis

Microvascular invasion (MVI) is rarely diagnosed preoperatively in hepatocellular carcinoma (HCC). The aim of this meta-analysis is to assess the diagnostic power of a non-smooth tumor margin on preoperative imaging for MVI. We performed a literature search using the PubMed, Embase and Cochrane Library databases, and 11 studies were included involving 618 MVI-positive cases and 1030 MVI-negative cases. Considerable heterogeneity was found, and was indicated to be attributable to the mean patient ages in the included studies. In subgroups of studies with a mean patient age older than 60 years and studies with computed tomography (CT) as the imaging method (as opposed to magnetic resonance imaging (MRI)), heterogeneity was low, and the diagnostic odds ratio (DOR) of the single two-dimensional imaging feature for MVI was 21.30 (95% CI [12.52, 36.23]) and 28.78 (95% CI [13.92, 59.36]), respectively; this power was equivalent to or greater than that of certain multivariable-based scoring systems. In conclusion, a non-smooth tumor margin on preoperative imaging is of great value for MVI assessment and should be considered for inclusion in future scoring systems.

Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results

OBJECTIVES

In this preliminary study, we examined whether imaging-based phenotypes are associated with reported predictive gene signatures in hepatocellular carcinoma (HCC).

METHODS

Thirty-eight patients (M/F 30/8, mean age 61 years) who underwent pre-operative CT or MR imaging before surgery as well as transcriptome profiling were included in this IRB-approved single-centre retrospective study. Eleven qualitative and four quantitative imaging traits (size, enhancement ratios, wash-out ratio, tumour-to-liver contrast ratios) were assessed by three observers and were correlated with 13 previously reported HCC gene signatures using logistic regression analysis.

RESULTS

Thirty-nine HCC tumours (mean size 5.7 ± 3.2 cm) were assessed. Significant positive associations were observed between certain imaging traits and gene signatures of aggressive HCC phenotype (G3-Boyault, Proliferation-Chiang profiles, CK19-Villanueva, S1/S2-Hoshida) with odds ratios ranging from 4.44-12.73 (P <0.045). Infiltrative pattern at imaging was significantly associated with signatures of microvascular invasion and aggressive phenotype. Significant but weak associations were also observed between each enhancement ratio and tumour-to-liver contrast ratios and certain gene expression profiles.

CONCLUSIONS

This preliminary study demonstrates a correlation between phenotypic imaging traits with gene signatures of aggressive HCC, which warrants further prospective validation to establish imaging-based surrogate markers of molecular phenotypes in HCC.

KEY POINTS

• There are associations between imaging and gene signatures of aggressive hepatocellular carcinoma. • Infiltrative type is associated with gene signatures of microvascular invasion and aggressiveness. • Infiltrative type may be a surrogate marker of microvascular invasion gene signature.

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

BACKGROUND & AIMS

This study aimed to identify preoperative magnetic resonance (MR) imaging biomarkers for predicting microvascular invasion (MVI), to determine their diagnostic performance and to evaluate whether they are associated with early recurrence after surgery for single hepatocellular carcinoma (HCC).

METHODS

The study included 197 patients with surgically resected HCC (≤5cm) who underwent preoperative gadoxetic acid-enhanced MR imaging. Significant MR imaging findings for predicting MVI were identified by univariate and multivariate analyses. Early recurrence rates (<2years) were analyzed with respect to significant imaging findings for predicting MVI.

RESULTS

Three MR imaging features were independently associated with MVI: arterial peritumoral enhancement (odds ratio [OR]=5.184; 95% confidence interval [CI]: 2.228, 12.063; p<0.001), non-smooth tumor margin (OR=3.555; 95% CI: 1.627, 7.769; p=0.001), and peritumoral hypointensity on hepatobiliary phase (HBP) (OR=4.705; 95% CI: 1.671, 13.246; p=0.003). When two of three findings were combined, the specificity was 92.5% (124/134). When all three findings were satisfied, the specificity was 99.3% (133/134). Early recurrence rates were significantly higher in patients with single HCC, with two or three significant MR imaging findings, compared to those with none (27.9% vs. 12.6%, respectively, p=0.030).

CONCLUSIONS

A combination of two or more of the following; arterial peritumoral enhancement, non-smooth tumor margin, and peritumoral hypointensity on HBP, can be used as a preoperative imaging biomarker for predicting MVI, with specificity >90%, and is associated with early recurrence after surgery of single HCC. Lay summary: A combination of two or more of the following; arterial peritumoral enhancement, non-smooth tumor margin, and peritumoral hypointensity on hepatobiliary phase, can be used as a preoperative imaging biomarker for predicting microvascular invasion, with specificity >90%, and is associated with early recurrence after curative resection of single HCC.

Can the Contrast-Enhanced Ultrasound Washout Rate Be Used to Predict Microvascular Invasion in Hepatocellular Carcinoma?

The objective of this study was to investigate use of the washout rate of hepatocellular carcinoma on contrast-enhanced ultrasound (CEUS) for pre-operative determination of the presence of microvascular invasion. The study included 271 patients who underwent liver resection for hepatocellular carcinoma between April 2008 and December 2012, and were examined with contrast-enhanced ultrasound before surgery. Patients were followed up at 3-mo intervals for 3 y. Four washout patterns were classified according to the start time of washout: rapid, portal, delayed and slow. Rapid washout, presence of two or more tumors and tumor size ≥5 cm were identified as independent pre-operative predictors of microvascular invasion on multivariate analysis. Recurrence rates for patients with none, one, two or three predictors were 22.6%, 34.7%, 57.6% and 75.0%, respectively. In combination with tumor number and tumor size, contrast-enhanced ultrasound washout rate may have a role in identifying hepatocellular carcinoma patients with microvascular invasion.

Vascular microinvasion from hepatocellular carcinoma: CT findings and pathologic correlation for the best therapeutic strategies

Recurrence of HCC reduces survival rates in patients treated with surgery, and one of the most relevant risk factors for tumour recurrence is microvascular invasion (mVI). The identification of mVI on preoperative examinations could improve surgical planning's and techniques so as to reduce the risk of tumour recurrence. During our study, we have revised 101 CT examinations of the liver performed on patients diagnosed with solitary HCC who had surgical treatment and pathological analysis of the specimens for mVI in order to detect CT signs which could be reliable in mVI prediction. On CT examinations, the tumours were evaluated for margins, capsule, size, contrast enhancement, halo sign and Thad. From our statistical analysis, we found out that irregularity in tumour margins and defects in peritumoural capsule are the most significant characteristics predicting mVI in HCC. Every report on CT examinations performed on surgical candidate patients should include suggestions about mVI probability in order to tailor procedures, reduce tumour recurrence risk and improve survival rates.

Microvascular invasion in hepatocellular carcinoma

Microvascular invasion is a crucial histopathologic prognostic factor for hepatocellular carcinoma. We reviewed the literature and aimed to draw attention to clinicopathologic and imaging findings that may predict the presence of microvascular invasion in hepatocellular carcinoma. Imaging findings suggesting microvascular invasion are disruption of capsule, irregular tumor margin, peritumoral enhancement, multifocal tumor, increased tumor size, and increased glucose metabolism on positron emission tomography-computed tomography. In the presence of typical findings, microvascular invasion may be predicted.