Radiologic features of hepatocellular carcinoma related to prognosis

Prediction early recurrence of hepatocellular carcinoma after hepatectomy using gadoxetic acid-enhanced MRI and IVIM

Objectives

This study aims to develop and validate a predictive nomogram for early recurrence in hepatocellular carcinoma (HCC), utilizing gadoxetic acid-enhanced MRI and intravoxel incoherent motion (IVIM) imaging to improve preoperative assessment and decision-making.

Materials and methods

From March 2018 and June 2022, a total of 245 patients with pathologically confirmed HCC, who underwent preoperative gadoxetic acid-enhanced MRI and IVIM, were retrospectively enrolled from two hospitals. These patients were divided into a training cohort (n = 160) and a validation cohort (n = 85). All patients were followed until death or the last follow-up date, with a minimum follow-up period of two years. Clinical indicators and pathologic information were compared between train cohort and validation cohort. Radiological features and diffusion parameters were compared between recurrence and non-recurrence groups using the chi-square test, Mann-Whitney U test and independent sample t test in training cohort. Univariate and multivariate analyses were performed to identify significant clinical-radiological variables associated with early recurrence in the training cohort. Based on these findings, a predictive nomogram integrating risk factors and diffusion parameters was developed. The predictive performance of the nomogram was evaluated in both the training and validation cohorts.

Results

No statistically significant difference in clinical and pathologic characteristics were observed between the training and validation cohorts. In training cohort, significant differences were identified between the recurrence and non-recurrence groups in tumor size, nodule-in-nodule architecture, mosaic architecture, non-smooth tumor margin, intratumor necrosis, satellite nodule, and peritumoral hypo-intensity in the hepatobiliary phase (HBP). The results of multivariate analysis identified tumor size (HR, 1.435; 95 % CI, 0.702-2.026; p < 0.05), mosaic architecture (HR, 0.790; 95 % CI, 0.421-1.480; p < 0.05), non-smooth tumor margin (HR, 1.775; 95 % CI, 0.941-3.273; p < 0.05), intratumor necrosis (HR, 1.414; 95 % CI, 0.807-2.476; p < 0.05), satellite nodule (HR, 0.648; 95 % CI, 0.352-1.191; p < 0.01), peritumoral hypo-intensity on HBP (HR, 2.786; 95 % CI, 1.141-6.802; p < 0.001) and D (HR, 0.658; 95 % CI,0.487-0.889; p < 0.01) were the independent risk factor for recurrence. The nomogram exhibited excellent predictive performance with C-index of 0.913 and 0.875 in the training cohort and validation cohort, respectively. Also, based on the nomogram score, the patients were classified according to risk factor and the Kaplan-Meier curve analysis also showed that the nomogram had a good predictive efficacy.

Conclusion

The nomogram, integrating radiological risk factors and diffusion parameters, offers a reliable tool for preoperative prediction of early recurrence in HCC patients.

A Nomogram Based on MRI Visual Decision Tree to Evaluate Vascular Endothelial Growth Factor in Hepatocellular Carcinoma

BACKGROUNDS

Anti-vascular endothelial growth factor (VEGF) therapy has been developed and recognized as an effective treatment for hepatocellular carcinoma (HCC). However, there remains a lack of noninvasive methods in precisely evaluating VEGF expression in HCC.

PURPOSE

To establish a visual noninvasive model based on clinical indicators and MRI features to evaluate VEGF expression in HCC.

STUDY TYPE

Retrospective.

POPULATION

One hundred forty HCC patients were randomly divided into a training (N = 98) and a test cohort (N = 42).

FIELD STRENGTH/SEQUENCE

3.0 T, T2WI, T1WI including pre-contrast, dynamic, and hepatobiliary phases.

ASSESSMENT

The fusion model constructed by history of smoking, albumin-to-globulin ratio (AGR) and the Radio-Tree model was visualized by a nomogram.

STATISTICAL TESTS

Performances of models were assessed by receiver operating characteristic (ROC) curves. Student's t-test, Mann-Whitney U-test, chi-square test, Fisher's exact test, univariable and multivariable logistic regression analysis, DeLong's test, integrated discrimination improvement (IDI), Hosmer-Lemeshow test, and decision curve analysis were performed. P < 0.05 was considered statistically significant.

RESULTS

History of smoking and AGR ≤1.5 were clinical independent risk factors of the VEGF expression. In training cohorts, values of area under the curve (AUCs) of Radio-Tree model, Clinical-Radiological (C-R) model, fusion model which combined history of smoking and AGR with Radio-Tree model were 0.821, 0.748, and 0.871. In test cohort, the fusion model showed highest AUC (0.844) than Radio-Tree and C-R models (0.819, 0.616, respectively). DeLong's test indicated that the fusion model significantly differed in performance from the C-R model in training cohort (P = 0.015) and test cohort (P = 0.007).

DATA CONCLUSION

The fusion model combining history of smoking, AGR and Radio-Tree model established with ML algorithm showed the highest AUC value than others.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Gd-EOB-DTPA-enhanced MR imaging features of hepatocellular carcinoma in non-cirrhotic liver

OBJECTIVE

To evaluate clinical, pathological and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) findings of hepatocellular carcinoma (HCC) in non-cirrhotic livers and compare with HCC in cirrhotic livers.

METHODS

This retrospective study included patients with pathologically confirmed HCC who underwent preoperative Gd-EOB-DTPA-enhanced MRI between January 2015 and October 2021. Propensity scores were utilized to match non-cirrhotic HCCs (NCHCCs) patients with cirrhotic HCCs (CHCCs) patients. The clinical, pathological and MR imaging features of NCHCCs were compared with CHCCs. Correlation between these features and the presence of NCHCCs were analyzed by logistic regression analysis. The predictive efficacy was evaluated using receiver operating characteristic (ROC) analysis. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs.

RESULTS

After propensity score matching (1:3), a total of 144 patients with HCCs (36 NCHCCs and 108 CHCCs) were included. NCHCCs were larger in tumor size than CHCCs (P < 0.001, Cohen's d = 0.737). NCHCCs were more common in patients who have hepatitis C (5.6 % vs 1.9 %, P > 0.05) or have no known liver disease (11.1 % vs 0.9 %, P = 0.004), while hepatitis B was more common in CHCC patients (83.3 % vs 97.2 %, P = 0.003). Compared with CHCCs, NCHCCs more frequently demonstrated non-smooth tumor margin (P = 0.001, Cramer's V = 0.273), peri-tumoral hyperintensity (P < 0.05, Cramer's V = 0.185), hyperintense and heterogeneous signals in hepatobiliary phase (HBP) (P < 0.05). CHCCs were more likely to have satellite nodules compared to NCHCCs (33.3 % vs 57.4 %, P < 0.05, Cramer's V = 0.209). Based on the univariate and multivariate logistic regression analysis, the tumor size, non-smooth tumor margin, heterogeneous intensity in HBP and satellite nodule were significantly correlated to NCHCCs (P all <0.05). ROC curve analysis demonstrated that tumor size and non-smooth tumor margin were potential imaging predictors for the diagnosis of NCHCC, with AUC values of 0.715 and 0.639, respectively. The combination of the two imaging features for identifying NCHCC achieved an AUC value of 0.761, with a sensitivity of 0.889 and a specificity of 0.630.

CONCLUSION

NCHCCs were more likely to show larger tumor size, non-smooth tumor margin, peri-tumoral hyperintensity, as well as hyperintense and heterogeneous signals in HBP at Gd-EOB-DTPA-enhanced MR imaging compared with NCHCCs. Tumor size and non-smooth tumor margin in HBP may help to discriminate NCHCCs.

Prediction of PD-L1 expression in unresectable hepatocellular carcinoma with gadoxetic acid-enhanced MRI

OBJECTIVES

To develop a model to predict programmed death-ligand 1 (PD-L1) expression in unresectable hepatocellular carcinoma (HCC) based on gadoxetic acid-enhanced magnetic resonance imaging (MRI) findings and clinical characteristics.

MATERIALS AND METHODS

We enrolled patients with unresectable HCC who underwent gadoxetic acid-enhanced MRI between January 2021 and May 2023. Immunohistochemical staining of PD-L1 was performed on a biopsy specimen. Patients with a history of any prior treatment for HCC or those lacking an MRI scan within 30 days of the biopsy date were excluded. Using the clinical and MRI findings, we developed a PD-L1 prediction score using logistic regression.

RESULTS

This study included 49 patients with HCC (median age, 64 years; interquartile range, 57-73 years; 44 men). Among these, 15 (31 %) were positive for PD-L1 expression. The PD-L1 prediction score was defined as the sum of arterial phase hypoenhancement (score 1), necrosis (score 1), and AFP >4000 ng/mL (score 2). The AUC value of the PD-L1 prediction score was 0.838 (95 % confidence interval [CI], 0.715-0.962). When the PD-L1 prediction score was ≥3, the sensitivity, specificity, and positive predictive value of PD-L1 positivity were 67 %, 91 %, and 77 %, respectively.

CONCLUSION

We developed a PD-L1 prediction score for unresectable HCC with high specificity that could potentially contribute to the identification of effective candidates for immune checkpoint inhibitors.

Advances in Understanding Hepatocellular Carcinoma Vasculature: Implications for Diagnosis, Prognostication, and Treatment

Hepatocellular carcinoma (HCC) progresses through multiple stages of hepatocarcinogenesis, with each stage characterized by specific changes in vascular supply, drainage, and microvascular structure. These vascular changes significantly influence the imaging findings of HCC, enabling non-invasive diagnosis. Vascular changes in HCC are closely related to aggressive histological characteristics and treatment responses. Venous drainage from the tumor toward the portal vein in the surrounding liver facilitates vascular invasion, and the unique microvascular pattern of vessels that encapsulate the tumor cluster (known as a VETC pattern) promotes vascular invasion and metastasis. Systemic treatments for HCC, which are increasingly being used, primarily target angiogenesis and immune checkpoint pathways, which are closely intertwined. By understanding the complex relationship between histopathological vascular changes in hepatocarcinogenesis and their implications for imaging findings, radiologists can enhance the accuracy of imaging diagnosis and improve the prediction of prognosis and treatment response. This, in turn, will ultimately lead to better patient care.

Inter-reader agreement for CT/MRI LI-RADS category M imaging features: a systematic review and meta-analysis

BACKGROUNDS/AIMS

To systematically evaluate inter-reader agreement in the assessment of individual liver imaging reporting and data system (LI-RADS) category M (LR-M) imaging features in computed tomography/magnetic resonance imaging (CT/MRI) LIRADS v2018, and to explore the causes of poor agreement in LR-M assignment.

METHODS

Original studies reporting inter-reader agreement for LR-M features on multiphasic CT or MRI were identified using the MEDLINE, EMBASE, and Cochrane databases. The pooled kappa coefficient (κ) was calculated using the DerSimonian-Laird random-effects model. Heterogeneity was assessed using Cochran's Q test and I2 statistics. Subgroup meta-regression analyses were conducted to explore the study heterogeneity.

RESULTS

In total, 24 eligible studies with 5,163 hepatic observations were included. The pooled κ values were 0.72 (95% confidence interval [CI], 0.65-0.78) for rim arterial phase hyperenhancement, 0.52 (95% CI, 0.39-0.65) for peripheral washout, 0.60 (95% CI, 0.50-0.70) for delayed central enhancement, 0.68 (95% CI, 0.57-0.78) for targetoid restriction, 0.74 (95% CI, 0.65-0.83) for targetoid transitional phase/hepatobiliary phase appearance, 0.64 (95% CI, 0.49-0.78) for infiltrative appearance, 0.49 (95% CI, 0.30-0.68) for marked diffusion restriction, and 0.61 (95% CI, 0.48-0.73) for necrosis or severe ischemia. Substantial study heterogeneity was observed for all LR-M features (Cochran's Q test, P<0.01; I2≥89.2%). Studies with a mean observation size of <3 cm, those performed using 1.5-T MRI, and those with multiple image readers, were significantly associated with poor agreement of LR-M features.

CONCLUSIONS

The agreement for peripheral washout and marked diffusion restriction was limited. The LI-RADS should focus on improving the agreement of LR-M features.

Radiomics and machine learning based on preoperative MRI for predicting extrahepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization

Purpose

To develop and validate a radiomics machine learning (Rad-ML) model based on preoperative MRI to predict extrahepatic metastasis (EHM) in hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE) treatment.

Methods

A total of 355 HCC patients who received multiple TACE procedures were split at random into a training set and a test set at a 7:3 ratio. Radiomic features were calculated from tumor and peritumor in arterial phase and portal venous phase, and were identified using intraclass correlation coefficient, maximal relevance and minimum redundancy, and least absolute shrinkage and selection operator techniques. Cox regression analysis was employed to determine the clinical model. The best-performing algorithm among eight machine learning methods was used to construct the Rad-ML model. A nomogram combining clinical and Rad-ML parameters was used to develop a combined model. Model performance was evaluated using C-index, decision curve analysis, calibration plot, and survival analysis.

Results

In clinical model, elevated neutrophil to lymphocyte ratio and alpha-fetoprotein were associated with faster EHM. The XGBoost-based Rad-ML model demonstrated the best predictive performance for EHM. When compared to the clinical model, both the Rad-ML model and the combination model performed better (C-indexes of 0.61, 0.85, and 0.86 in the training set, and 0.62, 0.82, and 0.83 in the test set, respectively). However, the combined model's and the Rad-ML model's prediction performance did not differ significantly. The most influential feature was peritumoral waveletHLL_firstorder_Minimum in AP, which exhibited an inverse relationship with EHM risk.

Conclusions

Our study suggests that the preoperative MRI-based Rad-ML model is a valuable tool to predict EHM in HCC patients treated with TACE.

Imaging prognostication and tumor biology in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, and represents a significant global health burden with rising incidence rates, despite a more thorough understanding of the etiology and biology of HCC, as well as advancements in diagnosis and treatment modalities. According to emerging evidence, imaging features related to tumor aggressiveness can offer relevant prognostic information, hence validation of imaging prognostic features may allow for better noninvasive outcomes prediction and inform the selection of tailored therapies, ultimately improving survival outcomes for patients with HCC.