Predictive Value of Contrast-enhanced Ultrasound for Early Recurrence of Single Lesion Hepatocellular Carcinoma After Curative Resection

The value of contrast-enhanced ultrasound combined with microflow imaging in predicting microvascular invasion of hepatocellular carcinoma before operation

OBJECTIVE

To evaluate the preoperative predictive value of contrast-enhanced ultrasound (CEUS) combined with microflow imaging (MFI) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

METHODS

In our study, 80 patients with HCC were analyzed retrospectively. According to the gold standard of postoperative pathology, the patients were divided into MVI positive group (n = 39) and MVI negative group (n = 41). we were to analyze the correlation between CEUS and MVI in combination with MFI, to identify independent risk factors for the occurrence of MVI positive, and to analyze the predictive efficacy of every independent risk factor and their combination in preoperative prediction of MVI.

RESULTS

In our study, 80 patients were enrolled, including 39 patients in the MVI-positive group and 41 patients in the MVI-negative group, with a MVI-positive rate of 48.8%. By univariate analysis and multivariate analysis, it was found that there were statistically significant differences in enhancement range extension, start time of wash out and CEUS-MFI between the two groups, which were independent risk factors for MVI-positive. The combination of three independent risk factors is more effective than single one in predicting MVI of HCC.

CONCLUSIONS

CEUS combined with MFI is feasible for the preoperative prediction of MVI in HCC, and can provides meaningful help for individualized clinical treatment.

Contrast enhanced ultrasound combined with serology predicts hepatocellular carcinoma recurrence: a retrospective observation cohort study

Objectives

To construct a novel model based on contrast-enhanced ultrasound (CEUS) and serological biomarkers to predict the early recurrence (ER) of primary hepatocellular carcinoma within 2 years after hepatectomy.

Methods

A total of 466 patients who underwent CEUS and curative resection between 2016.1.1 and 2019.1.1 were retrospectively recruited from one institution. The training and testing cohorts comprised 326 and 140 patients, respectively. Data on general characteristics, CEUS Liver Imaging Reporting and Data System (LI-RADS) parameters, and serological were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence, and the Contrast-enhanced Ultrasound Serological (CEUSS) model was constructed. Different models were compared using prediction error and time-dependent area under the receiver operating characteristic curve (AUC). The CEUSS model's performances in ER prediction were assessed.

Results

The baseline data of the training and testing cohorts were equal. LI-RADS category, α-fetoprotein level, tumor maximum diameter, total bilirubin level, starting time, iso-time, and enhancement pattern were independent hazards, and their hazards ratios were 1.417, 1.309, 1.133, 1.036, 0.883, 0.985, and 0.70, respectively. The AUCs of CEUSS, BCLC,TNM, and CNLC were 0.706, 0.641, 0.647, and 0.636, respectively, in the training cohort and 0.680, 0.583, 0.607, and 0.597, respectively, in the testing cohort. The prediction errors of CEUSS, BCLC, TNM, and CNLC were 0.202, 0.205, 0.205, and 0.200, respectively, in the training cohort and 0.204, 0.221, 0.219, and 0.211, respectively, in the testing cohort.

Conclusions

The CEUSS model can accurately and individually predict ER before surgery and may represent a new tool for individualized treatment.

Prediction of early recurrence of HCC after hepatectomy by contrast-enhanced ultrasound-based deep learning radiomics

Objective

This study aims to evaluate the predictive model based on deep learning (DL) and radiomics features from contrast-enhanced ultrasound (CEUS) to predict early recurrence (ER) in patients with hepatocellular carcinoma (HCC).

Methods

One hundred seventy-two patients with HCC who underwent hepatectomy and followed up for at least 1 year were included in this retrospective study. The data were divided according to the 7:3 ratios of training and test data. The ResNet-50 architecture, CEUS-based radiomics, and the combined model were used to predict the early recurrence of HCC after hepatectomy. The receiver operating characteristic (ROC) curve and calibration curve were drawn to evaluate its diagnostic efficiency.

Results

The CEUS-based radiomics ROCs of the “training set” and “test set” were 0.774 and 0.763, respectively. The DL model showed increased prognostic value, the ROCs of the “training set” and “test set” were 0.885 and 0.834, respectively. The combined model ROCs of the “training set” and “test set” were 0.943 and 0.882, respectively.

Conclusion

The deep learning radiomics model integrating DL and radiomics features from CEUS was used to predict ER and achieve satisfactory performance. Its diagnostic efficiency is significantly better than that of the single model.

Contrast-enhanced ultrasound-based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation

OBJECTIVES

This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation.

METHODS

This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort (n = 139) and a test cohort (n = 76). Four representative images-grayscale ultrasound, arterial phase, portal venous phase and delayed phase-were extracted from each CEUS video. Ultrasomics features were extracted from tumoral and peritumoral area inside the region of interest. Logistic regression was used to establish models, including a tumoral model, a peritumoral model and a combined model with additional clinical risk factors. The performance of the three models in predicting recurrence within 2 years was verified.

RESULTS

The combined model performed best in predicting recurrence within 2 years, with an area under the curve (AUC) of 0.845, while the tumoral model had an AUC of 0.810 and the peritumoral model one of 0.808. For prediction of recurrence-free survival, the 2-year cumulative recurrence rate was significant higher in the high-risk group (76.5%) than in the low-risk group (9.5%; p < 0.0001).

CONCLUSION

These CEUS ultrasomics models, especially the combined model, had good efficacy in predicting early recurrence of HCC. The combined model has potential for individual survival assessment for HCC patients undergoing resection or ablation.

ADVANCES IN KNOWLEDGE

CEUS ultrasomics had high sensitivity, specificity and PPV in diagnosing early recurrence of HCC, and high efficacy in predicting early recurrence of HCC (AUC > 0.8). The combined model performed better than the tumoral ultrasomics model and peritumoral ultrasomics model in predicting recurrence within 2 years. Recurrence was more likely to occur in the high-risk group than in the low-risk group, with 2-year cumulative recurrence rates, respectively, 76.5% and 9.5% (p < 0.0001).

Novel model combining contrast-enhanced ultrasound with serology predicts hepatocellular carcinoma recurrence after hepatectomy

BACKGROUND

Surgery is the primary curative option in patients with hepatocellular carcinoma (HCC). However, recurrence within 2 years is observed in 30%–50% of patients, being a major cause of mortality.

AIM

To construct and verify a non-invasive prediction model combining contrast-enhanced ultrasound (CEUS) with serology biomarkers to predict the early recurrence of HCC.

METHODS

Records of 744 consecutive patients undergoing first-line curative surgery for HCC in one institution from 2016–2018 were reviewed, and 292 local patients were selected for analysis. General characteristics including gender and age, CEUS liver imaging reporting and data system (LIRADS) parameters including wash-in time, wash-in type, wash-out time, and wash-out type, and serology biomarkers including alanine aminotransferase, aspartate aminotransferase, platelets, and alpha-fetoprotein (AFP) were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence. Then a nomogram called CEUS model was constructed. The CEUS model was then used to predict recurrence at 6 mo, 12 mo, and 24 mo, the cut-off value was calculate by X-tile, and each C-index was calculated. Then Kaplan-Meier curve was compared by log-rank test. The calibration curves of each time were depicted.

RESULTS

A nomogram predicting early recurrence (ER), named CEUS model, was formulated based on the results of the multivariate Cox regression analysis. This nomogram incorporated tumor diameter, preoperative AFP level, and LIRADS, and the hazard ratio was 1.123 (95% confidence interval [CI]: 1.041-1.211), 1.547 (95%CI: 1.245-1.922), and 1.428 (95%CI: 1.059-1.925), respectively. The cut-off value at 6 mo, 12 mo, and 24 mo was 100, 80, and 50, and the C-index was 0.748 (95%CI: 0.683-0.813), 0.762 (95%CI: 0.704-0.820), and 0.762 (95%CI: 0.706-0.819), respectively. The model showed satisfactory results, and the calibration at 6 mo was desirable; however, the calibration at 12 and 24 mo should be improved.

CONCLUSION

The CEUS model enables the well-calibrated individualized prediction of ER before surgery and may represent a novel tool for biomarker research and individual counseling.

The value of contrast-enhanced ultrasound in predicting postoperative recurrence of hepatocellular carcinoma: A protocol for systematic review and meta-analysis

BACKGROUND

As one of the key factors, postoperative recurrence of hepatocellular carcinoma (HCC) influences the therapeutic effects and survival period of patients. Therefore, the early diagnosis of postoperative recurrence of HCC plays an important role in improving the therapeutic effects and prognosis. Contrast-enhanced ultrasound (CEUS) plays an important role in the early diagnosis of postoperative recurrence of HCC. However, the accuracy of CEUS in predicting postoperative recurrence of HCC is still controversial. Therefore, in this study, a meta-analysis was carried out to further evaluate the accuracy of CEUS in predicting postoperative recurrence of HCC, thus providing evidence support for the early diagnosis of HCC.

METHODS

The literatures on the CEUS diagnosis of postoperative recurrence of HCC were collected by searching China National Knowledge Infrastructure, Wanfang, China Biology Medicine disc, PubMed, EMBASE, Cochrane Library, and Web of Science on computer. The retrieval time is set from the start of the database until April 2021. The meta-analysis of the literatures that meet the quality standards was conducted by Stata 16.0 software.

RESULTS

The results of this meta-analysis will be submitted to a peer-reviewed journal for publication.

CONCLUSION

This study will provide evidence support for the accuracy of CEUS in the diagnosis of postoperative recurrence of HCC.

ETHICS AND DISSEMINATION

The private information from individuals will not be published. This systematic review also should not damage participants' rights. Ethical approval is not available. The results may be published in a peer-reviewed journal or disseminated in relevant conferences.

OSF REGISTRATION NUMBER

DOI 10.17605/OSF.IO/HB46W.

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.