Imaging features of small hepatocellular carcinomas with microvascular invasion on gadoxetic acid-enhanced MR imaging

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics

Background

Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes (DEGs) in patients with and without MVI based on TCGA data, established a prediction model and explored the prognostic value of DEGs for HCC patients with MVI.

Methods

In this paper, gene expression and clinical data of liver cancer patients were downloaded from the TCGA database. The DEG analysis was conducted using DESeq2. Using the least absolute shrinkage and selection operator, MVI-status-related genes were identified. A Kaplan-Meier survival analysis was performed using these genes. Finally, we validated two genes, HOXD9 and HOXD10, using two sets of HCC tissue microarrays from 260 patients.

Results

Twenty-three MVI-status-related key genes were identified. Based on the key genes, we built a classification model using random forest and time-dependent receiver operating characteristic (ROC), which reached 0.814. Then, we performed a survival analysis and found ten genes had a significant difference in survival time. Simultaneously, using two sets of 260 patients’ HCC tissue microarrays, we validated two key genes, HOXD9 and HOXD10. Our study indicated that HOXD9 and HOXD10 were overexpressed in HCC patients with MVI compared with patients without MVI, and patients with MVI with HOXD9 and 10 overexpression had a poorer prognosis than patients with MVI with low expression of HOXD9 and 10.

Conclusion

We established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic value of DEGs for HCC patients with MVI. These DEGs that are related to MVI warrant further study regarding the occurrence and development of MVI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09047-1.

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

Background

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

Methods

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

Results

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

Conclusion

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

Integrating baseline MR imaging biomarkers into BCLC and CLIP improves overall survival prediction of patients with hepatocellular carcinoma (HCC)

OBJECTIVES

We aimed to evaluate the independent predictive role of baseline imaging biomarkers for overall survival (OS) and transplant-free survival (TFS) in patients with HCC and assess the incremental value of these biomarkers to current staging systems.

METHODS

In this retrospective IRB approved study, the clinical, laboratory, and imaging parameters of 304 HCC patients were collected. Cox regression model was utilized to identify the potential predictors of survival. Recursive partitioning test was utilized to identify the optimal ADC cutoff for stratifying patients' OS. Patients were stratified based on Barcelona Clinic Liver Cancer (BCLC) and Cancer of the Liver Italian Program (CLIP). Binary ADC value (above vs. below the cutoff) and tumor margin (well- vs. ill-defined) were integrated into BCLC and CLIP. OS and TFS was compared for patients based on standard criteria with and without imaging biomarkers.

RESULTS

At baseline, patients with low tumor ADC and well-defined tumor margin (favorable imaging biomarkers) had longer survival, as compared to those with high ADC and ill-defined tumor margin (unfavorable imaging biomarkers) (median OS of 43 months vs. 7 months, respectively) (p < 0.001). Tumor ADC and tumor margin remained strong independent predictors of survival after adjustment for demographics, BCLC and CLIP staging, and tumor burden. Incorporating ADC and tumor margin improved performance of OS prediction by 9% in BCLC group and 6% in CLIP group.

CONCLUSION

Incorporating ADC and tumor margin to current staging systems for HCC significantly improve prediction of OS and TFS of these criteria.

KEY POINTS

• ADC and tumor margin are predictors of overall survival in HCC patients, independent of clinical, laboratory, and other imaging variables. • Adding ADC and tumor margin improved the prognostic value of BCLC and CLIP criteria by 9% and 6%, respectively. • High ADC and ill-defined tumor margin at baseline predicted poor survival, regardless of patient's liver function and general health status.

Value of T1 mapping on gadoxetic acid-enhanced MRI for microvascular invasion of hepatocellular carcinoma: a retrospective study

BACKGROUND

To evaluate the utility of non-invasive parameters derived from T1 mapping and diffusion-weighted imaging (DWI) on gadoxetic acid-enhanced MRI for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

METHODS

A total of 94 patients with single HCC undergoing partial hepatectomy was analyzed in this retrospective study. Preoperative T1 mapping and DWI on gadoxetic acid-enhanced MRI was performed. The parameters including precontrast, postcontrast and reduction rate of T1 relaxation time and apparent diffusion coefficient (ADC) values were measured for differentiating MVI-positive HCCs (n = 38) from MVI-negative HCCs (n = 56). The receiver operating characteristic curve (ROC) was analyzed to compare the diagnostic performance of the calculated parameters.

RESULTS

MVI-positive HCCs demonstrated a significantly lower reduction rate of T1 relaxation time than that of MVI-negative HCCs (39.4% vs 49.9, P < 0.001). The areas under receiver operating characteristic curve (AUC) were 0.587, 0.728, 0.824, 0,690 and 0.862 for the precontrast, postcontrast, reduction rate of T1 relaxation time, ADC and the combination of reduction rate and ADC, respectively. The cut-off value of the reduction rate and ADC calculated through maximal Youden index in ROC analyses was 44.9% and 1553.5 s/mm². To achieve a better diagnostic performance, the criteria of combining the reduction rate lower than 44.9% and the ADC value lower than 1553.5 s/mm² was proposed with a high specificity of 91.8% and accuracy of 80.9%.

CONCLUSIONS

The proposed criteria of combining the reduction rate of T1 relaxation time lower than 44.9% and the ADC value lower than 1553.5 s/mm² on gadoxetic acid-enhanced MRI holds promise for evaluating MVI status of HCC.

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.

Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation

OBJECTIVES

To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation.

METHODS

Our study consisted of a training dataset (n = 93) and a validation dataset (40) with clinically confirmed HCC after liver transplantation from October 2011 to December 2016. Radiomics features were extracted by delineating regions-of-interest (ROIs) around the lesion in four phases of CT images. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association between radiomics signature and recurrence-free survival (RFS) was assessed. Preoperative clinical characteristics potentially associated with RFS were evaluated to develop a clinical model. A combined model incorporating clinical risk factors and radiomics signature was built.

RESULTS

The stable radiomics features associated with the recurrence of HCC were simply found in arterial phase and portal phase. The prediction model based on the radiomics features extracted from the arterial phase showed better prediction performance than the portal vein phase or the fusion signature combining both of arterial and portal vein phase. A radiomics nomogram based on combined model consisting of the radiomics signature and clinical risk factors showed good predictive performance for RFS with a C-index of 0.785 (95% confidence interval [CI]: 0.674-0.895) in the training dataset and 0.789 (95% CI: 0.620-0.957) in the validation dataset. The calibration curves showed agreement in both training (p = 0.121) and validation cohorts (p = 0.164).

CONCLUSIONS

Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after liver transplantation.

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.

Comparison of values of CT and MRI imaging in the diagnosis of hepatocellular carcinoma and analysis of prognostic factors

Value of computed tomography (CT) and magnetic resonance imaging (MRI) in the diagnosis of small hepatocellular carcinoma (HCC), and in analysis of the prognostic factors of primary hepatocellular carcinoma (PHC) were compared. A total of 300 patients with PHC were selected from January 2013 to January 2016. Among them, 170 patients were diagnosed with small HCC. Patients were diagnosed by MRI and CT scans, respectively, and diagnostic efficacy of the methods was compared. A single factor and multivariate analysis of prognostic factors were performed on 300 patients. The sensitivity of MRI screening was 78.82%, specificity was 78.46%, accuracy was 78.67%, positive predictive value was 82.72%, and negative predictive value was 73.91%. CT screening showed a sensitivity of 62.35%, a specificity of 73.85%, an accuracy of 67.33%, a positive predictive value of 75.71%, and a negative predictive value of 60.00%. Differences in sensitivity, accuracy, and negative predictive value between MRI and CT screening were statistically significant (P<0.05). There was no statistically significant difference between two groups in specificity and positive predictive value (P>0.05). Diagnostic efficiency of MRI is better than that of CT diagnosis. Univariate analysis showed that age, hepatitis B cirrhosis background, tumor stage, and portal vein embolization were prognostic factors for PHC. Cox multivariate regression analysis showed that the background of liver cirrhosis, tumor stage, and portal thrombosis were independent risk factors for poor prognosis for PHC patient and the differences were statistically significant (P<0.05). MRI is superior to CT in the sensitivity, specificity and accuracy of the diagnosis of small HCC. Individualized comprehensive treatment plans based on the patient's condition may be effective in prolonging the patient's survival time. Imaging diagnosis can provide survival basis for patients, improve diagnostic accuracy, and help to improve the survival rate.

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.

A new laboratory-based algorithm to predict microvascular invasion and survival in patients with hepatocellular carcinoma

BACKGROUND

Preoperative serum inflammatory markers have been correlated with survival outcomes after resection of hepatocellular carcinoma (HCC). Whether they can predict microvascular invasion (MVI) in HCC is still unknown. This study aimed to evaluate the association of inflammatory markers with MVI, and develop a simple and inexpensive preoperative prediction model for MVI.

METHODS

We developed a novel index using routine laboratory tests to predict MVI. The index was developed based on a study on patients with HCC, and validated in an internal cohort and another external cohort. The infiltration of CD8⁺ T cells in tumors was measured using immunohistochemistry. The prediction accuracy was evaluated with the area under the receiver operating characteristic curve (AUC).

RESULTS

There were 165 patients in the training cohort, 107 patients in the internal validation cohort and 80 patients in the external validation cohort. On multivariable analysis in the training cohort, alkaline phosphatase (ALP) and lymphocyte count were independent predictors of MVI. Thus, the ALP-to-lymphocyte ratio (ALR) was developed. The AUCs of the ALR for MVI were higher than the other conventional clinical indices. An optimal cutoff point for the ALR of 69.9 stratified HCC patients into the high (≥69.9) and low (<69.9) groups. An ALR ≥69.9 was significantly associated with worse overall and disease-free survival outcomes. The performance of ALR was validated in the internal and in external cohorts. The CD8⁺ T cell counts were significantly higher in HCC in the ALR<69.9 groups.

CONCLUSION

ALR was a simple, accurate and inexpensive alternative to predict MVI and an independent risk factor of prognosis for HCC patients. The dismal survival outcomes in patients with high ALR scores were related to decreased infiltrations of CD8⁺ T cells in tumors.

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.

Microvascular invasion of single small hepatocellular carcinoma ≤3 cm: Predictors and optimal treatments

BACKGROUND

Small hepatocellular carcinomas (HCC ≤3 cm) are generally considered to have low malignant potential; however, some of them display pathological microvascular invasion (MVI).

METHODS

Between 1991 and 2013, 414 patients with a single HCC ≤3 cm underwent curative hepatic resection (HR). Predictors for MVI were identified. Using another cohort (149 patients during 2000-2014), our predictors for MVI in HCC ≤3 cm were validated. In 428 patients with a single HCC ≤3 cm who had predictors for MVI, survival was compared among anatomical HR (n = 149), partial HR (n = 227), and radiofrequency ablation (RFA) (n = 52).

RESULTS

The positive rate of MVI reached 40.6% (168/414 patients). Independent predictors for MVI were as follows: tumor diameter ≥2 cm (odds ratio 1.84, P = .0052), alpha-fetoprotein (AFP) ≥200 ng/mL (odds ratio 1.82, P = .0466), and des-gamma-carboxy prothrombin (DCP) ≥40 mAU/mL (odds ratio 1.79, P = .0126). Matching at least one predictor among these three could predict MVI in HCC ≤3 cm well (sensitivity 82.8%, positive predictive value [PPV] 48.7%). This criterion could also predict MVI in HCC ≤3 cm well in another cohort (sensitivity 82.8%, PPV 30.3%). In patients with single HCC ≤3 cm matching our criterion for predicting MVI, anatomical HR led to significantly better survival in both disease-free (hazard ratio 0.689, P = .0231) and overall (hazard ratio 0.589, P = .0316) survivals.

CONCLUSION

Matching at least one factor among three (tumor diameter ≥2 cm, AFP ≥200 ng/mL, or DCP ≥40 mAU/mL) can predict MVI in HCC ≤3 cm. In such patients, anatomical HR would be recommended to improve survival.

Intraoperative ultrasound for prediction of hepatocellular carcinoma biological behaviour: Prospective comparison with pathology

BACKGROUND & AIMS

Preoperative prediction of both microinvasive hepatocellular carcinoma and histological grade of hepatocellular carcinoma is pivotal to treatment planning and prognostication. The aim of this study was to evaluate whether some intraoperative ultrasound features correlate with both the presence of same histological patterns and differentiation grade of hepatocellular carcinoma on the histological features of the primary resected tumour.

METHODS

All patients with single, small hepatocellular carcinoma that underwent hepatic resection were included in this prospective double-blind study: the intraoperative ultrasound patterns of nodule were registered and compared with similar histological features.

RESULTS

A total of 179 patients were enclosed in this study: 97 (54%) patients (34% in HCC ≤2 cm) had a microinvasive hepatocellular carcinoma at ultrasound examination, while 82 (46%) patients (41% in HCC ≤2 cm) at histological evaluation. Statistical analysis showed that diameters ≤2 cm, presence of satellites and microinvasive hepatocellular carcinoma at ultrasound examination were the variables with the strongest association with the histological findings. In the multivariate analysis, the vascular microinfiltration and infiltrative hepatocellular carcinoma aspect were independent predictors for grading.

CONCLUSIONS

In patients with cirrhosis and hepatocellular carcinoma, the prevalence of microinvasive hepatocellular carcinoma is high, even in cases of HCC ≤2 cm. Intraoperative ultrasound findings strongly correlated with histopathological criteria in detecting microinvasive patterns and are useful to predict neoplastic differentiation. The knowledge of these features prior to treatment are highly desired (this can be obtained by an intraoperative ultrasound examination), as they could help in providing optimal management of patients with hepatocellular carcinoma.

Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Preoperative Gd-EOB-DTPA-Dynamic Enhanced MRI and Histopathological Correlation

Objective

To investigate the imaging features observed in preoperative Gd-EOB-DTPA-dynamic enhanced MRI and correlated with the presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.

Methods

66 HCCs in 60 patients with preoperative Gd-EOB-DTPA-dynamic enhanced MRI were retrospectively analyzed. Features including tumor size, signal homogeneity, tumor capsule, tumor margin, peritumor enhancement during mid-arterial phase, peritumor hypointensity during hepatobiliary phase, signal intensity ratio on DWI and apparent diffusion coefficients (ADC), T1 relaxation times, and the reduction rate between pre- and postcontrast enhancement images were assessed. Correlation between these features and histopathological presence of MVI was analyzed to establish a prediction model.

Results

Histopathology confirmed that MVI were observed in 17 of 66 HCCs. Univariate analysis showed tumor size (p = 0.003), margin (p = 0.013), peritumor enhancement (p = 0.001), and hypointensity during hepatobiliary phase (p = 0.004) were associated with MVI. A multiple logistic regression model was established, which showed tumor size, margin, and peritumor enhancement were combined predictors for the presence of MVI (α = 0.1). R² of this prediction model was 0.353, and the sensitivity and specificity were 52.9% and 93.0%, respectively.

Conclusion

Large tumor size, irregular tumor margin, and peritumor enhancement in preoperative Gd-EOB-DTPA-dynamic enhanced MRI can predict the presence of MVI in HCC.

Investigation of Association Between Borderline Pancreatic Head Cancer and Glucose Uptake by Using Positron-Emission Tomographic Studies

BACKGROUND In the background of the well-known importance of positron-emission tomographic studies (PET) in the prediction of pancreatic oncologic problems, we designed and performed this investigation to study the link between borderline pancreatic head cancer and glucose uptake by using PET. MATERIAL AND METHODS We retrospectively investigated patients during the period of almost 4 years (May 2013 to December 2016). Patients underwent potentially curative resection for borderline exocrine pancreatic head adenocarcinoma without undergoing neoadjuvant therapy. We divided our PET protocol into 2 sets of methods as per renal calyces: 1) U-RC type in which renal calyx (RC) has relatively higher value than that of 18F-fluoro-2-deoxyglucose (18F-FDG) uptake and 2) S-RC type in which renal calyx has similar value than that of 18F-FDG uptake. RESULTS A total of 67 patients were enrolled after reclassification on the basis of majority-agreement. Among these patients, U-RC type was found in 22 patients (32.8%) while S-RC type was found in 45 patients (67.2%). Significant statistical differences were observed for each of the 2 types of pancreatic head cancer (U-RC type and S-RC type) in terms of adjusted cancer antigen 19-9 (CA 19-9), size of the tumor, tumor volume (TV2.8), maximum standard uptake value (SUV↑), and lesion glycolysis (LG). A significantly longer disease-free survival time was shown by U-RC type (n=18) pancreatic cancer in comparison to S-RC type (n=42) (25.3 vs. 11.2 months). Additionally, U-RC type (n=4) had higher disease-free survival than did aS-RC type (n=3) (29.4 vs. 12.5 months). CONCLUSIONS Our PET protocol appears to be an indicator for estimation of recurrence of pancreatic head cancer and is as an indispensable asset to oncologists.

How to Differentiate Borderline Hepatic Nodules in Hepatocarcinogenesis: Emphasis on Imaging Diagnosis

BACKGROUND

Rapid advances in liver imaging have improved the evaluation of hepatocarcinogenesis and early diagnosis and treatment of hepatocellular carcinoma (HCC). In this situation, detection of early-stage HCC in its development is important for the improvement of patient survival and optimal treatment strategies. Because early HCCs are considered precursors of progressed HCC, precise differentiation between a dysplastic nodule (DN), especially a high-grade DN, and early HCC is important. In clinical practice, these nodules are frequently called "borderline hepatic nodules."

SUMMARY

This article discusses radiological and pathological characteristics of these borderline hepatic nodules and offers an understanding of multistep hepatocarcinogenesis by focusing on the descriptions of the imaging changes in the progression of DN and early HCC. Detection and accurate diagnosis of borderline hepatic nodules are still a challenge with contrast enhanced ultrasonography, CT, and MRI with extracellular contrast agents. However, gadoxetic acid-enhanced MRI may be useful for improving the diagnosis of these borderline nodules.

KEY MESSAGES

Since there is a net effect of incomplete neoangiogenesis and decreased portal venous flow in the early stage of hepatocarcinogenesis, borderline hepatic nodules commonly show iso- or hypovascularity. Therefore, precise differentiation of these nodules remains a challenging issue. In MRI using hepatobiliary contrast agents, signal intensity of HCCs on hepatobiliary phase (HBP) is regarded as a potential imaging biomarker. Borderline hepatic nodules are seen as nonhypervascular and hypointense nodules on the HBP, which is important for predicting tumor behavior and determining appropriate therapeutic strategies.

Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma

PURPOSE

Microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) cannot be accurately predicted preoperatively. This study aimed to establish a predictive scoring model of MVI in solitary HCC patients without macroscopic vascular invasion.

METHODS

A total of 309 consecutive HCC patients who underwent curative hepatectomy were divided into the derivation (n=206) and validation cohort (n=103). A predictive scoring model of MVI was established according to the valuable predictors in the derivation cohort based on multivariate logistic regression analysis. The performance of the predictive model was evaluated in the derivation and validation cohorts.

RESULTS

Preoperative imaging features on CECT, such as intratumoral arteries, non-nodular type of HCC and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model was established according to the β coefficients of the 3 predictors. Area under receiver operating characteristic (AUROC) of the predictive scoring model was 0.872 (95% CI, 0.817-0.928) and 0.856 (95% CI, 0.771-0.940) in the derivation and validation cohorts. The positive and negative predictive values were 76.5% and 88.0% in the derivation cohort and 74.4% and 88.3% in the validation cohort. The performance of the model was similar between the patients with tumor size ≤5cm and >5cm in AUROC (P=0.910).

CONCLUSIONS

The predictive scoring model based on intratumoral arteries, non-nodular type of HCC, and absence of the radiological tumor capsule on preoperative CECT is of great value in the prediction of MVI regardless of tumor size.

Functional imaging in liver tumours

Functional imaging encompasses techniques capable of assessing physiological parameters of tissues, and offers useful clinical information in addition to that obtained from morphological imaging. Such techniques may include magnetic resonance imaging with diffusion-weighted sequences or hepatobiliary contrast agents, perfusion imaging, or molecular imaging with radiolabelled tracers. The liver is of major importance in oncological practice; not only is hepatocellular carcinoma one of the malignancies with steadily rising incidence worldwide, but hepatic metastases are regularly observed with a range of solid neoplasms. Within the realm of hepatic oncology, different functional imaging modalities may occupy pivotal roles in lesion characterisation, treatment selection and follow-up, depending on tumour size and type. In this review, we characterise the major forms of functional imaging, discuss their current application to the management of patients with common primary and secondary liver tumours, and anticipate future developments within this field.

Microvascular invasion in hepatocellular carcinoma: is it predictable with a new, preoperative application of diffusion-weighted imaging?

PURPOSE

The study aimed to explore the use of MRI in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

The preoperative MRI and tissues of resected HCC patients were collected. The imaging characteristics that have previously been suggested and the mismatch between diffusion-weighted imaging (DWI) and T2-weighted imaging of regions, which the authors called DWI/T2 mismatch, were analyzed and compared with histopathological references.

RESULTS

A multivariate logistic regression analysis showed that DWI/T2 mismatch was an independent predictor of MVI.

CONCLUSION

The DWI/T2 mismatch can be a preoperative predictor of MVI for HCC.

Clinical implications for imaging of vascular invasion in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the second largest cause of cancer mortality in the world, with vascular invasion being one of the most important prognostic factors. HCC with tumor thrombus was traditionally considered to have very limited treatment options. However, multiple promising treatment strategies have emerged in recent years, with diagnostic and interventional radiologists playing a major role in patient management. We provide a comprehensive update on the diagnosis and management of HCC with vascular invasion and the role of the radiologist in this condition.

Magnetic resonance imaging with gadoxetic acid for local tumour progression after radiofrequency ablation in patients with hepatocellular carcinoma

OBJECTIVES

To develop and validate a prediction model using magnetic resonance imaging (MRI) for local tumour progression (LTP) after radiofrequency ablation (RFA) in hepatocellular carcinoma (HCC) patients.

METHODS

Two hundred and eleven patients who had received RFA as first-line treatment for HCC were retrospectively analyzed. They had undergone gadoxetic acid-enhanced MRI before treatment, and parameters including tumour size; margins; signal intensities on T1-, T2-, and diffusion-weighted images, and hepatobiliary phase images (HBPI); intratumoral fat or tumoral capsules; and peritumoural hypointensity in the HBPI were used to develop a prediction model for LTP after treatment. This model to discriminate low-risk from high-risk LTP groups was constructed based on Cox regression analysis.

RESULTS

Our analyses produced the following model: 'risk score = 0.617 × tumour size + 0.965 × tumour margin + 0.867 × peritumoural hypointensity on HBPI'. This was able to predict which patients were at high risk for LTP after RFA (p < 0.001). Patients in the low-risk group had a significantly better 5-year LTP-free survival rate compared to the high-risk group (89.6 % vs. 65.1 %; hazard ratio, 3.60; p < 0.001).

CONCLUSION

A predictive model based on MRI before RFA could robustly identify HCC patients at high risk for LTP after treatment.

KEY POINTS

• Tumour size, margin, and peritumoural hypointensity on HBPI were risk factors for LTP. • The risk score model can predict which patients are at high risk for LTP. • This prediction model could be helpful for risk stratification of HCC patients.

Differentiation of small (≤2 cm) hepatocellular carcinomas from small benign nodules in cirrhotic liver on gadoxetic acid-enhanced and diffusion-weighted magnetic resonance images

OBJECTIVE

To identify imaging characteristics that differentiate small (≤2 cm) HCCs from small (≤2 cm) benign nodules in cirrhotic liver on gadoxetic acid-enhanced and diffusion-weighted (DW) magnetic resonance (MR) images.

MATERIALS AND METHODS

On gadoxetic acid-enhanced and DW MR images, we analysed signal intensity of 222 small HCCs and 61 benign nodules (diameter, 0.5-2 cm) at each sequence and rim enhancement during portal or equilibrium phases. Univariate and multivariate logistic regression analyses identified predictors of HCC. Combinations of significant MR findings in multivariate analysis were compared with American Association for the Study of Liver Disease (AASLD) practice guidelines.

RESULTS

In multivariate analysis, arterial enhancement (adjusted odds ratio [aOR], 8.6), T2 hyperintensity (aOR, 5.8), and hyperintensity on DW images (aOR, 3.8) were significant for differentiating small HCCs from benign nodules (p ≤ 0.004). When two or all three findings were applied as diagnostic criteria for differentiating small HCCs from benign nodules, sensitivity and accuracy were significantly higher compared with AASLD practice guidelines (91% vs. 78% and 89% vs. 81%, respectively; each p < 0.0001).

CONCLUSION

On gadoxetic acid-enhanced MR imaging, arterial enhancement and hyperintensity on T2-weighted and DW MR images are helpful for differentiating small HCCs from benign nodules in liver cirrhosis.

Diagnosing Borderline Hepatic Nodules in Hepatocarcinogenesis: Imaging Performance

OBJECTIVE

The purposes of this article are to describe the pathologic and radiologic features of small nodular lesions and to offer insight into the multistep process of hepatocarcinogenesis by describing the progression of imaging changes that link dysplastic nodules and early hepatocellular carcinoma, (HCC) to small HCC that has progressed.

CONCLUSION

Nodules larger than 1 cm found during ultrasound surveillance of a cirrhotic liver should be investigated further with diagnostic imaging. Contrast-enhanced CT and dynamic MRI are the primary diagnostic studies for the diagnosis of HCC; contrast-enhanced ultrasound can be used as an alternative test. If a nodule has the typical hallmark of hypervascularity in the hepatic arterial phase with washout in the portal venous or delayed phase, a definitive diagnosis of HCC can be made. Nodules found during ultrasound surveillance that are smaller than 1 cm can be followed with ultrasound examinations at intervals of 3-6 months.

Focal liver lesions: Practical magnetic resonance imaging approach

With the widespread of cross-sectional imaging, a growth of incidentally detected focal liver lesions (FLL) has been observed. A reliable detection and characterization of FLL is critical for optimal patient management. Maximizing accuracy of imaging in the context of FLL is paramount in avoiding unnecessary biopsies, which may result in post-procedural complications. A tremendous development of new imaging techniques has taken place during these last years. Nowadays, Magnetic resonance imaging (MRI) plays a key role in management of liver lesions, using a radiation-free technique and a safe contrast agent profile. MRI plays a key role in the non-invasive correct characterization of FLL. MRI is capable of providing comprehensive and highly accurate diagnostic information, with the additional advantage of lack of harmful ionizing radiation. These properties make MRI the mainstay for the noninvasive evaluation of focal liver lesions. In this paper we review the state-of-the-art MRI liver protocol, briefly discussing different sequence types, the unique characteristics of imaging non-cooperative patients and discuss the role of hepatocyte-specific contrast agents. A review of the imaging features of the most common benign and malignant FLL is presented, supplemented by a schematic representation of a simplistic practical approach on MRI.

MRI features of hepatocellular carcinoma related to biologic behavior

Imaging studies including magnetic resonance imaging (MRI) play a crucial role in the diagnosis and staging of hepatocellular carcinoma (HCC). Several recent studies reveal a large number of MRI features related to the prognosis of HCC. In this review, we discuss various MRI features of HCC and their implications for the diagnosis and prognosis as imaging biomarkers. As a whole, the favorable MRI findings of HCC are small size, encapsulation, intralesional fat, high apparent diffusion coefficient (ADC) value, and smooth margins or hyperintensity on the hepatobiliary phase of gadoxetic acid-enhanced MRI. Unfavorable findings include large size, multifocality, low ADC value, non-smooth margins or hypointensity on hepatobiliary phase images. MRI findings are potential imaging biomarkers in patients with HCC.

CT and MR imaging diagnosis and staging of hepatocellular carcinoma: part II. Extracellular agents, hepatobiliary agents, and ancillary imaging features

Computed tomography (CT) and magnetic resonance (MR) imaging play critical roles in the diagnosis and staging of hepatocellular carcinoma (HCC). The second article of this two-part review discusses basic concepts of diagnosis and staging, reviews the diagnostic performance of CT and MR imaging with extracellular contrast agents and of MR imaging with hepatobiliary contrast agents, and examines in depth the major and ancillary imaging features used in the diagnosis and characterization of HCC.

Hepatocellular carcinoma: diagnostic criteria by imaging techniques

Imaging plays a very important role in the diagnosis of HCC. Indeed, in high-risk patients a noninvasive diagnosis can only be obtained by imaging in presence of typical features. These features include arterial enhancement followed by washout during the portal venous and/or delayed phases on CT scan or MRI. This pattern is quite specific and has been endorsed by both Western and Asian diagnostic guidelines. However, its sensitivity is not very high, especially for small lesions. Therefore ancillary signs may be needed to increase the reliability of the diagnosis. Recent hepatobiliary MRI contrast agents seem to be interesting to improve characterization of small nodules in the cirrhotic liver.

Microvascular invasion in small hepatocellular carcinoma: is it predictable with preoperative diffusion-weighted imaging?

BACKGROUND AND AIM

The presence of microvascular invasion (MVI) is an independent risk factor affecting recurrence-free survival following surgical treatment for small hepatocellular carcinoma (HCC). Our aim in this study was to investigate whether diffusion-weighted imaging (DWI) could be useful in predicting MVI for small HCC.

METHODS

Breath-hold DWI (b-value 0, 500 s/mm(2) ) and gadopentate dimeglumine-enhanced dynamic imaging of preoperative magnetic resonance imaging of 109 surgically proven small HCCs from 92 patients were retrospectively analyzed. The signal intensity ratio on DWI and apparent diffusion coefficients (ADCs) for lesions were quantitatively measured. Signal intensity ratio and ADC of DWI, tumor size, tumor shape, tumor capsule, peritumoral enhancement on arterial phase images, and dynamic enhancement pattern were analyzed as radiological parameters reflecting MVI and were compared with histopathological references. The chi-square test, Fisher's exact test, Mann-Whitney U test, and the independent t-test were used for univariate analysis. To identify the independent predictors of MVI among these radiological parameters and to evaluate their diagnostic performance, multivariate logistic regression analysis and receiver operating characteristic curve analysis were performed, respectively.

RESULTS

A univariate analysis showed that a lower ADC value (P = 0.005) and irregular circumferential enhancement (P = 0.020) showed statistically significant associations with MVI. A multiple logistic regression analysis showed that the ADC value and irregular circumferential enhancement were independent predictors of MVI. With a cut-off of 1.227 × 10(-3)  mm(2) /s, the ADC value provided a sensitivity of 66.7% and a specificity of 78.6% in the prediction of MVI with an odds ratio of 7.63 (P < 0.01).

CONCLUSIONS

Lower ADC values (< 1.227 × 10(-3)  mm(2) /s) on DWI with b-value of 0.500 s/mm(2) can be a useful preoperative predictor of MVI for small HCCs.