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

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.

Hepatocellular carcinoma macroscopic gross appearance on imaging: predictor of outcome after transarterial chemoembolization in a real-life multicenter French cohort

BACKGROUND

Conventional transarterial chemoembolization (cTACE) with lipiodol is widely performed in patients with hepatocellular carcinoma (HCC) unsuitable for curative treatment. Additional tumor parameters such as HCC macroscopic appearance based on imaging might be helpful for transarterial chemoembolization prognostication and management.

PATIENTS AND METHODS

A total of 405 patients with HCC who underwent cTACE between 2008 and 2016 from a real-life multicenter French cohort were retrospectively reviewed. Tumors were classified into two macroscopic types according to HCC gross appearance on imaging: nodular versus non-nodular. The study population was stratified into two groups: derivation and validation cohorts. Independent prognostic factors of survival based on multivariate cox regression models were determined and then assessed in the validation set. Thereafter, time to progression (TTP) and radiological response rate were investigated for each prognostic factors of survival.

RESULTS

Median overall survival (OS) was 35 months for Barcelona Clinic Liver Cancer (BCLC) stage A, 22 months for BCLC stage B and 12 months for BCLC stage C patients (P < 0.0001). The corresponding TTP for these patients was 12 (7-17) months, 5 (3-6) months and 1.2 (1.2-3) months (P < 0.0001). Multivariate analysis revealed that tumors size and number, non-nodular type, alpha-fetoprotein, aspartate aminotransferase serum levels and impairment of performance status-1 were independent predictors of survival among the study groups. Non-nodular type was the most powerful factor that influences OS, TTP and radiological response rate for the recommended transarterial chemoembolization candidates. TTP was consistent with OS within each stage.

CONCLUSION

HCC macroscopic appearance on imaging is a determinant predictor of outcome after cTACE in a real-life multicenter cohort.

Can IVIM help predict HCC recurrence after hepatectomy?

PURPOSE

To determine the diagnostic performance of intravoxel incoherent motion (IVIM) parameters to predict tumor recurrence after hepatectomy in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

One hundred and fifty-seven patients (mean age 52.54 ± 11.32 years, 87% male) with surgically and pathologically confirmed HCC were included. Regions of interests were drawn including the tumors by two independent radiologists. ADC and IVIM-derived parameters (true diffusion coefficient [D]; pseudodiffusion coefficient [D*]; pseudodiffusion fraction [f]) were obtained preoperatively. The Cox proportional hazards model was used to analyze the predictors associated with tumor recurrence after hepatectomy.

RESULTS

Forty-seven of 157 (29.9%) patients experienced tumor recurrence. The multivariate Cox proportional hazards model revealed that a D value < 0.985 × 10-3 mm2/s (hazard ratio (HR), 0.190; p = 0.023) was a risk factor for tumor recurrence. Additional risk factors included younger age (HR, 0.328; p = 0.034) and higher serum alpha-fetoprotein (AFP) level (HR, 2.079; p = 0.013). Further, receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of the obtained Cox regression model improved from 0.68 for the combination of AFP and age alone to 0.724 for the combination of D value, AFP, and age.

CONCLUSION

The D value derived from the IVIM model is a potential biomarker for the preoperative prediction of recurrence after hepatectomy in patients with HCC. When combined with age and AFP levels, D can improve the predictive performance for tumor recurrence.

KEY POINTS

• The recurrence rate of HCC after hepatectomy was higher in patients with ADC, D, and f values that were lower than the optimal cutoff values. • The optimal cutoff values of ADC, D, D*, and f for predicting recurrence in HBV associated HCC were 0.858 × 10-3 mm2/s, 0.985 × 10-3 mm2/s, 12.5 × 10-3 mm2/s, and 23.4%, respectively. • The D value derived from IVIM diffusion-weighted imaging may be a useful biomarker for preoperative prediction of recurrence after hepatectomy in patients with HCC. When combined with age and AFP levels, D can improve the predictive performance for tumor recurrence.

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.

Nomogram For Preoperative Prediction Of Microvascular Invasion Risk In Hepatocellular Carcinoma

Objective

To preoperatively predict the microvascular invasion (MVI) risk in hepatocellular carcinoma (HCC) using nomogram.

Methods

A retrospective cohort of 513 patients with HCC hospitalized at Xiangya Hospital between January 2014 and December 2018 was included in the study. Univariate and multivariate analysis was performed to identify the independent risk factors for MVI. Based on the independent risk factors, nomogram was established to preoperatively predict the MVI risk in HCC. The accuracy of nomogram was evaluated by using receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA).

Results

Tumor size (OR=1.17, 95% CI: 1.11–1.23, p<0.001), preoperative AFP level greater than 155 ng/mL (OR=1.65, 95% CI: 1.13–2.39, p=0.008) and NLR (OR=1.14, 95% CI: 1.00–1.29, p=0.042) were the independent risk factors for MVI. Incorporating these 3 factors, nomogram was established with the concordance index of 0.71 (95% CI, 0.66–0.75) and well-fitted calibration curves. DCA confirmed that using this nomogram added more benefit compared with the measures that treat all patients or treat none patients. At the cutoff value of predicted probability ≥0.44, the model demonstrated sensitivity of 61.64%, specificity of 71.53%, positive predictive value (PPV) of 64.13%, and negative predictive value (NPV) of 69.31%.

Conclusion

Nomogram was established for preoperative prediction of the MVI risk in HCC patients, and better therapeutic choice will be made if it was applied in clinical practice.

A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

BACKGROUND

Radiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

METHODS

Two hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.

RESULTS

Higher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.

CONCLUSIONS

The nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KEY POINTS

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

The impact of resection margin and microvascular invasion on long-term prognosis after curative resection of hepatocellular carcinoma: a multi-institutional study

BACKGROUND

The resection margin (RM) status and microscopic vascular invasion (MVI) are known prognostic factors for hepatocellular carcinoma (HCC). An enhanced understanding of their impact on long-term prognosis is required to improve oncological outcomes.

METHODS

Using multi-institutional data, the different impact of the RM status (narrow, <1 cm, or wide, ≥1 cm) and MVI (positive or negative) on overall survival (OS) and recurrence-free survival (RFS) after curative liver resection of solitary HCC without macrovascular invasion was analyzed.

RESULTS

In 801 patients, 306 (38%) had a narrow RM and 352 (44%) had positive MVI. The median OS and RFS were 109.8 and 74.8 months in patients with wide RM & negative MVI, 93.5 and 53.1 months with wide RM & positive MVI, 79.2 and 41.6 months with narrow RM & negative MVI, and 69.2 and 37.5 months with narrow RM & positive MVI (both P < 0.01). On multivariable analyses, narrow RM & positive MVI had the highest hazard ratio with reduced OS and RFS (HR 2.96, 95% CI 2.11-4.17, and HR 3.15, 95% CI, 2.09-4.67, respectively).

CONCLUSIONS

Concomitant having narrow RM and positive MVI increases the risks of postoperative death and recurrence by about 2-fold in patients with solitary HCC.

A Nomogram Predicting Microvascular Invasion Risk in BCLC 0/A Hepatocellular Carcinoma after Curative Resection

BACKGROUND

Numerous studies have shown that hepatocellular carcinoma (HCC) without microvascular invasion (MVI) may have better outcomes. This study established a preoperative MVI risk nomogram mainly incorporating three related risk factors of MVI in BCLC 0/A HCC after surgery.

METHODS

Independent predictors for the risk of MVI were investigated, and an MVI risk nomogram was established based on 60 patients in the training group who underwent curative hepatectomy for BCLC 0/A HCC and validated using a dataset in the validation group.

RESULTS

Univariate analysis in the training group showed that hepatitis viral B (HBV) DNA (P=0.034), tumor size (P<0.001), CT value in the venous phase (P=0.039), CT value in the delayed phase (P=0.017), peritumoral enhancement (P=0.013), visible small blood vessels in the arterial phase (P=0.002), and distance from the tumor to the inferior vena cava (IVC) (DTI, P=0.004) were risk factors significantly associated with the presence of MVI. According to multivariate analysis, the independent predictive factors of MVI, including tumor size (P=0.002), CT value in the delayed phase (P=0.018), and peritumoral enhancement (P=0.057), were incorporated in the corresponding nomogram. The nomogram displayed an unadjusted C-index of 0.851 and a bootstrap-corrected C-index of 0.832. Calibration curves also showed good agreement on the presence of MVI. ROC curve analyses showed that the nomogram had a large AUC (0.851).

CONCLUSIONS

The proposed nomogram consisting of tumor size, CT value in the delayed phase, and peritumoral enhancement was associated with MVI risk in BCLC 0/A HCC following curative hepatectomy.

18F-FDG PET/CT predicts microvascular invasion and early recurrence after liver resection for hepatocellular carcinoma: A prospective observational study

BACKGROUND

This study assessed the prognostic value of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) in the prediction of MVI and early recurrence following resection.

METHOD

This prospective study (ClinicalTrials.gov ID: NCT02145013) included 78 consecutive HCC patients who underwent 18F-FDG PET/CT before curative-intent resection from 2014 to 2017. Prognostic factors available before surgery for predicting MVI and early recurrence (≤2 years) were identified by univariate and multivariate analyses.

RESULTS

The 18F-FDG PET/CT result was positive in 30 (38%) patients. MVI was present in 33% (26/78) of specimens. Early recurrence occurred in 19% (14/74) of surviving patients. PET/CT positivity was the sole independent predictor of MVI (odds ratio [OR] = 3.6, 95% confidence interval [CI] = 1.1-11.2; p = 0.03), with a specificity and sensitivity for predicting MVI of 73% and 62%, respectively. Analysis of variables available before surgery showed that PET/CT positivity (hazard ratio [HR] = 5.8, 95% CI = 1.6-20.4; p = 0.006) and the male sex (HR = 6.6; 95% CI = 1.8-24.2; p = 0.005) were independent predictors of early recurrence.

CONCLUSION

18F-FDG PET/CT predicts MVI and early recurrence after surgery for HCC and could be used to select patients for neoadjuvant treatment.

Liver Imaging Reporting and Data System Category 5: MRI Predictors of Microvascular Invasion and Recurrence After Hepatectomy for Hepatocellular Carcinoma

OBJECTIVE. We investigated in Liver Imaging Reporting and Data System category 5 (LR-5) observations whether imaging features, including LI-RADS imaging features, could predict microvascular invasion (MVI) and posthepatectomy recurrence in high-risk adult patients with hepatocellular carcinoma (HCC). MATERIALS AND METHODS. We retrospectively identified 149 high-risk patients who underwent 3-T MRI within 1 month before hepatectomy for HCC; 81 of 149 patients with no HCC recurrence were followed for more than 1 year. Tumors with clear surgical margins were confirmed in each hepatectomy specimen. MVI was evaluated histologically by a histopathologist. Tumor recurrence was determined by clinical and imaging follow-up. Two independent radiologists reviewed the prehepatectomy MR images and assessed LI-RADS v2018 imaging features as well as some non-LI-RADS features in all LR-5 observations in consensus. Alpha-fetoprotein level, tumor number, and imaging features were analyzed as potential predictors for MVI and posthepatectomy recurrence using multivariate logistic regression and Cox proportional hazards models. RESULTS. One hundred forty-nine patients with pathologically confirmed HCC were included; 64 of 149 (43.0%) patients had MVI, whereas 48 of 129 (37.2%) patients had tumor recurrence within 3 years after hepatectomy. Mosaic architecture (odds ratio, 3.420; p < 0.001) and nonsmooth tumor margin (odds ratio, 2.554; p = 0.011) were independent predictors of MVI. Multifocal tumors (hazard ratio, 2.101; p = 0.034), absence of fat in mass (hazard ratio, 2.109; p = 0.015), and nonsmooth tumor margin (hazard ratio, 2.415; p = 0.005) were independent predictors of posthepatectomy recurrence. CONCLUSION. In high-risk patients with LR-5 HCC, mosaic architecture and non-smooth tumor margin independently predicted MVI. Multifocal tumors, absence of fat in mass, and nonsmooth tumor margin independently predicted recurrence.

Model-based three-dimensional texture analysis of contrast-enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma

The purpose of the present study was to investigate the value of contrast-enhanced magnetic resonance imaging (CE-MRI) texture analysis for preoperatively predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Accordingly, a retrospective study of 142 patients with pathologically confirmed HCC was performed. The patients were divided into two cohorts: The training cohort (n=99) and the validation cohort (n=43), including the MVI-positive group (n=53) and MVI-negative group (n=89). On the basis of three-dimensional texture analysis, 58 features were extracted from the preoperative CE-MR images of arterial-phase (AP) and portal-venous-phase (PP). The t-test or Kruskal-Wallis test, univariate logistic regression analysis and Pearson correlation were applied for feature reduction. Clinical-radiological features were also analyzed. Multivariate logistic regression analysis was used to build the texture model and combined model with clinical-radiological features. The MVI-predictive performance of the models was evaluated using receiver operating characteristic (ROC) analysis and presented using nomogram. Among the clinical features, a significant difference was found in maximum tumor diameter (P=0.002), tumor differentiation (P=0.026) and α-fetoprotein level (P=0.025) between the two groups in the training cohort. Four MR texture features in AP and five in PP images were identified through feature reduction. On ROC analysis, the AP texture model showed better diagnostic performance than did the PP model in the validation cohort, with an area under the curve (AUC) of 0.773 vs. 0.623, sensitivity of 0.750 vs. 0.500, and specificity of 0.815 vs. 0.926. Together with the clinical features, the combined model of AP improved the AUC, sensitivity and specificity to 0.810, 0.811 and 0.790, respectively, which was demonstrated in nomogram. To conclude, model-based texture analysis of CE-MRI could predict MVI in HCC preoperatively and noninvasively, and the AP image shows better predictive efficiency than PP image. The combined model of AP with clinical-radiological features could improve MVI prediction ability.

Prognostic value of preoperative computed tomography in HBV-related hepatocellular carcinoma patients after curative resection

Background: Preoperative treatments are considered for patients with worse outcome to improve overall survival and reduce tumor relapse. This study developed a prognostic risk estimation for patients with hepatitis B virus (HBV)-related solitary hepatocellular carcinoma after curative resection, including preoperative computed tomography (CT) signatures. Methods: Preoperative multiphasic CTs for 166 patients with operable HCC were performed in our hospital from 15 November 2013 through 15 May 2015. Follow-up information, until 5 June 2017, included: CT, pathological and clinical characteristics, and recurrence and metastases of HCC confirmed by pathological or radiological diagnosis. The parameters were analyzed by the Kaplan-Meier method and Cox proportional hazards regression analysis. Results: In multivariate analyses, overall survival was not significantly associated with any of the analyzed prognostic risk factors, but did show that the following were significant prognostic risk factors for disease-free survival: larger tumor size, positive radiogenomic venous invasion, non-smooth tumor margin, and histological microvascular invasion. These were all incorporated into the nomogram. The calibration curves for predicting the probability of disease-free survival between the nomogram and actual observation showed good conformity. Conclusion: In patients with HBV-related HCC, CT signatures were a noninvasive significant indicator of disease-free survival. Thus, consideration of CT signatures may optimize preoperative treatment strategies for the individual patient.

Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction

BACKGROUND

This study was performed to prospectively develop and validate a radiomics nomogram for predicting postoperative early recurrence (≤1 year) of hepatocellular carcinoma (HCC) using whole-lesion radiomics features on preoperative gadoxetic acid-enhanced magnetic resonance (MR) images.

METHODS

In total, 155 patients (training cohort: n = 108; validation cohort: n = 47) with surgically confirmed HCC were enrolled in this IRB-approved prospective study. Three-dimensional whole-lesion regions of interest were manually delineated along the tumour margins on multi-sequence MR images. Radiomics features were generated and selected to build a radiomics score using the least absolute shrinkage and selection operator (LASSO) method. Clinical characteristics and qualitative imaging features were identified by two independent radiologists and combined to establish a clinical-radiological nomogram. A radiomics nomogram comprising the radiomics score and clinical-radiological risk factors was constructed based on multivariable logistic regression analysis. Diagnostic performance and clinical usefulness were measured by receiver operation characteristic (ROC) and decision curves.

RESULTS

In total, 14 radiomics features were selected to construct the radiomics score. For the clinical-radiological nomogram, the alpha-fetoprotein (AFP) level, gross vascular invasion and non-smooth tumour margin were included. The radiomics nomogram integrating the radiomics score with clinical-radiological risk factors showed better discriminative performance (AUC = 0.844, 95%CI, 0.769 to 0.919) than the clinical-radiological nomogram (AUC = 0.796, 95%CI, 0.712 to 0.881; P = 0.045), with increased clinical usefulness confirmed using a decision curve analysis.

CONCLUSIONS

Incorporating multiple predictive factors, the radiomics nomogram demonstrated great potential in the preoperative prediction of early HCC recurrence after surgery.

Use of gadoxetate disodium in patients with chronic liver disease and its implications for liver imaging reporting and data system (LI-RADS)

Use of gadoxetate disodium, a hepatobiliary gadolinium-based agent, in patients with chronic parenchymal liver disease offers the advantage of improved sensitivity for detecting hepatocellular carcinoma (HCC). Imaging features of liver observations on gadoxetate-enhanced MRI may also serve as biomarkers of recurrence-free and overall survival following definitive treatment of HCC. A number of technical and interpretative pitfalls specific to gadoxetate exist, however, and needs to be recognized when protocoling and interpreting MRI exams with this agent. This article reviews the advantages and pitfalls of gadoxetate use in patients at risk for HCC, and the potential impact on Liver Imaging Reporting and Data System (LI-RADS) imaging feature assessment and categorization. Level of Evidence: 5 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;49:1236-1252.

Prognostic and Therapeutic Implications of Microvascular Invasion in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a morbid condition for which surgical and ablative therapy are the only options for cure. Nonetheless, over half of patients treated with an R0 resection will develop recurrence. Early recurrences within 2 years after resection are thought to be due to the presence of residual microscopic disease, while late recurrences > 2 years after resection are thought to be de novo metachronous HCCs arising in chronically injured liver tissue. Microvascular invasion (MVI) is defined as the presence of micrometastatic HCC emboli within the vessels of the liver, and is a critical determinant of early recurrence and survival. In this review, we summarize the pathogenesis and clinical relevance of MVI, which correlates with adverse biological features, including high grade, large tumor size, and epithelial-mesenchymal transition. Multiple classification schemas have been proposed to capture the heterogeneous features of MVI that are associated with prognosis. However, currently, MVI can only be determined based on surgical specimens, limiting its clinical applicability. Going forward, advances in axial imaging technologies, molecular characterization of biopsy tissue, and novel serum biomarkers hold promise as future methods for non-invasive MVI detection. Ultimately, MVI status may be used to help clinicians determine treatment plans, particularly with respect to surgical intervention, and to provide more accurate prognostication.

Hepatocellular Carcinoma: Current Imaging Modalities for Diagnosis and Prognosis

As opposed to most solid cancers, hepatocellular carcinoma (HCC) does not necessarily require histological confirmation. Noninvasive diagnosis is possible and relies on imaging. In cirrhotic patients, the diagnosis can be obtained in tumors displaying typical features that include non-rim arterial phase hyperenhancement followed by washout during the portal venous and/or delayed phases on CT or MR imaging. This pattern is very specific and, as such, has been endorsed by both Western and Asian diagnostic guidelines and systems. However, its sensitivity is not very high, especially for small lesions. Numerous ancillary features favoring the diagnosis of HCC may be depicted, including appearance after injection of hepatobiliary MR imaging contrast agents. These features increase confidence in diagnosis, but cannot be used as substitutes to liver biopsy. Aside from its diagnostic purpose, imaging also helps to assess tumor biology and patient outcome, by identifying features of local invasiveness. The purpose of this review article is to offer an overview of the role of imaging for the diagnosis and prognostication of HCC.

A scientometric analysis on hepatocellular carcinoma magnetic resonance imaging research from 2008 to 2017

Background

With the development of new magnetic resonance imaging (MRI) techniques, an increasing number of articles have been published regarding hepatocellular carcinoma magnetic resonance imaging (HCCMRI) in the past decade. However, few studies have statistically analyzed these published articles. In this study, we aim to systematically evaluate the scientific outcomes of HCCMRI research and explore the research hotspots from 2008 to 2017.

Methods

The included articles regarding HCCMRI research from 2008 to 2017 were downloaded from the Web of Science Core Collection and verified by two experienced radiologists. Excel 2016 was used to analyze the literature data, including the publication years and journals. CiteSpace V was used to perform co-occurrence analyses for authors, countries/regions and institutions and to generate the related collaboration network maps. Reference co-citation analysis (RCA) and burst keyword detection were also performed using CiteSpace V to explore the research hotspots in the past decade.

Results

A total of 835 HCCMRI articles published from 2008 to 2017 were identified. Journal of Magnetic Resonance Imaging published the most articles (79 publications, 9.46%). Extensive cooperating relationship were observed among countries/regions and among authors. South Korea had the most publications (199 publications, 21.82%), followed by the United States of America (USA) (190 publications, 20.83%), Japan (162 publications, 17.76%), and the People's Republic of China (148 publications, 16.23%). Among the top 10 co-cited authors, Bruix J (398 citations) was ranked first, followed by Llovet JM (235 citations), Kim YK (170 citations) and Forner A (152 citations). According to the RCA, ten major clusters were explored over the last decade; "LI-RADS data system" and "microvascular invasion" (MVI) were the two most recent clusters. Forty-seven burst keywords with the highest citation strength were detected over time. Of these keywords, "microvascular invasion" had the highest strength in the last 3 years. The LI-RADS has been constantly updated with the latest edition released in July 2018. However, the LI-RADS still has limitations in identifying certain categories of lesions by conceptual and non-quantitative probabilistic methods. Plenty of questions still need to be further answered such as the difference of diagnostic efficiency of each major/ancillary imaging features. Preoperative prediction of MVI of HCC is very important to therapeutic decision-making. Some parameters of Gd-EOB-DTPA-enhanced MRI were found to be useful in prediction of MVI, however, with a high specificity but a very low sensitivity. Comprehensive predictive model incorporating both imaging and clinical variables may be the more preferable in prediction of MVI of HCC.

Conclusions

HCCMRI-related publications displayed a gradually increasing trend from 2008 to 2017. The USA has a central position in collaboration with other countries/regions, while South Korea contributed the most in the number of publications. Of the ten major clusters identified in the RCA, the two most recent clusters were "LI-RADS data system" and "microvascular invasion", indicative of the current HCCMRI research hotspots.

CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation

OBJECTIVE

To construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after curative treatment.

MATERIALS AND METHODS

In total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI).

RESULTS

By comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E (P = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 (P = 0.01) compared to PT-E.

CONCLUSION

The CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E.

Current status of imaging biomarkers predicting the biological nature of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is heterogeneous in terms of its biological nature. Various factors related to its biological nature, including size, multifocality, macroscopic morphology, grade of differentiation, macro/microvascular invasion, bile duct invasion, intra-tumoral fat and molecular factors, and their value as prognostic imaging biomarkers have been reported. And recently, genome-based molecular HCC classification correlated with clinical outcome has been elucidated. The imaging biomarkers suggesting a less aggressive nature of HCC are smaller size, solitary tumor, smooth margin suggesting small nodular type with indistinct margin and simple nodular type with distinct margin, capsule, imaging biomarkers predicting early or well-differentiated grade, intra-tumoral fat detection, and low fluorodeoxyglucose (FDG) accumulation. The imaging biomarkers suggesting an aggressive HCC nature are larger size, multifocality, non-smooth margin suggesting simple nodular type with extranodular growth, confluent multinodular, and infiltrative type, imaging biomarkers predicting poor differentiation, macrovascular tumor thrombus, predicting microvascular invasion imaging biomarkers, bile duct dilatation or tumor thrombus, and high FDG accumulation. In the genome-based molecular classification, CTNNB-1 mutated HCC shows a less aggressive nature, while CK19/EpCAM positive HCC and macrotrabecular massive HCC show an aggressive one. Better understanding of these imaging biomarkers can contribute to devising more appropriate treatment plans for HCC.

Hepatocellular Carcinoma with Irregular Rim-Like Arterial Phase Hyperenhancement: More Aggressive Pathologic Features

BACKGROUND AND AIMS

The purpose of our study was to examine the histopathologic characteristics of hepatocellular carcinoma (HCC) with irregular rim-like arterial phase enhancement (IRE), which has been reported to be associated with more aggressive tumor behavior.

METHODS

We investigated 84 pathologically confirmed HCCs in 84 patients who underwent curative hepatic resection after gadoxetate-enhanced magnetic resonance imaging between January 2008 and February 2013. Two abdominal radiologists independently reviewed these images and classified HCCs into two categories: HCC showing IRE (IRE-HCC) and HCC showing hypoenhancement or diffuse arterial enhancement (non-IRE-HCC). Twenty-two HCCs were classified as IRE-HCCs, and 51 were classified as non-IRE-HCCs concordantly by both reviewers. The remaining 11 HCCs, on whose radiologic classifications the reviewers disagreed, were classified as HCCs with intermediate enhancement patterns. The HCC clinicopathologic characteristics and patient outcomes were then compared.

RESULTS

IRE-HCCs showed more frequent microvascular invasion (91 vs. 35%), lower microvascular density (246.5 vs. 426.5/mm²), higher proportions of sinusoid-like microvascular pattern (55 vs. 0%) and macrotrabecular pattern (45 vs. 0%), and larger areas of tumor necrosis (15 vs. 0%) and fibrous stroma (8.3 vs. 2.1%) than non-IRE-HCCs. IRE-HCCs also expressed higher levels of immunomarkers of hypoxia (carbonic anhydrase IX, 64 vs. 8%) and stemness (EpCAM, 50 vs. 20%). p values were < 0.001 for all comparisons except for EpCAM (p = 0.026). HCCs with intermediate enhancement patterns showed mixed/intermediate pathologic features from both IRE- and non-IRE-HCCs. IRE-HCC patients showed poorer 5-year disease-free survival after curative resection than non-IRE-HCC patients (p = 0.005).

CONCLUSIONS

IRE-HCCs demonstrate aggressive histopathologic features, including more hypoxic and fibrotic tumor microenvironments and increased stemness, compared to non-IRE-HCCs. IRE might therefore serve as a noninvasive imaging biomarker for aggressive HCC.

Imaging features related with prognosis of hepatocellular carcinoma

We discuss various imaging features that have been reported to be associated with the prognosis of hepatocellular carcinoma (HCC) but not included in the current staging systems: findings related with microvascular invasion, tumor encapsulation, intratumoral fat, presence of satellite nodules, peritumoral hypointensity on hepatobiliary phase images of gadoxetic-acid enhanced MRI, restricted diffusion, and irregular rim-like hyperenhancement. Current evidence suggests that larger (> 2 cm) tumor size, presence of satellite nodules, presence of irregular rim-like hyperenhancement of a tumor, peritumoral parenchymal enhancement in the arterial phase, and peritumoral hypointensity observed on hepatobiliary phase images are independent imaging features to portend a worse prognosis in patients with hepatocellular carcinoma.

Peritumoral tissue on preoperative imaging reveals microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

BACKGROUND

Histologic microvascular invasion (MVI) substantially worsens the prognosis of patients with hepatocellular carcinoma, and can only be diagnosed postoperatively. Preoperative assessment of MVI by imaging has been focused on tumor-related features, while peritumoral imaging features have been indicated elsewhere to be more accurate. The aim of the present study is to evaluate the association between peritumoral imaging features and MVI.

METHODS

Literature search was performed using the PubMed, Embase, and Cochrane Library databases. Summary results of the association between peritumoral imaging features and MVI were presented as the odds ratio (OR) and the 95% confidence interval. Meta-regression and subgroup analyses were performed when heterogeneity was detected. Diagnostic accuracy analysis was also conducted for identified features.

RESULTS

Ten studies were included in the analysis. Moderate and low heterogeneities were found among the seven studies on peritumoral enhancement and four studies on peritumoral hypointensity on HBP, respectively. Summary results revealed a significant association between MVI and peritumoral enhancement (OR 4.04 [2.23, 7.32], p < 0.05), and peritumoral hypointensity on HBP (OR 10.62 [5.31, 21.26], p < 0.05). Diagnostic accuracy analysis revealed high specificity (0.90-0.94) but low sensitivity (0.29-0.40) for both features to assess MVI.

CONCLUSION

The two peritumoral imaging features are significantly associated with MVI. The two features highly suggest MVI only when present with a high false negative rate. Promotion of their diagnostic efficiency can be a worthwhile task for future research.

LI-RADS Classification and Prognosis of Primary Liver Cancers at Gadoxetic Acid-enhanced MRI

Purpose To (a) evaluate the postsurgical prognostic implication of the Liver Imaging Reporting and Data System (LI-RADS) categories of primary liver cancers and (b) determine the performance of LI-RADS version 2017 in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (IHCC) and combined hepatocellular-cholangiocarcinoma (cHCC-CC) at gadoxetic acid-enhanced MRI. Materials and Methods In this retrospective study, 194 patients with cirrhosis and surgically proven single primary liver cancer (53 with cHCC-CC, 44 with IHCC, and 97 with HCC) were evaluated with gadoxetic acid-enhanced MRI between 2009 and 2014. The mean patient age was 57 years (age range, 30-83 years). There were 155 men with a mean age of 56 years (range, 30-81 years) and 39 women with a mean age of 58 years (range, 38-83 years). Two independent readers assigned an LI-RADS category for each nodule. Overall survival (OS), recurrence-free survival (RFS), and their associated factors were evaluated by using the Kaplan-Meier method, log-rank test, and Cox proportional hazard model. Results In the multivariable analysis, the LI-RADS category was an independent factor for OS (hazard ratio, 4.2; P < .001) and RFS (hazard ratio, 2.6; P = .01). The LR-M category showed more correlation with poorer OS and RFS than did the LR-4 or LR-5 category for all primary liver cancers (P < .001 for both), HCCs (P = .01 and P < .001, respectively), and cHCC-CCs (P = .01 and P = .03, respectively). The LR-5 category had a sensitivity of 69% (67 of 97) and a specificity of 87% (84 of 97) in the diagnosis of HCC; most false-positive diagnoses (85%, 11 of 13) were the result of misclassification of cHCC-CCs. Conclusion The Liver Imaging Reporting and Data System (LI-RADS) category was associated with postsurgical prognosis of primary liver cancers, independent of pathologic diagnosis. The LI-RADS enabled the correct classification of most hepatocellular carcinomas (HCCs) and intrahepatic cholangiocarcinomas, whereas differentiation of combined hepatocellular-cholangiocarcinoma from HCC was unreliable. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Bashir and Chernyak in this issue.

Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images

BACKGROUND

This study aims to establish a radiomics analysis system for the diagnosis and clinical behaviour prediction of hepatocellular carcinoma (HCC) based on multi-parametric ultrasound imaging.

METHODS

A total of 177 patients with focal liver lesions (FLLs) were included in the study. Every patient underwent multi-modal ultrasound examination, including B-mode ultrasound (BMUS), shear wave elastography (SWE), and shear wave viscosity (SWV) imaging. The radiomics analysis system was built on sparse representation theory (SRT) and support vector machine (SVM) for asymmetric data. Through the sparse regulation from the SRT, the proposed radiomics system can effectively avoid over-fitting issues that occur in regular radiomics analysis. The purpose of the proposed system includes differential diagnosis between benign and malignant FLLs, pathologic diagnosis of HCC, and clinical prognostic prediction. Three biomarkers, including programmed cell death protein 1 (PD-1), antigen Ki-67 (Ki-67) and microvascular invasion (MVI), were included and analysed. We calculated the accuracy (ACC), sensitivity (SENS), specificity (SPEC) and area under the receiver operating characteristic curve (AUC) to evaluate the performance of the radiomics models.

RESULTS

A total of 2560 features were extracted from the multi-modal ultrasound images for each patient. Five radiomics models were built, and leave-one-out cross-validation (LOOCV) was used to evaluate the models. In LOOCV, the AUC was 0.94 for benign and malignant classification (95% confidence interval [CI]: 0.88 to 0.98), 0.97 for malignant subtyping (95% CI: 0.93 to 0.99), 0.97 for PD-1 prediction (95% CI: 0.89 to 0.98), 0.94 for Ki-67 prediction (95% CI: 0.87 to 0.97), and 0.98 for MVI prediction (95% CI: 0.93 to 0.99). The performance of each model improved when the viscosity modality was included.

CONCLUSIONS

Radiomics analysis based on multi-modal ultrasound images could aid in comprehensive liver tumor evaluations, including diagnosis, differential diagnosis, and clinical prognosis.

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.

3D MR Elastography of Hepatocellular Carcinomas as a Potential Biomarker for Predicting Tumor Recurrence

BACKGROUND

Preoperative prediction of tumor recurrence is important in the management of patients with hepatocellular carcinoma (HCC).

PURPOSE

To investigate whether tumor stiffness derived by magnetic resonance elastography (MRE) could predict early recurrence of HCC after hepatic resection.

STUDY TYPE

Retrospective.

POPULATION

In all, 99 patients with pathologically confirmed HCCs after surgical resection.

FIELD STRENGTH/SEQUENCE

3.0T; preoperative MRE with 60-Hz mechanical vibrations using an active acoustic driver.

ASSESSMENT

Regions of interest (ROIs) were manually drawn in the tumors to measure mean tumor stiffness. Surgical specimens were reviewed for histological grade, capsule, vascular invasion, and surgical margins. The early recurrence of HCC was defined as that occurring within 2 years after resection.

STATISTICAL TESTS

Cox proportional hazard models were used to evaluate risk factors associated with the time to early recurrence.

RESULTS

HCCs with recurrence had higher tumor stiffness, higher rate of advanced T stage, vascular invasion, lower rate of capsule formation, larger tumor size, higher aspartate aminotransferase (AST), and hepatitis B virus (HBV)-DNA level and aspartate aminotransferase / alanine aminotransferase ratio (P = 0.031, 0.007, 0.01, <0.001, 0.015, 0.034, 0.01, and 0.014, respectively) than HCCs without recurrence. Vascular invasion (hazard ratio [HR] = 2.922; 95% confidence interval [CI]: [1.079, 7.914], P = 0.035) and mean tumor stiffness (HR = 1.163; 95% CI: [1.055, 1.282], P = 0.002) were risk factors associated with early recurrence. Each 1-kPa increase in tumor stiffness was associated with a 16.3% increase in the risk for tumor recurrence.

DATA CONCLUSION

The mean stiffness of HCCs may be a useful, noninvasive, quantitative biomarker for the prediction of early HCC recurrence after hepatic resection.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;49:719-730.

Noninvasive imaging of hepatocellular carcinoma: From diagnosis to prognosis

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and a major public health problem worldwide. Hepatocarcinogenesis is a complex multistep process at molecular, cellular, and histologic levels with key alterations that can be revealed by noninvasive imaging modalities. Therefore, imaging techniques play pivotal roles in the detection, characterization, staging, surveillance, and prognosis evaluation of HCC. Currently, ultrasound is the first-line imaging modality for screening and surveillance purposes. While based on conclusive enhancement patterns comprising arterial phase hyperenhancement and portal venous and/or delayed phase wash-out, contrast enhanced dynamic computed tomography and magnetic resonance imaging (MRI) are the diagnostic tools for HCC without requirements for histopathologic confirmation. Functional MRI techniques, including diffusion-weighted imaging, MRI with hepatobiliary contrast agents, perfusion imaging, and magnetic resonance elastography, show promise in providing further important information regarding tumor biological behaviors. In addition, evaluation of tumor imaging characteristics, including nodule size, margin, number, vascular invasion, and growth patterns, allows preoperative prediction of tumor microvascular invasion and patient prognosis. Therefore, the aim of this article is to review the current state-of-the-art and recent advances in the comprehensive noninvasive imaging evaluation of HCC. We also provide the basic key concepts of HCC development and an overview of the current practice guidelines.

Preoperative histogram analysis of intravoxel incoherent motion (IVIM) for predicting microvascular invasion in patients with single hepatocellular carcinoma

PURPOSE

To evaluate the value of intravoxel incoherent motion (IVIM) histogram analysis based on whole tumor volume in predicting microvascular invasion (MVI) of single hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

The study enrolled 41 patients with pathologically proven HCCs who underwent IVIM diffusion-weighted imaging with nine b values and contrast-enhanced magnetic resonance imaging (MRI). Histogram parameters including mean; skewness; kurtosis; and percentiles (5th, 10th, 25th, 50th, 75th, 90th, 95th) were derived from apparent diffusion coefficient (ADC), perfusion fraction (f), true diffusion coefficient (D), and pseudo diffusion coefficient (D*). Quantitative histogram parameters and clinical data were compared between HCCs with and without MVI. For significant parameters, receiver operating characteristic (ROC) curves were further plotted to compare the diagnosis performance for identifying MVI.

RESULTS

The mean, 5th, 10th, 25th, 50th, and 75th percentiles of D, and the 5th, 10th, and 25th percentiles of ADC between HCCs with and without MVI were statistically significant (all P＜0.05). The histogram parameters of D* and f showed no statistically significant differences between HCCs with and without MVI (all P＞0.05). The areas under the ROC curves (AUCs) were 0.707-0.874 for D and 0.668-0.720 for ADC. The largest AUC of D (5th percentile) showed significantly higher accuracy than that of ADC or tumor size (P = 0.009-0.046). With a cut-off of 0.403 × 10^-3 mm²/s, the 5th percentile of D value provided a sensitivity of 81% and a specificity of 85% in the prediction of MVI.

CONCLUSIONS

Histogram analysis of IVIM based on whole tumor volume can be useful for predicting MVI. The 5th percentile of D was most useful value to predict MVI of HCC.

Contribution of virtual biopsy to the screening of microvascular invasion in hepatocellular carcinoma: A pilot study

BACKGROUND & AIMS

Microvascular invasion (mVI) is a major prognostic factor in hepatocellular carcinoma (HCC) that cannot be detected before surgery. Predictive biomarkers of mVI are thus urgently needed. We have developed an original approach of virtual biopsy to assess the performance of an immunohistochemical panel comprising three biomarkers of mVI (H4K16ac, H4K20me2, PIVKA-II) for the prediction of mVI in HCC core needle biopsies (CNB).

METHODS

A test set of HCC surgical specimens (n = 64) and an independent validation set of HCC CNB (n = 42) were retrospectively constituted. Immunostainings were first quantified in the test set on the whole tissue section, to determine optimal cut-off values for each marker. From the digitised image of the whole section, three virtual biopsies were provided. Immunostainings and accuracy of the panel for the prediction of mVI were further assessed in virtual biopsies and in the validation set of CNB.

RESULTS

In virtual biopsies, PIVKA-II/H4K16ac had the best performance for prediction of mVI, with sensitivity, specificity, predictive positive value (PPV), and predictive negative value (PNV) of 30%, 97%, 91%, 56%, respectively. In CNB, PIVKA-II/H4K20me2 showed the best accuracy for prediction of mVI, with sensitivity, specificity, PPV, and NPV of 43%, 95%, 90%, and 62%, respectively. The two panels were independent predictive factors of mVI (PIVKA-II/H4K16ac, P = .037; PIVKA-II/H4K20me2, P = .026).

CONCLUSION

This study shows that a panel of two markers is able to predict mVI in HCC CNB, and pave the way for the future development of prognostic biomarkers in HCC that could guide the therapeutic strategy.

Imaging features of microvascular invasion in hepatocellular carcinoma developed after direct-acting antiviral therapy in HCV-related cirrhosis

OBJECTIVES

To evaluate imaging features of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) developed after direct-acting antiviral (DAA) therapy in HCV-related cirrhosis.

METHODS

Retrospective cohort study on 344 consecutive patients with HCV-related cirrhosis treated with DAA and followed for 48-74 weeks. Using established imaging criteria for MVI, HCC features were analysed and compared with those in nodules not occurring after DAA.

RESULTS

After DAA, HCC developed in 29 patients (single nodule, 18 and multinodular, 11). Median interval between therapy end and HCC diagnosis was 82 days (0-318). Forty-one HCC nodules were detected (14 de novo, 27 recurrent): maximum diameter was 10-20 mm in 27, 20-50 mm in 13, and > 50 mm in 1. Imaging features of MVI were present in 29/41 nodules (70.7%, CI: 54-84), even in 17/29 nodules with 10-20 mm diameter (58.6%, CI: 39-76). MVI was present in only 17/51 HCC nodules that occurred before DAA treatment (33.3%, CI: 22-47) (p= 0.0007). MVI did not correlate with history of previous HCC.

CONCLUSIONS

HCC occurs rapidly after DAA therapy, and aggressive features of MVI characterise most neoplastic nodules. Close imaging evaluations are needed after DAA in cirrhotic patients.

KEY POINTS

• In HCV cirrhosis, hepatocellular carcinoma develops soon after direct-acting antiviral therapy. • HCC presents imaging features of microvascular invasion, predictive of more aggressive progression. • Cirrhotic patients need aggressive and close monitoring after direct-acting antiviral therapy.

Validated preoperative computed tomography risk estimation for postoperative hepatocellular carcinoma recurrence

AIM

To develop and validate a risk estimation of tumor recurrence following curative resection of operable hepatocellular carcinoma (HCC).

METHODS

Data for 128 patients with operable HCC (according to Barcelona Clinic Liver Cancer imaging criteria) who underwent preoperative computed tomography (CT) evaluation at our hospital from May 1, 2013 through May 30, 2014 were included in this study. Follow-up data were obtained from hospital medical records. Follow-up data through May 30, 2016 were used to retrospectively analyze preoperative multiphasic CT findings, surgical histopathology results, and serum α-fetoprotein and thymidine kinase-1 levels. The χ² test, independent t-test, and Mann-Whitney U test were used to analyze data. A P-value of < 0.05 was considered statistically significant.

RESULTS

During the follow-up period, 38 of 128 patients (29.7%) had a postoperative HCC recurrence. Microvascular invasion (MVI) was associated with HCC recurrence (χ² = 13.253, P < 0.001). Despite postoperative antiviral therapy and chemotherapy, 22 of 44 patients with MVI experienced recurrence after surgical resection. The presence of MVI was 57.9% sensitive, 75.6% specific and 70.3% accurate in predicting postoperative recurrence. Of 84 tumors without MVI, univariate analysis confirmed that tumor margins, tumor margin grade, and tumor capsule detection on multiphasic CT were associated with HCC recurrence (P < 0.05). Univariate analyses showed no difference between groups with respect to hepatic capsular invasion, Ki-67 proliferation marker value, Edmondson-Steiner grade, largest tumor diameter, necrosis, arterial phase enhanced ratio, portovenous phase enhanced ratio, peritumoral enhancement, or serum α-fetoprotein level.

CONCLUSION

Non-smooth tumor margins, incomplete tumor capsules and missing tumor capsules correlated with postoperative HCC recurrence. HCC recurrence following curative resection may be predicted using CT.