Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images

Decision-Tree Models Indicative of Microvascular Invasion on MRI Predict Survival in Patients with Hepatocellular Carcinoma Following Tumor Ablation

Purpose

Histological microvascular invasion (MVI) is a risk factor for poor survival and early recurrence in hepatocellular carcinoma (HCC) after surgery. Its prognostic value in the setting of locoregional therapies (LRT), where no tissue samples are obtained, remains unknown. This study aims to establish CT-derived indices indicative of MVI on liver MRI with superior soft tissue contrast and evaluate their association with patient survival after ablation via interstitial brachytherapy (iBT) versus iBT combined with prior conventional transarterial chemoembolization (cTACE).

Patients and Methods

Ninety-five consecutive patients, who underwent ablation via iBT alone (n = 47) or combined with cTACE (n = 48), were retrospectively included between 01/2016 and 12/2017. All patients received contrast-enhanced MRI prior to LRT. Overall (OS), progression-free survival (PFS), and time-to-progression (TTP) were assessed. Decision-tree models to determine Radiogenomic Venous Invasion (RVI) and Two-Trait Predictor of Venous Invasion (TTPVI) on baseline MRI were established, validated on an external test set (TCGA-LIHC), and applied in the study cohorts to investigate their prognostic value for patient survival. Statistics included Fisher's exact and t-test, Kaplan-Meier and cox-regression analysis, area under the receiver operating characteristic curve (AUC-ROC) and Pearson's correlation.

Results

OS, PFS, and TTP were similar in both treatment groups. In the external dataset, RVI showed low sensitivity but relatively high specificity (AUC-ROC = 0.53), and TTPVI high sensitivity but only low specificity (AUC-ROC = 0.61) for histological MVI. In patients following iBT alone, positive RVI and TTPVI traits were associated with poorer OS (RVI: p < 0.01; TTPVI: p = 0.08), PFS (p = 0.04; p = 0.04), and TTP (p = 0.14; p = 0.03), respectively. However, when patients with combined cTACE and iBT were stratified by RVI or TTPVI, no differences in OS (p = 0.75; p = 0.55), PFS (p = 0.70; p = 0.43), or TTP (p = 0.33; p = 0.27) were observed.

Conclusion

The study underscores the role of non-invasive imaging biomarkers indicative of MVI to identify patients, who would potentially benefit from embolotherapy via cTACE prior to ablation rather than ablation alone.

Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study

BACKGROUND

Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC.

AIM

To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC.

METHODS

This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR).

RESULTS

Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%.

CONCLUSION

Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.

A Nomogram of Magnetic Resonance Imaging for Preoperative Assessment of Microvascular Invasion and Prognosis of Hepatocellular Carcinoma

BACKGROUND

Microvascular invasion (MVI) is a predictor of recurrence and overall survival in hepatocellular carcinoma (HCC), the preoperative diagnosis of MVI through noninvasive methods play an important role in clinical treatment.

AIMS

To investigate the effectiveness of radiomics features in evaluating MVI in HCC before surgery.

METHODS

We included 190 patients who had undergone contrast-enhanced MRI and curative resection for HCC between September 2015 and November 2021 from two independent institutions. In the training cohort of 117 patients, MVI-related radiomics models based on multiple sequences and multiple regions from MRI were constructed. An independent cohort of 73 patients was used to validate the proposed models. A final Clinical-Imaging-Radiomics nomogram for preoperatively predicting MVI in HCC patients was generated. Recurrence-free survival was analyzed using the log-rank test.

RESULTS

For tumor-extracted features, the performance of signatures in fat-suppressed T1-weighted images and hepatobiliary phase was superior to that of other sequences in a single-sequence model. The radiomics signatures demonstrated better discriminatory ability than that of the Clinical-Imaging model for MVI. The nomogram incorporating clinical, imaging and radiomics signature showed excellent predictive ability and achieved well-fitted calibration curves, outperforming both the Radiomics and Clinical-Radiomics models in the training and validation cohorts.

CONCLUSIONS

The Clinical-Imaging-Radiomics nomogram model of multiple regions and multiple sequences based on serum alpha-fetoprotein, three MRI characteristics, and 12 radiomics signatures achieved good performance for predicting MVI in HCC patients, which may help clinicians select optimal treatment strategies to improve subsequent clinical outcomes.

Predictive value of radiomics analysis of enhanced CT for three-tiered microvascular invasion grading in hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is a major risk factor, for recurrence and metastasis of hepatocellular carcinoma (HCC) after radical surgery and liver transplantation. However, its diagnosis depends on the pathological examination of the resected specimen after surgery; therefore, predicting MVI before surgery is necessary to provide reference value for clinical treatment. Meanwhile, predicting only the existence of MVI is not enough, as it ignores the degree, quantity, and distribution of MVI and may lead to MVI-positive patients suffering due to inappropriate treatment. Although some studies have involved M2 (high risk of MVI), majority have adopted the binary classification method or have not included radiomics.

PURPOSE

To develop three-class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors.

METHODS

The data of 166 patients with HCC confirmed by surgery and pathology were analyzed retrospectively. The patients were divided into the training (116 cases) and test (50 cases) groups at a ratio of 7:3. Of them, 69 cases were MVI positive in the training group, including 45 cases in the low-risk group (M1) and 24 cases in the high-risk group (M2), and 47 cases were MVI negative (M0). In the training group, the optimal subset features were obtained through feature selection, and the arterial phase radiomics model, portal venous phase radiomics model, delayed phase radiomics model, three-phase radiomics model, clinical imaging model, and combined model were developed using Linear Support Vector Classification. The test group was used for validation, and the efficacy of each model was evaluated through the receiver operating characteristic curve (ROC).

RESULTS

The clinical imaging features of MVI included alpha-fetoprotein, tumor size, tumor margin, peritumoral enhancement, intratumoral artery, and low-density halo. The area under the curve (AUC) of the ROC values of the clinical imaging model for M0, M1, and M2 were 0.831, 0.701, and 0.847, respectively, in the training group and 0.782, 0.534, and 0.785, respectively, in the test group. After combined radiomics analyis, the AUC values for M0, M1, and M2 in the test group were 0.818, 0.688, and 0.867, respectively. The difference between the clinical imaging model and the combined model was statistically significant (p = 0.029).

CONCLUSION

The clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading, which can provide precise reference value for preoperative clinical diagnosis and treatment. The combined application of the two models had a high predictive efficacy.

Precision of MRI radiomics features in the liver and hepatocellular carcinoma

OBJECTIVES

To assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma.

METHODS

The study population consisted of 55 patients, including 16 with untreated HCCs, who underwent two repeat contrast-enhanced abdominal MRI exams within 1 month to evaluate: (1) test-retest repeatability using the same MRI system (n = 28, 10 HCCs); (2) inter-platform reproducibility between different MRI systems (n = 27, 6 HCCs); (3) inter-observer reproducibility (n = 16, 16 HCCs). Shape and 1st- and 2nd-order radiomics features were quantified on pre-contrast T1-weighted imaging (WI), T1WI portal venous phase (pvp), T2WI, and ADC (apparent diffusion coefficient), on liver regions of interest (ROIs) and HCC volumes of interest (VOIs). Precision was assessed by calculating intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), and coefficient of variation (CV).

RESULTS

There was moderate to excellent test-retest repeatability of shape and 1st- and 2nd-order features for all sequences in HCCs (ICC: 0.53-0.99; CV: 3-29%), and moderate to good test-retest repeatability of 1st- and 2nd-order features for T1WI sequences, and 2nd-order features for T2WI in the liver (ICC: 0.53-0.73; CV: 12-19%). There was poor inter-platform reproducibility for all features and sequences, except for shape and 1st-order features on T1WI in HCCs (CCC: 0.58-0.99; CV: 3-15%). Good to excellent inter-observer reproducibility was found for all features and sequences in HCCs (CCC: 0.80-0.99; CV: 4-15%) and moderate to good for liver (CCC: 0.45-0.86; CV: 6-25%).

CONCLUSIONS

MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI system and across readers but have low reproducibility across MR systems, except for shape and 1st-order features on T1WI. Data must be interpreted with caution when performing multiplatform radiomics studies.

KEY POINTS

• MRI radiomics features have acceptable repeatability when using the same MRI system but less reproducible when using different MRI platforms. • MRI radiomics features extracted from T1 weighted-imaging show greater stability across exams than T2 weighted-imaging and ADC. • Inter-observer reproducibility of MRI radiomics features was found to be good in HCC tumors and acceptable in liver parenchyma.

Two-Trait Predictor of Venous Invasion on Contrast-Enhanced CT as a Preoperative Predictor of Outcomes for Early-Stage Hepatocellular Carcinoma After Hepatectomy

Objectives

This study aimed to assess the effectiveness of the two-trait predictor of venous invasion (TTPVI) on contrast-enhanced computed tomography (CECT) for the preoperative prediction of clinical outcomes in patients with early-stage hepatocellular carcinoma (HCC) after hepatectomy.

Methods

This retrospective study included 280 patients with surgically resected HCC who underwent preoperative CECT between 2012 and 2013. CT imaging features of HCC were assessed, and univariate and multivariate Cox regression analyses were used to evaluate the CT features associated with disease-free survival (DFS) and overall survival (OS). Subgroup analyses were used to summarized the hazard ratios (HRs) between patients in whom TTPVI was present and those in whom TTPVI was absent using a forest plot.

Results

Capsule appearance [HR, 0.504; 95% confidence interval (CI), 0.341–0.745; p < 0.001], TTPVI (HR, 1.842; 95% CI, 1.319–2.572; p < 0.001) and high level of alanine aminotransferase (HR, 1.620; 95% CI, 1.180–2.225, p = 0.003) were independent risk factors for DFS, and TTPVI (HR, 2.509; 95% CI, 1.518–4.147; p < 0.001), high level of alpha-fetoprotein (HR, 1.722; 95% CI, 1.067–2.788; p = 0.026), and gamma-glutamyl transpeptidase (HR, 1.787; 95% CI, 1.134–2.814; p = 0.026) were independent risk factors for OS. A forest plot revealed that the TTPVI present group had lower DFS and OS rates in most subgroups. Patients in whom TTPVI was present in stages I and II had a lower DFS and OS than those in whom TTPVI was absent. Moreover, there were significant differences in DFS (p < 0.001) and OS (p < 0.001) between patients classified as Barcelona Clinic Liver Cancer stage A in whom TTPVI was absent and in whom TTPVI was present.

Conclusions

TTPVI may be used as a preoperative biomarker for predicting postoperative outcomes for patients with early-stage HCC.