A nomogram based on multi-modal ultrasound for prediction of microvascular invasion and recurrence of hepatocellular carcinoma

An Accurate Model for Microvascular Invasion Prediction in Solitary Hepatocellular Carcinoma ≤5 cm Based on CEUS and EOB-MRI: A Retrospective Study with External Validation

RATIONALE AND OBJECTIVES

To develop a model combining contrast-enhanced ultrasound (CEUS) and ethoxybenzyl-enhanced magnetic resonance imaging (EOB-MRI) for predicting microvascular invasion (MVI) in solitary hepatocellular carcinoma (HCC) ≤5 cm.

MATERIALS AND METHODS

Patients between December 2019 and May 2024 in one center were retrospectively enrolled and randomly divided into the training cohort and internal validation cohort in a ratio of 7:3. Patients in a separate center were enrolled between January 2022 and December 2023 to be included as the external validation cohort. CEUS and EOB-MRI image features were extracted and used to develop models in the training cohort, and verified in the two validation cohorts. The predictive accuracy and clinical utility of models were evaluated using area under receiver operating characteristic curve (AUROC), Brier score, calibration plot and decision curve analysis (DCA). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to compare different models.

RESULTS

From the two centers a total of 493 patients, of which 134 were MVI positive, were evaluated. The CEUS+EOB model included seven image features and showed better discrimination ability than the individual CEUS/EOB-MRI model, with AUROCs of 0.92, 0.94, and 0.90 in the training cohort and two validation cohorts, respectively (p<0.05). The lowest Brier score of the combined model indicated the highest predictive precision. DCA also showed that the combined model added more net benefits. Both the NRI and IDI values >0 indicated that the combined model had significantly positive improvement (p<0.05).

CONCLUSION

The CEUS+EOB model was developed to assist clinicians in evaluating MVI in solitary HCC ≤5 cm.

Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation

OBJECTIVE

To preoperatively predict microvascular invasion (MVI) and relapse-free survival (RFS) in hepatocellular carcinoma (HCC) ≥3 cm by constructing and externally validating a combined radiomics model using preoperative enhanced CT images.

METHODS

This retrospective study recruited adults who underwent surgical resection between September 2016 and August 2020 in our hospital with pathologic confirmation of HCC ≥3 cm and MVI status. For external validation, adults who underwent surgical resection between September 2020 and August 2021 in our hospital were included. Histopathology was the reference standard. The HCC area was segmented on the arterial and portal venous phase CT images to develop a CT radiomics model. A combined model was developed using selected radiomics features, demographic information, laboratory index and radiological features. Analysis of variance and support vector machine were used as features selector and classifier. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were used to evaluate models' performance. The Kaplan-Meier method and log-rank test were used to evaluate the predictive value for RFS.

RESULTS

A total of 202 patients were finally enrolled (median age, 59 years, 173 male). Thirteen and 24 features were selected for the CT radiomics model and the combined model, and the area under the ROC curves (AUC) were 0.752 (95 %CI 0.615, 0.889) and 0.890 (95 %CI 0.794, 0.985) in the external validation set, respectively. Calibration curves and DCA showed a higher net clinical benefit of the combined model. The high-risk group (P < 0.001) was an independent predictor for RFS.

CONCLUSIONS

The combined model showed high accuracy for preoperatively predicting MVI and RFS in HCC ≥3 cm.

Advances in multi-omics studies of microvascular invasion in hepatocellular carcinoma

Microvascular invasion (MVI) represents a pivotal independent prognostic factor for the recurrence of hepatocellular carcinoma (HCC) after surgery. It contributes to early intervention for potentially recurrent HCC to enhance patient outcomes and increase survival rates. Traditionally, the diagnosis of MVI has relied on postoperative pathological analysis, and accurate preoperative detection methodologies are lacking. Recent research suggests that multi-omics strategies play a role in definitively diagnosing MVI before surgery and offering personalized selection for clinical decision-making in HCC management. This review meticulously examines a multi-omics approach for the preoperative prediction of MVI in HCC patients, aiming to innovate diagnostic paradigms to anticipate postsurgical recurrence, thereby facilitating earlier and more personalized therapeutic strategies.

Construction of a nomogram combining CEUS and MRI imaging for preoperative diagnosis of microvascular invasion in hepatocellular carcinoma

Purpose

To use Sonazoid contrast-enhanced ultrasound (S-CEUS) and Gadolinium-Ethoxybenzyl-Diethylenetriamine Penta-Acetic Acid magnetic-resonance imaging (EOB-MRI), exploring a non-invasive preoperative diagnostic strategy for microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

Methods

111 newly developed HCC cases were retrospectively collected. Both S-CEUS and EOB-MRI examinations were performed within one month of hepatectomy. The following indicators were investigated: size; vascularity in three phases of S-CEUS; margin, signal intensity, and peritumoral wedge shape in EOB-MRI; tumoral homogeneity, presence and integrity of the tumoral capsule in S-CEUS or EOB-MRI; presence of branching enhancement in S-CEUS; baseline clinical and serological data. The least absolute shrinkage and selection operator regression and multivariate logistic regression analysis were applied to optimize feature selection for the model. A nomogram for MVI was developed and verified by bootstrap resampling.

Results

Of the 16 variables we included, wedge and margin in HBP of EOB-MRI, capsule integrity in AP or HBP/PVP images of EOB-MRI/S-CEUS, and branching enhancement in AP of S-CEUS were identified as independent risk factors for MVI and incorporated into construction of the nomogram. The nomogram achieved an excellent diagnostic efficiency with an area under the curve of 0.8434 for full data training set and 0.7925 for bootstrapping validation set for 500 repetitions. In evaluating the nomogram, Hosmer-Lemeshow test for training set exhibited a good model fit with P > 0.05. Decision curve analysis of nomogram model yielded excellent clinical net benefit with a wide range (5-80 % and 85-94 %) of risk threshold.

Conclusions

The MVI Nomogram established in this study may provide a strategy for optimizing the preoperative diagnosis of MVI, which in turn may improve the treatment and prognosis of MVI-related HCC.

Prediction of microvascular invasion in hepatocellular carcinoma with conventional ultrasound, Sonazoid-enhanced ultrasound, and biochemical indicator: a multicenter study

PURPOSE

To develop and validate a preoperative prediction model based on multimodal ultrasound and biochemical indicator for identifying microvascular invasion (MVI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.

METHODS

From May 2022 to November 2023, a total of 318 patients with pathologically confirmed single HCC ≤ 5 cm from three institutions were enrolled. All of them underwent preoperative biochemical, conventional ultrasound (US), and contrast-enhanced ultrasound (CEUS) (Sonazoid, 0.6 mL, bolus injection) examinations. Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. The models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis in both validation and test sets. Subgroup analyses in patients with different liver background and tumor sizes were conducted to further investigate the model's performance.

RESULTS

Logistic regression analyses showed that obscure tumor boundary in B-mode US, intra-tumoral artery in pulsed-wave Doppler US, complete Kupffer-phase agent clearance in Sonazoid-CEUS, and biomedical indicator PIVKA-II were independently correlated with MVI-positive. The combined model comprising all predictors showed the highest AUC, which were 0.937 and 0.893 in the validation and test sets. Good calibration and prominent net benefit were achieved in both sets. No significant difference was found in subgroup analyses.

CONCLUSIONS

The combination of biochemical indicator, conventional US, and Sonazoid-CEUS features could help preoperative MVI prediction in patients with a single HCC ≤ 5 cm.

CRITICAL RELEVANCE STATEMENT

Investigation of imaging features in conventional US, Sonazoid-CEUS, and biochemical indicators showed a significant relation with MVI-positivity in patients with a single HCC ≤ 5 cm, allowing the construction of a model for preoperative prediction of MVI status to help treatment decision making.

KEY POINTS

MVI status is important for patients with a single HCC ≤ 5 cm. The model based on conventional US, Sonazoid-CEUS and PIVKA-II performs best for MVI prediction. The combined model has potential for preoperative prediction of MVI status.

A Grading System of Microvascular Invasion for Patients with Hepatocellular Carcinoma Undergoing Liver Resection with Curative Intent: A Multicenter Study

Background

Microvascular invasion (MVI) is closely correlated with poor clinical outcomes in patients with hepatocellular carcinoma (HCC). A grading system of MVI is needed to assist in the management of HCC patient.

Methods

Multicenter data of HCC patients who underwent liver resection with curative intent was analyzed. This grading system was established by detected number and distance from tumor boundary of MVI. Survival outcomes were compared among patients in each group. This system was verified by time-receiver operating characteristic curve, time-area under the curve, calibration curve, and decision curve analyses. Cox regression analysis was performed to study the associated factors of prognosis. Logistic analysis was used to study the predictive factors of MVI.

Results

All patients were classified into 4 groups: M0: no MVI; M1: 1~5 proximal MVIs (≤1 cm from tumor boundary); M2a: >5 proximal MVIs (≤1 cm from tumor boundary); M2b: ≥1 distal MVIs (>1 cm from tumor boundary). The recurrence-free survival (RFS), overall survival (OS), and early RFS rates among all the individual groups were significantly different. Based on the number of proximal MVI (0~5 vs >5), patients in the M2b group were further divided into two subgroups which also showed different prognosis. Multiple methods showed this grading system to be significantly better than the MVI two-tiered system in prognostic evaluation. Four multivariate models for RFS, OS, early RFS, late RFS, and a predictive model of MVI were then established and were shown to satisfactorily evaluate prognosis and have a great discriminatory power, respectively.

Conclusion

This MVI grading system could precisely evaluate prognosis of HCC patients after liver resection with curative intent and it could be employed in routine pathological reports. The severity of MVI from both adjacent and distant from tumor boundary should be stated. A hypothesis about two occurrence modes of distal MVI was proposed.

Perfluorobutane application value in microwave ablation of small hepatocellular carcinoma (<3 cm)

BACKGROUND

No studies have been retrieved comparing perfluorobutane with sulfur hexafluoride for microwave ablation (MWA) in small hepatocellular carcinoma(sHCC).

OBJECTIVE

To retrospective investigate the value of perfluorobutane ultrasonography contrast agent in ultrasonography (US)-guided MWA of sHCC.

METHODS

We conducted a retrospective clinical controlled study about US-guided percutaneous MWA in patients with sHCC, and in patients undergoing intra-operative treatment with perfluorobutane or sulfur hexafluoride. In both groups, a contrast agent was injected to clear the tumor and then a needle was inserted. A 5-point needle prick difficulty score was developed to compare needle prick difficulty in the two groups of cases.

RESULTS

A total of 67 patients were included: 25 patients in group perfluorobutane, aged 41-82 (60.64±9.46), tumor size 1.1-2.8 (1.78±0.45) cm. 42 patients in group sulfur hexafluoride, aged 38-78 (62.26±9.27), with tumor size of 1.1-3.0 (1.89±0.49) cm. There was no significant difference in age or tumor size in both groups (P > 0.05). Puncture difficulty score (5-point): 2.0-2.7 (2.28±0.29) in group perfluorobutane, and 2.0-4.7 (2.95±0.85) in group sulfur hexafluoride, and the difference between the two groups was statistically significant (P < 0.05). Enhanced imaging results within 3 months after surgery: complete ablation rate was 100% (25/25) in the group perfluorobutane, 95.2% (40/42 in the group sulfur hexafluoride), with no significant difference between the two groups (P > 0.05).

CONCLUSION

Perfluorobutane kupffer phase can make the operator accurately deploy the ablation needle and reduce the difficulty of operation.

Application and prospects of AI-based radiomics in ultrasound diagnosis

Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.

Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma

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

Stiffness on shear wave elastography as a potential microenvironment biomarker for predicting tumor recurrence in HBV-related hepatocellular carcinoma

BACKGROUND

To explore the pathologic basis and prognostic value of tumor and liver stiffness measured pre-operatively by two-dimensional shear wave elastography (2D-SWE) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients who undergo hepatic resection.

METHODS

A total of 191 HBV-infected patients with solitary resectable HCC were prospectively enrolled. The stiffness of intratumoral tissue, peritumoral tissue, adjacent liver tissue, and distant liver tissue was evaluated by 2D-SWE. The correlations between stiffness and pathological characteristics were analyzed in 114 patients. The predictive value of stiffness for recurrence-free survival (RFS) was evaluated, and Cutoff Finder was used for determining optimal cut-off stiffness values. Cox proportional hazards analysis was used to identify independent predictors of RFS.

RESULTS

Pathologically, intratumoral stiffness was associated with stroma proportion and microvascular invasion (MVI) while peritumoral stiffness was associated with tumor size, capsule, and MVI. Adjacent liver stiffness was correlated with capsule and liver fibrosis stage while distant liver stiffness was correlated with liver fibrosis stage. Peritumoral stiffness, adjacent liver stiffness, and distant liver stiffness were all correlated to RFS (all p < 0.05). Higher peritumoral stiffness (> 49.4 kPa) (HR = 1.822, p = 0.023) and higher adjacent liver stiffness (> 24.1 kPa) (HR = 1.792, p = 0.048) were significant independent predictors of worse RFS, along with tumor size and MVI. The nomogram based on these variables showed a C-index of 0.77 for RFS prediction.

CONCLUSIONS

Stiffness measured by 2D-SWE could be a tumor microenvironment and tumor invasiveness biomarker. Peritumoral stiffness and adjacent liver stiffness showed important values in predicting tumor recurrence after curative resection in HBV-related HCC.

CLINICAL RELEVANCE STATEMENT

Tumor and liver stiffness measured by two-dimensional shear wave elastography serve as imaging biomarkers for predicting hepatocellular carcinoma recurrence, reflecting biological behavior and tumor microenvironment.

KEY POINTS

• Stiffness measured by two-dimensional shear wave elastography is a useful biomarker of tumor microenvironment and invasiveness. • Higher stiffness indicated more aggressive behavior of hepatocellular carcinoma. • The study showed the prognostic value of peritumoral stiffness and adjacent liver stiffness for recurrence-free survival. • The nomogram integrating peritumoral stiffness, adjacent liver stiffness, tumor size, and microvascular invasion showed a C-index of 0.77.

Comparison of non-radiomics imaging features and radiomics models based on contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma within 5 cm

OBJECTIVES

The purpose of this study is to establish microvascular invasion (MVI) prediction models based on preoperative contrast-enhanced ultrasound (CEUS) and ethoxybenzyl-enhanced magnetic resonance imaging (EOB-MRI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.

METHODS

Patients with a single HCC ≤ 5 cm and accepting CEUS and EOB-MRI before surgery were enrolled in this study. Totally, 85 patients were randomly divided into the training and validation cohorts in a ratio of 7:3. Non-radiomics imaging features, the CEUS and EOB-MRI radiomics scores were extracted from the arterial phase, portal phase and delayed phase images of CEUS and the hepatobiliary phase images of EOB-MRI. Different MVI predicting models based on CEUS and EOB-MRI were constructed and their predictive values were evaluated.

RESULTS

Since univariate analysis revealed that arterial peritumoral enhancement on the CEUS image, CEUS radiomics score, and EOB-MRI radiomics score were significantly associated with MVI, three prediction models, namely the CEUS model, the EOB-MRI model, and the CEUS-EOB model, were developed. In the validation cohort, the areas under the receiver operating characteristic curve of the CEUS model, the EOB-MRI model, and the CEUS-EOB model were 0.73, 0.79, and 0.86, respectively.

CONCLUSIONS

Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm.

CLINICAL RELEVANCE STATEMENT

Radiomics models based on CEUS and EOB-MRI are effective for MVI predicting and conducive to pretreatment decision-making in patients with a single HCC within 5 cm.

KEY POINTS

• Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. • There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm.

Radiological findings in non-surgical recurrent hepatocellular carcinoma: From locoregional treatments to immunotherapy

Since hepatocellular carcinoma (HCC) represents an important cause of mortality and morbidity all over the world. Currently, it is fundamental not only to achieve a curative treatment but also to manage in the best way any possible recurrence. Even if the latest update of the Barcelona Clinic Liver Cancer guidelines for HCC treatment has introduced new locoregional techniques and confirmed others as well-established clinical practices, there is still no consensus about the treatment of recurrent HCC (RHCC). Locoregional treatments and medical therapy represent two of the most widely accepted approaches for disease control, especially in the advanced stage of liver disease. Different medical treatments are now approved, and others are under investigation. On this basis, radiology plays a central role in the diagnosis of RHCC and the assessment of response to locoregional treatments and medical therapy for RHCC. This review summarized the actual clinical practice by underlining the importance of the radiological approach both in the diagnosis and treatment of RHCC.

Clinical features and prognostic factors in patients with microvascular infiltration of hepatocellular carcinoma: Development and validation of a nomogram and risk stratification based on the SEER database

Background

The goal is to establish and validate an innovative prognostic risk stratification and nomogram in patients of hepatocellular carcinoma (HCC) with microvascular invasion (MVI) for predicting the cancer-specific survival (CSS).

Methods

1487 qualified patients were selected from the Surveillance, Epidemiology and End Results (SEER) database and randomly assigned to the training cohort and validation cohort in a ratio of 7:3. Concordance index (C-index), area under curve (AUC) and calibration plots were adopted to evaluate the discrimination and calibration of the nomogram. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities and compare it to the American Joint Committee on Cancer (AJCC) tumor staging system. C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement of the new model over the AJCC tumor staging system. The new risk stratifications based on the nomogram and the AJCC tumor staging system were compared.

Results

Eight prognostic factors were used to construct the nomogram for HCC patients with MVI. The C-index for the training and validation cohorts was 0.785 and 0.776 respectively. The AUC values were higher than 0.7 both in the training cohort and validation cohort. The calibration plots showed good consistency between the actual observation and the nomogram prediction. The IDI values of 1-, 3-, 5-year CSS in the training cohort were 0.17, 0.16, 0.15, and in the validation cohort were 0.17, 0.17, 0.17 (P<0.05). The NRI values of the training cohort were 0.75 at 1-year, 0.68 at 3-year and 0.67 at 5-year. The DCA curves indicated that the new model more accurately predicted 1-year, 3-year, and 5-year CSS in both training and validation cohort, because it added more net benefit than the AJCC staging system. Furthermore, the risk stratification system showed the CSS in different groups had a good regional division.

Conclusions

A comprehensive risk stratification system and nomogram were established to forecast CSS for patients of HCC with MVI.