Precise localization of microvascular invasion in hepatocellular carcinoma based on three-dimensional histology-MR image fusion: an ex vivo experimental study

Background

Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). However, MVI cannot be detected by conventional imaging. To localize MVI precisely on magnetic resonance (MR) images, we evaluated the feasibility and accuracy of 3-dimensional (3D) histology-MR image fusion of the liver.

Methods

Animal models of VX2 liver tumors were established in 10 New Zealand white rabbits under ultrasonographic guidance. The whole liver lobe containing the VX2 tumor was extracted and divided into 4 specimens, for a total of 40 specimens. MR images were obtained with a T2-weighted sequence for each specimen, and then histological images were obtained by intermittent, serial pathological sections. 3D histology-MR image fusion was performed via landmark registration in 3D Slicer software. We calculated the success rate and registration errors of image fusion, and then we located the MVI on MR images. Regarding influencing factors, we evaluated the uniformity of tissue thickness after sampling and the uniformity of tissue shrinkage after dehydration.

Results

The VX2 liver tumor model was successfully established in the 10 rabbits. The incidence of MVI was 80% (8/10). 3D histology-MR image fusion was successfully performed in the 39 specimens, and the success rate was 97.5% (39/40). The average registration error was 0.44±0.15 mm. MVI was detected in 20 of the 39 successfully registered specimens, resulting in a total of 166 MVI lesions. The specific location of all MVI lesions was accurately identified on MR images using 3D histology-MR image fusion. All MVI lesions showed as slightly hyperintense on the high-resolution MR T2-weighted images. The results of the influencing factor assessment showed that the tissue thickness was uniform after sampling (P=0.38), but the rates of the tissue shrinkage was inconsistent after dehydration (P<0.001).

Conclusions

3D histology-MR image fusion of the isolated liver tumor model is feasible and accurate and allows for the successful identification of the specific location of MVI on MR images.

Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) with microvascular invasion (MVI) has a poor prognosis, is prone to recurrence and metastasis, and requires more complex surgical techniques. Radiomics is expected to enhance the discriminative performance for identifying HCC, but the current radiomics models are becoming increasingly complex, tedious, and difficult to integrate into clinical practice. The purpose of this study was to investigate whether a simple prediction model using noncontrast-enhanced T2-weighted magnetic resonance imaging (MRI) could preoperatively predict MVI in HCC.

Methods

A total of 104 patients with pathologically confirmed HCC (training cohort, n=72; test cohort, n=32; ratio, about 7:3) who underwent liver MRI within 2 months prior to surgery were retrospectively included. A total of 851 tumor-specific radiomic features were extracted on T2-weighted imaging (T2WI) for each patient using AK software (Artificial Intelligence Kit Version; V. 3.2.0R, GE Healthcare). Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used in the training cohort for feature selection. The selected features were incorporated into a multivariate logistic regression model to predict MVI, which was validated in the test cohort. The model's effectiveness was evaluated using the receiver operating characteristic and calibration curves in the test cohort.

Results

Eight radiomic features were identified to establish a prediction model. In the training cohort, the area under the curve, accuracy, specificity, sensitivity, and positive and negative predictive values of the model for predicting MVI were 0.867, 72.7%, 84.2%, 64.7%, 72.7%, and 78.6%, respectively; while in the test cohort, they were 0.820, 75%, 70.6%, 73.3%, 75%, and 68.8%, respectively. The calibration curves displayed good consistency between the prediction of MVI by the model and actual pathological results in both the training and validation cohorts.

Conclusions

A prediction model using radiomic features from single T2WI can predict MVI in HCC. This model has the potential to be a simple and fast method to provide objective information for decision-making during clinical treatment.

A diagnostic test of three-dimensional magnetic resonance elastography imaging for preoperative prediction of microvascular invasion in patients with T1 stage clear cell renal carcinoma

Background

Detection of microvascular invasion (MVI) of kidney tumors is important for selecting the optimal therapeutic strategy. Currently, the prediction of MVI lacks an accurate imaging biomarker. This study evaluated the performance of three-dimensional (3D) magnetic resonance elastography (MRE) imaging in predicting microvascular invasion (MVI) of T1 stage clear cell renal carcinoma (ccRCC).

Methods

In this prospective study, we conducted pre-surgical imaging with a clinical 3.0 T magnetic resonance imaging (MRI) system. Firstly, 83 consecutive patients were enrolled in this study. A 3D MRE stiffness map was generated and transferred to a post-processing workstation. Contrast-enhanced computed tomography (CT) was conducted to calculate the tumor enhancement ratio. The presence of MVI was evaluated by histopathological analysis and graded according to the risk stratification based upon the number and distribution. The mean stiffness and CT tumor enhancement ratio was calculated for tumors with or without MVI. The diagnostic performance [sensitivity, specificity, positive predictive value, negative predictive value, area under the curve (AUC)] and independent predicting factors for MVI were investigated.

Results

Finally, A total of 80 patients (aged 46.7±13.2 years) were enrolled, including 22 cases of tumors with MVI. The mean MRE stiffness of kidney parenchyma and kidney tumors was 4.8±0.2 and 4.5±0.7 kPa, respectively. There was significant difference in the mean MRE stiffness between tumors with MVI (5.4±0.6 kPa) and tumors without MVI (4.1±0.3 kPa) (P<0.05). The sensitivity, specificity, positive predictive value, negative predictive value, and the AUC for mean stiffness in the prediction of MVI were 100%, 75%, 63%, 96%, and 0.87 [95% confidence interval (CI): 0.72, 0.94], respectively. The corresponding values for the CT tumor enhancement ratio were 90%, 80%, 63%, 96%, and 0.88 (95% CI: 0.71, 0.93), respectively. The odds ratio (OR) value for MRE tumor stiffness and CT kidney tumor enhancement ratio in the prediction of MVI was 2.9 (95% CI: 1.8, 3.7) and 1.2 (95% CI: 1.0, 1.7), respectively (P>0.05).

Conclusions

3D MRE imaging has promising diagnostic performance for predicting MVI in T1 stage ccRCC, which may improve the reliability of surgical strategy selection with T1 stage ccRCC.

A reasonable identification of the early recurrence time based on microvascular invasion for hepatocellular carcinoma after R0 resection: A multicenter retrospective study

BACKGROUND

Early and late recurrence of hepatocellular carcinoma (HCC) have different clinical outcomes, especially for those accompanied by microvascular invasion (MVI), but the definition of early recurrence remains controversial. Therefore, a reasonable identification of the early recurrence time for HCC is urgently needed.

METHODS

Resected recurrence patients were enrolled and divided into two cohorts, one for identification of the early recurrence time and another for verification of the accuracy of the point. Univariable and multivariable Cox regression analyses were adopted to identify the prognostic factors of recurrence HCC (rHCC) and Kaplan-Meier method was applied to analyze the overall survival (OS). The appropriate cutoff value was determined by the exhaustive method using different recurrence intervals from 1 to 24 months in turn.

RESULTS

In total, 292 resected rHCC patients were analyzed to calculate the early recurrence interval, and another 421 resected rHCC patients with MVI were enrolled to verify the efficacy of adjuvant transarterial chemoembolization (TACE) in this recurrence interval. MVI was identified as an independent risk factor by multivariable analysis. The OS of rHCC patients without MVI is better than that of patients with MVI when the recurrence time was within 13 months, while not beyond 13 months. The verification cohort demonstrated that adjuvant TACE provided longer survival for rHCC with MVI when the recurrence time was within 13 months, while not beyond 13 months.

CONCLUSION

For HCC patients with MVI who underwent R0 resection, 13 months may be a reasonable early recurrence time point, and within this interval, postoperative adjuvant TACE may result in longer survival compared with surgery alone.

18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Background

Microvascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI.

Methods

In this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20). Results-driven feature engineering workflow was used to select the most predictive feature combinations. The prediction model was based on supervised machine learning classifier. Ten-fold cross validation on training cohort and independent test cohort were constructed to ensure stability and generalization ability of models.

Results

For HCC and ICC classification, radiomics predictors composed of two PET and one CT feature achieved area under the curve (AUC) of 0.86 (accuracy, sensitivity, specificity was 0.82, 0.78, 0.88, respectively) on test cohort. For MVI prediction, in HCC group, our MVI prediction model achieved AUC of 0.88 (accuracy, sensitivity, specificity was 0.78, 0.88, 0.60 respectively) with three PET features associated with tumor stage on test cohort. In ICC group, the phenotype composed of two PET features and carbohydrate antigen 19-9 (CA19-9) achieved AUC of 0.90 (accuracy, sensitivity, specificity was 0.77, 0.75, 0.80, respectively).

Conclusions

¹⁸F-FDG PET/CT radiomic features integrating clinical factors have potential in HCC and ICC classification and MVI prediction, while PET features have dominant predictive power in model performance. The prediction model has value in providing a non-invasive biomarker for an earlier indication and comprehensive quantification of primary liver cancers.

Impact of pathological response after preoperative transcatheter arterial chemoembolization (TACE) on incidences of microvascular invasion and early tumor recurrence in hepatocellular carcinoma: a multicenter propensity score matching analysis

Background

To study the influence of pathological responses (PR) after transcatheter arterial chemoembolization (TACE) on incidences of microvascular invasion (MVI) and early recurrence in hepatocellular carcinoma (HCC) patients.

Methods

Between 2013 to 2015, consecutive HCC patients who underwent liver resection with "curative" intent at three hospitals were enrolled in this study. Patients with different areas of PR after preoperative TACE were compared with those without preoperative TACE on the incidences of MVI, early recurrence rates and patterns of recurrence before and after propensity score matching (PSM).

Results

Of 1,970 patients, 737 patients who received preoperative TACE were divided into three groups according to the areas of PR: ≥90% (n=226), 60-90% (n=447), and <60% (n=64). PR ≥90% was an independent protective factor of incidences of MVI [odds ratio (OR), 0.144; 95% confidence interval (CI), 0.082-0.245, P<0.001) and early recurrence (HR, 0.742; 95% CI, 0.561-0.963, P=0.032); while PR<60% was an independent risk factor of incidences of MVI (OR, 6.076; 95% CI, 3.004-11.728, P<0.001) and early recurrence (HR, 1.428; 95% CI, 1.095-1.929; P=0.009). Furthermore, patients with PR <60% were significantly more likely to develop multiple intrahepatic recurrences involving multiple hepatic segments when compared with patients without preoperative TACE.

Conclusions

This study indicated the area of PR after TACE was closely associated with the incidences of MVI and early tumor recurrence. Patients with PR <60% were at significantly higher risks of having more MVI, early and multiple tumor recurrences.

New Insights Into a Classification-Based Microvascular Invasion Prediction Model in Hepatocellular Carcinoma: A Multicenter Study

Background and Aims

Most microvascular invasion (MVI)-predicting models have not considered MVI classification, and thus do not reflect true MVI effects on prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel MVI-predicting model focused on MVI classification, hoping to provide useful information for clinical treatment strategy decision-making.

Methods

A retrospective study was conducted with data from two Chinese medical centers for 800 consecutive patients with HCC (derivation cohort) and 250 matched patients (external validation cohort). MVI-associated variables were identified by ordinal logistic regression. Predictive models were constructed based on multivariate analysis results and validated internally and externally. The models' discriminative ability and calibration ability were examined.

Results

Four factors associated independently with MVI: tumor diameter, tumor number, serum lactate dehydrogenase (LDH) ≥ 176.58 U/L, and γ-glutamyl transpeptidase (γ-GGT). Area under the curve (AUC)s for our M2, M1, and M0 nomograms were 0.864, 0.648, and 0.782. Internal validation of all three models was confirmed with AUC analyses in D-sets (development datasets) and V-sets (validation datasets) and C-indices for each cohort. GiViTI calibration belt plots and Hosmer-Lemeshow (HL) chi-squared calibration values demonstrated good consistency between observed frequencies and predicted probabilities for the M2 and M0 nomograms. Although the M1 nomogram was well calibrated, its discrimination was poor.

Conclusion

We developed and validated MVI prediction models in patients with HCC that differentiate MVI classification and may provide useful guidance for treatment planning.

Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Background

The presence of microvascular invasion (MVI) is considered an independent prognostic factor associated with early recurrence and poor survival in hepatocellular carcinoma (HCC) patients after resection. Artificial intelligence (AI), mainly consisting of non-deep learning algorithms (NDLAs) and deep learning algorithms (DLAs), has been widely used for MVI prediction in medical imaging.

Aim

To assess the diagnostic accuracy of AI algorithms for non-invasive, preoperative prediction of MVI based on imaging data.

Methods

Original studies reporting AI algorithms for non-invasive, preoperative prediction of MVI based on quantitative imaging data were identified in the databases PubMed, Embase, and Web of Science. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) scale. The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated using a random-effects model with 95% CIs. A summary receiver operating characteristic curve and the area under the curve (AUC) were generated to assess the diagnostic accuracy of the deep learning and non-deep learning models. In the non-deep learning group, we further performed meta-regression and subgroup analyses to identify the source of heterogeneity.

Results

Data from 16 included studies with 4,759 cases were available for meta-analysis. Four studies on deep learning models, 12 studies on non-deep learning models, and two studies compared the efficiency of the two types. For predictive performance of deep learning models, the pooled sensitivity, specificity, PLR, NLR, and AUC values were 0.84 [0.75–0.90], 0.84 [0.77–0.89], 5.14 [3.53–7.48], 0.2 [0.12–0.31], and 0.90 [0.87–0.93]; and for non-deep learning models, they were 0.77 [0.71–0.82], 0.77 [0.73–0.80], 3.30 [2.83–3.84], 0.30 [0.24–0.38], and 0.82 [0.79–0.85], respectively. Subgroup analyses showed a significant difference between the single tumor subgroup and the multiple tumor subgroup in the pooled sensitivity, NLR, and AUC.

Conclusion

This meta-analysis demonstrates the high diagnostic accuracy of non-deep learning and deep learning methods for MVI status prediction and their promising potential for clinical decision-making. Deep learning models perform better than non-deep learning models in terms of the accuracy of MVI prediction, methodology, and cost-effectiveness.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php? RecordID=260891, ID:CRD42021260891.

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma based on kupffer phase radiomic features of sonazoid contrast-enhanced ultrasound (SCEUS): A prospective study

OBJECTIVES

To establish and evaluate a machine learning radiomics model based on grayscale and Sonazoid contrast enhanced ultrasound images for the preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.

METHODS

100 cases of histopathological confirmed HCC lesions were prospectively included. Regions of interest were segmented on both grayscale and Kupffer phase of Sonazoid contrast enhanced (CEUS) images. Radiomic features were extracted from tumor region and region containing 5 mm of peritumoral liver tissues. Maximum relevance minimum redundancy (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) were used for feature selection and Support Vector Machine (SVM) classifier was trained for radiomic signature calculation. Radiomic signatures were incorporated with clinical variables using univariate-multivariate logistic regression for the final prediction of MVI. Receiver operating characteristic curves, calibration curves and decision curve analysis were used to evaluate model's predictive performance of MVI.

RESULTS

Age were the only clinical variable significantly associated with MVI. Radiomic signature derived from Kupffer phase images of peritumoral liver tissues (kupfferPT) displayed a significantly better performance with an area under the receiver operating characteristic curve (AUROC) of 0.800 (95% confidence interval: 0.667, 0.834), the final prediction model using Age and kupfferPT achieved an AUROC of 0.804 (95% CI: 0.723, 0.878), accuracy of 75.0%, sensitivity of 87.5% and specificity of 69.1%.

CONCLUSIONS

Radiomic model based on Kupffer phase ultrasound images of tissue adjacent to HCC lesions showed an observable better predictive value compared to grayscale images and has potential value to facilitate preoperative identification of HCC patients at higher risk of MVI.

A multidimensional nomogram combining imaging features and clinical factors to predict the invasiveness and metastasis of combined hepatocellular cholangiocarcinoma

Background

Combined hepatocellular cholangiocarcinoma (CHCC-CCA) is a rare type of primary liver cancer having aggressive behavior. Few studies have investigated the prognostic factors of CHCC-CCA. Therefore, this study aimed to establish a nomogram to evaluate the risk of microvascular invasion (MVI) and the presence of satellite nodules and lymph node metastasis (LNM), which are associated with prognosis.

Methods

One hundred and seventy-one patients pathologically diagnosed with CHCC-CCA were divided into a training set (n=116) and validation set (n=55). Logistic regression analysis was used to assess the relative value of clinical factors associated with the presence of MVI and satellite nodules. The least absolute shrinkage and selection operator (LASSO) algorithm was used to establish the imaging model of all outcomes, and to build clinical model of LNM. Nomograms were constructed by incorporating clinical risk factors and imaging features. The model performance was evaluated on the training and validation sets to determine its discrimination ability, calibration, and clinical utility. Kaplan Meier analysis and time dependent receiver operating characteristic (ROC) were displayed to evaluate the prognosis value of the predicted nomograms of MVI and satellite nodule.

Results

A nomogram comprising the platelet to lymphocyte ratio (PLR), albumin-to-alkaline phosphatase ratio (AAPR) and imaging model was established for the prediction of MVI. Carcinoembryonic antigen (CEA) level and size were combined with the imaging model to establish a nomogram for the prediction of the presence of satellite nodules. Favorable calibration and discrimination were observed in the training and validation sets for the MVI nomogram (C-indexes of 0.857 and 0.795), the nomogram for predicting satellite nodules (C-indexes of 0.919 and 0.883) and the LNM nomogram (C-indexes of 0.872 and 0.666). Decision curve analysis (DCA) further confirmed the clinical utility of the nomograms. The preoperatively predicted MVI and satellite nodules by the combined nomograms achieved satisfactory performance in recurrence-free survival (RFS) and overall survival (OS) prediction.

Conclusions

The proposed nomograms incorporating clinical risk factors and imaging features achieved satisfactory performance for individualized preoperative predictions of MVI, the presence of satellite nodules, and LNM. The prediction models were demonstrated to be good indicator for predicting the prognosis of CHCC-CCA, facilitating treatment strategy optimization for patients with CHCC-CCA.

The impact of liver fibrosis on microvascular invasion and prognosis of hepatocellular carcinoma with a solitary nodule: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Background

The pathogenesis of non-cirrhotic hepatocellular carcinoma (HCC) with a high recurrence remains controversial, while microvascular invasion (MVI) is highly suggestive of tumor recurrence. This study aimed to investigate the effects of liver fibrosis on MVI and prognosis in HCC.

Methods

Based on the data of HCC in the Surveillance, Epidemiology, and End Results (SEER) database [2004–2015], multivariate logistic regression was used for correlation analysis. Survival was analyzed by Log-Rank test and Cox regression, and decision curve analysis and receiver operating characteristic curves were established to evaluate alternative diagnostic and prognostic strategies.

Results

The study included 1,492 patients with MVI (17.8%) or without MVI (82.2%) for HCC with a solitary nodule. Liver fibrosis was significantly correlated with the occurrence of MVI, and the risk of MVI in patients with a fibrosis score F5–6 was lower than in those with a score of F0–4 (OR =0.651, 95% CI: 0.492–0.860). Combining liver fibrosis could improve the prediction performance of MVI risk models, but liver fibrosis was less associated with survival outcomes in comparison with other tumor characteristics.

Conclusions

Lower liver fibrosis correlated with a higher risk of MVI in HCC with a solitary nodule and was a good indicator for improving the performance of MVI risk models. However, it was not a prognostic sensitive indicator.

Prognostic and predictive significance of the tumor microenvironment in hepatocellular carcinoma

BACKGROUND

Identification of molecular markers that reflect the characteristics of the tumor microenvironment (TME) may be beneficial to predict the prognosis of post-operative hepatocellular carcinoma (HCC) patients.

OBJECTIVE AND METHODS

A total of 100 tissue samples from HCC patients were separately stained by immunohistochemistry to examine the expression levels of CD56, CD8α, CD68, FoxP3, CD31 and pan-Keratin. The prognostic values were analyzed by Cox regression and the Kaplan-Meier method.

RESULTS

Univariate and multivariate logistic analysis showed that FoxP3 was the independent factor associated with microvascular invasion (MVI), tumor size and envelop invasion; CD68 was associated with envelope invasion and AFP. Kaplan-Meier survival curves revealed that CD68 and FoxP3 expression were significantly associated with relapse free survival (RFS) of HCC patients (P< 0.05). The ROC curve indicated that the combination of tumor number, MVI present and CD68 expression yielded a ROC curve area of 82.3% (86.36% specificity, 68.75% sensitivity) to evaluate the prognosis of HCC patients, which was higher than the classifier established by the combination of tumor number and MVI (78.8% probability, 63.64% specificity and 85.42% sensitivity).

CONCLUSIONS

Our study indicated that CD68 and FoxP3 are associated with prognosis of HCC patients, and CD68 can be considered as a potential prognostic and predictive biomarker.

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.

Potential application of dynamic contrast enhanced ultrasound in predicting microvascular invasion of hepatocellular carcinoma

OBJECTIVE

To investigate the clinical value of dynamic contrast enhanced ultrasound (D-CEUS) in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

PATIENTS AND METHODS

In this retrospective study, 16 patients with surgery and histopathologically proved HCC lesions were included. Patients were classified according to the presence of MVI: MVI positive group (n = 6) and MVI negative group (n = 10). Contrast enhanced ultrasound (CEUS) examinations were performed within a week before surgery. Dynamic analysis was performed by VueBox® software (Bracco, Italy). Three regions of interests (ROIs) were set in the center of HCC lesions, at the margin of HCC lesions and in the surrounding liver parenchyma accordingly. Time intensity curves (TICs) were generated and quantitative perfusion parameters including WiR (wash-in rate), WoR (wash-out rate), WiAUC (wash-in area under the curve), WoAUC (wash-out area under the curve) and WiPi (wash-in perfusion index) were obtained and analyzed.

RESULTS

All of HCC lesions showed arterial hyperenhancement (100 %) and at the late phase as hypoenhancement (75%) in CEUS. Among all CEUS quantitative parameters, the WiAUC and WoAUC were higher in MVI positive group than in MVI negative group in the center HCC lesions (P < 0.05), WiAUC, WoAUC and WiPI were higher in MVI positive group than in MVI negative group at the margin of HCC lesions. WiR and WoR were significant higher in MVI positive group.

CONCLUSIONS

D-CEUS with quantitative perfusion analysis has potential clinical value in predicting the existence of MVI in HCC lesions.

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.

Postoperative adjuvant transarterial chemoembolization for intrahepatic cholangiocarcinoma patients with microvascular invasion: a propensity score analysis

Background

Microvascular invasion (MVI) is an independent risk factor associated with tumor recurrence and poor survival in patients with intrahepatic cholangiocarcinoma (ICC) after partial hepatectomy (PH). The potential impact of adjuvant TACE on the prognosis of patients with ICC involving MVI (ICC-MVI) remains uncertain. Our aim was to investigate the effectiveness of postoperative adjuvant transarterial chemoembolization (TACE) on ICC involving MVI.

Methods

Multicentric data consisted of 223 patients who underwent curative-intent PH for ICC-MVI from 2002-2015 were retrospectively analyzed. The impact of adjuvant TACE was evaluated using inverse probability of treatment weighting (IPTW) and propensity-score matched (PSM) analyses.

Results

No association between the TACE and the overall survival (OS) and recurrence rates was observed among the overall ICC-MVI patients. However, subgroup analyses revealed that adjuvant TACE favored OS (HR, 0.62; 95% CI, 0.39-0.99; P=0.047) and time to recurrence (TTR) (HR, 0.59; 95% CI, 0.36-0.97; P=0.037) among patients with elevated CA19-9 and those without lymphadenectomy (HR, 0.53; 95% CI, 0.30-0.93; P=0.027 for OS, and HR, 0.49; 95% CI, 0.28-0.87; P=0.015 for TTR, respectively). In the CA19-9 ≥39 U/L subgroup and Nx subgroup, adjuvant TACE was associated with higher 1-, 3-, and 5-year OS rates (P=0.033 and P=0.034, respectively) and lower corresponding recurrence rates (P=0.024 and P=0.023, respectively).

Conclusions

Among the ICC-MVI patients undergoing curative-intent PH, only those have elevated CA19-9 or who did not undergo lymphadenectomy might be suitable for adjuvant TACE.

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.

Integrated nomograms to predict overall survival and recurrence-free survival in patients with combined hepatocellular cholangiocarcinoma (cHCC) after liver resection

The current clinical classification of primary liver cancer is unable to efficiently predict the prognosis of combined hepatocellular cholangiocarcinoma (cHCC). Accurate satellite nodules (SAT) and microvascular invasion (MVI) prediction in cHCC patients is very important for treatment decision making and prognostic evaluation. The aim of this work was to explore important factors affecting the prognosis of cHCC patients after liver resection and to develop preoperative nomograms to predict SAT and MVI in cHCC patients. The nomogram was developed using the data from 148 patients who underwent liver resection for cHCC patients at our hospital between January 2006 and December 2014. Based on the results of the multivariate analysis, a nomogram integrating all significant independent factors affecting overall survival and recurrence-free survival was constructed to predict the prognosis of cHCC. Next, risk factors for SAT and MVI were evaluated with logistic regression. Blood signatures were established using the LASSO regression, and then, we combined the clinical risk factors and blood signatures of the patients to establish predictive models for SAT and MVI. The C-index of the nomogram for predicting survival was 0.685 (95% CI, 0.638 to 0.732), which was significantly higher than the C-index for other liver cancer classification systems.

Sorafenib in the treatment of patients with hepatocellular carcinoma (HCC) and microvascular infiltration: a systematic review and meta-analysis

OBJECTIVE

Microvascular invasion is shown to be an independent risk factor for liver cancer recurrence. Timely treatment may reduce the recurrence rate and prolong total survival time. The aim of this study was to investigate the effectiveness of sorafenib in treating patients with hepatocellular carcinoma (HCC) and microvascular invasion.

METHODS

A comprehensive literature search was conducted in PubMed, EMBASE, MEDLINE, web of science and Cochrane Library databases for articles published up to December 2019. Two researchers independently reviewed and cross-checked independent reports with sufficient information. A meta-analysis was conducted to assess the impact of sorafenib on mortality in patients with HCC and microvascular involvement.

RESULTS

Four studies were included in the qualitative and quantitative analyses, comprising 955 cancer events and 505 cancer deaths. Meta-analyses showed that sorafenib treatment was associated with an improved survival rate versus no sorafenib treatment in patients with HCC and microvascular invasion (relative risk 1.369, 95% confidence interval 1.193, 1.570).

CONCLUSIONS

Sorafenib treatment may improve survival in patients with HCC and microvascular invasion. However, due to the potential for residual confounding, the results should be interpreted with caution.

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Objectives: To establish a radiomic algorithm based on grayscale ultrasound images and to make preoperative predictions of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods: In this retrospective study, 322 cases of histopathologically confirmed HCC lesions were included. The classifications based on preoperative grayscale ultrasound images were performed in two stages: (1) classifier #1, MVI-negative and MVI-positive cases; (2) classifier #2, MVI-positive cases were further classified as M1 or M2 cases. The gross-tumoral region (GTR) and peri-tumoral region (PTR) signatures were combined to generate gross- and peri-tumoral region (GPTR) radiomic signatures. The optimal radiomic signatures were further incorporated with vital clinical information. Multivariable logistic regression was used to build radiomic models. Results: Finally, 1,595 radiomic features were extracted from each HCC lesion. At the classifier #1 stage, the radiomic signatures based on features of GTR, PTR, and GPTR showed area under the curve (AUC) values of 0.708 (95% CI, 0.603-0.812), 0.710 (95% CI, 0.609-0.811), and 0.726 (95% CI, 0.625-0.827), respectively. Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744 (95% CI, 0.646-0.841). At the classifier #2 stage, the AUC of the GTR radiomic signature was 0.806 (95% CI, 0.667-0.944). Conclusions: Our radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients. The GTR radiomic signature may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.

Correlation between the expressions of circular RNAs in peripheral venous blood and clinicopathological features in hepatocellular carcinoma

Background

Recent studies have reported that circular RNAs (circRNAs) are involved in the development of hepatocellular carcinoma (HCC). This study evaluated the expression of preoperative peripheral venous blood circRNAs in HCC patients and their predictive ability for microvascular invasion (MVI).

Methods

Seven circRNAs (circMTO1, circ-10720, circZKSCAN1, cSMARCA5, circHIPK3, circSETD3 and ciRS-7) were screened from the literature as circRNAs with reported biological functions in HCC. The expression levels of seven circRNAs in preoperative blood samples and HCC tissues were detected by quantitative reverse transcription polymerase chain reaction. The correlations between the circRNA expressions in blood and the clinicopathological factors of HCC patients were analyzed. The risk factors of MVI were analyzed by univariate and multivariate logistic regression. The functional role of circSETD3 in cell migration and invasion was evaluated by wound healing and Transwell assays in vitro.

Results

The expressions of all seven circRNAs were measured in peripheral venous blood samples. The venous expression levels of circHIPK3 and circMTO1 were significantly associated with gender, while circ-10720 and circMTO1 levels were significantly correlated with gross vascular invasion. Furthermore, circMTO1 and cSMARCA5 levels were significantly associated with alpha-fetoprotein level and ciRS-7 was significantly associated with satellite nodules. Importantly, low venous circSETD3 expression was significantly associated with prothrombin induced by vitamin K absence or antagonist-II (PIVKA-II) level, MVI, gross vascular invasion, and liver capsule. Furthermore, venous circSETD3 expression had predictive ability for MVI. Knockdown of circSETD3 promoted cell invasion and metastasis in vitro.

Conclusions

CircRNAs were stably present in peripheral venous blood and associated with multiple clinicopathological characteristics of HCC patients. Venous circSETD3 was an independent risk factor of MVI and shows ability to predict MVI in HCC patients before surgery.

Circulating tumor DNA correlates with microvascular invasion and predicts tumor recurrence of hepatocellular carcinoma

Background

To evaluate the feasibility of predicting tumor recurrence of hepatocellular carcinoma (HCC) patients after curative hepatectomy by detection of circulating tumor DNA (ctDNA) through droplet digital PCR (ddPCR).

Methods

HCC patients receiving surgical treatment were enrolled and peripheral blood samples before and after hepatectomy were collected. Four hotspot mutants, TP53-rs28934571 (c.747G>T), TRET-rs1242535815 (c.1-124C>T), CTNNB1-rs121913412 (c.121A>G) and CTNNB1-rs121913407 (c.133T>C) were selected to detect ctDNA and the mutant allele frequency (MAF) was calculated accordingly. The matched peripheral blood mononuclear cells (PBMCs) were used for Sanger sequencing. The clinicopathologic information of the patients was retrospectively analyzed and the predictive abilities for postoperative recurrence of different clinicopathologic parameters and ctDNA were compared.

Results

Eighty-one patients were enrolled and 70.4% (57/81) of them had detectable ctDNA before hepatectomy. Positive preoperative ctDNA status was related to larger tumor size (P=0.001), multiple tumor lesions (P=0.001), microvascular invasion (MVI) (P<0.001), advanced BCLC stages (P<0.001) and shorter disease free survival (DFS) (P<<0.001) and overall survival (OS) (P<<0.001). Multivariate analysis showed that detectable ctDNA was the independent risk factor for postoperative recurrence. Moreover, receiver operating characteristic (ROC) curves proved that ctDNA possessed the second largest area under the curve (AUC) in foretelling postoperative recurrence right after BCLC stage. For patients after surgery, the alterations of MAF were also correlated to postsurgical recurrence. Patients with increased MAF had more incidences of MVI (P=0.016) and recurrence (P<0.001). At the same time, Kaplan-Meier curves revealed a significant shorter DFS and OS in the patients with increased MAF compared to the patients with decreased MAF (P<0.001 and P=0.0045, respectively) and ROC curves showed MAF to possess the greatest AUC among all the indices for postoperative recurrence.

Conclusions

Digital droplets PCR assessment of specific gene combination through ctDNA possesses potential prognostic value in HCC patients undergoing surgical treatment.

Does microvascular invasion in Barcelona Clinic Liver Cancer stage A multinodular hepatocellular carcinoma indicate early-stage behavior?

Background

To identify the impact of tumor number on Barcelona Clinic Liver Cancer (BCLC) early-stage hepatocellular carcinoma (HCC) and the impact of microvascular invasion (MVI) on multinodular HCC (MHCC).

Methods

We retrospectively analyzed 1,548 patients who had early-stage HCC [solitary HCC (SHCC, n=1,481) and MHCC (n=67)], according to the BCLC classification, after curative resection. Recurrence-free survival (RFS) and overall survival (OS) were compared. Propensity score matching (PSM) was used to balance potential confounding factors.

Results

Both before and after PSM, significant differences were noted between the MHCC group and the SHCC group in RFS but not in OS. For the PSM cohort, the 5-year RFS rates were 7.5% and 41.2% for the MVI-positive MHCC group and the SHCC group, respectively (P<0.001). The 5-year OS rates were 48.9% and 75.2% for the MVI-positive MHCC group and the SHCC group, respectively (P=0.017). The RFS and OS were not significantly different between the MVI-negative MHCC group and the SHCC group. MVI (P=0.029) and multiple nodules (P=0.029) were associated with early recurrence.

Conclusions

The presence of MVI in BCLC early-stage MHCC was highly suggestive of a poor prognosis and should not be classified as early-stage biological behavior.

Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade

BACKGROUND

The aim of this study was to prospectively evaluate the diagnostic efficacy of diffusion kurtosis imaging (DKI) in predicting microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) with comparison to the conventional diffusion-weighted imaging (DWI).

METHODS

This prospective study was approved by the Institutional Review Board, and written informed consent was obtained from all patients. From September 2015 to January 2017, 74 consecutive HCC patients were enrolled in this study. Preoperative magnetic resonance imaging including DKI protocol was performed, and patients were followed up for at least one year after surgery. Diffusion parameters including the mean corrected apparent diffusion coefficient (MD), mean apparent kurtosis coefficient (MK), and apparent diffusion coefficient (ADC) were calculated. Differences of diffusion parameters among different histopathological groups were compared. For parameters that were significantly different between pathological groups, receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic efficiency for identifying MVI and predicting high-grade HCC. Univariate and multivariate logistic regression analyses were used to evaluate the relative value of clinical and laboratory variables and diffusion parameters as risk factors for early recurrence (≤1 year).

RESULTS

Among all the studied diffusion parameters, only MK differed significantly between the MVI-positive and MVI-negative group (0.91±0.10 vs. 0.82±0.09, P<0.001), and showed moderate diagnostic efficacy (AUC =0.77) for identifying MVI. High-grade HCCs showed significantly higher MK values (0.93±0.10 vs. 0.82±0.09, P<0.001), along with MD (1.34±0.18 vs. 1.54±0.22, P<0.001) and ADC values (1.17±0.15 vs. 1.30±0.16, P=0.001) than low-grade HCCs. For differentiating high-grade from low-grade HCCs, MK demonstrated a higher area under the ROC curve (AUC) and significantly higher specificity than MD and ADC (AUC =0.81 vs. 0.76 and 0.74; specificity =82.2% vs. 60.0% and 60.0%, P=0.02). In addition, higher MK (OR =5.700, P=0.002) and Barcelona Clinic Liver Cancer (BCLC) stage C (OR =6.329, P=0.005) were independent risk factors for early HCC recurrence.

CONCLUSIONS

DKI-derived MK values outperformed conventional ADC values for predicting MVI and histologic grade of HCC, and are associated with increased risk of early tumor recurrence.

Postoperative adjuvant transarterial chemoembolization for multinodular hepatocellular carcinoma within the Barcelona Clinic Liver Cancer early stage and microvascular invasion

Background

The survival benefit of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) remained controversial. We aimed to investigate the prognosis effect of PA-TACE on the Barcelona Clinic Liver Cancer (BCLC) early stage multinodular hepatocellular carcinoma (MHCC) patients with/without microvascular invasion (MVI).

Methods

Two hundred and seventy-one patients from January 2010 to December 2014 undergoing curative hepatectomy were included in this study. Disease-free survival (DFS) rates and overall survival (OS) rates as well as prognostic factors were analyzed by the Kaplan-Meier method, the log-rank test and the Cox proportional hazard model.

Results

Thirty-four point four percent (44/128) MVI positive and 55.2% (79/143) MVI negative patients underwent PA-TACE. Multivariate analysis revealed that HBV DNA load >10³ copy/mL, >three tumors, MVI, and without PA-TACE were independent risk factors for poor DFS. Higher alkaline phosphatase (ALP), three tumors, MVI, and without PA-TACE were independent risk factors for poor OS. Both DFS and OS were significantly improved in patients with MVI who received PA-TACE as compared to those who underwent hepatic resection alone (5-year DFS, 26.3% vs. 20.7%, P=0.038; 5-year OS, 73.6% vs. 47.7%, P=0.005). No differences were noted in DFS and OS among MVI negative patients with or without PA-TACE (5-year DFS, 33.7% vs. 33.0%, P=0.471; 5-year OS, 84.1% vs. 80.3%, P=0.523). Early recurrence was more likely to occur in patients without PA-TACE (P=0.001).

Conclusions

PA-TACE was a safe intervention and could effectively prevent tumor recurrence and improve the survival of the BCLC early stage MHCC patients with MVI.