Subclassification of advanced-stage hepatocellular carcinoma with macrovascular invasion: combined transarterial chemoembolization and radiotherapy as an alternative first-line treatment

A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma

PURPOSE

Given its heterogeneity and diverse clinical outcomes, precise subclassification of Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment.

EXPERIMENTAL DESIGN

We recruited 2,626 patients with BCLC-C HCC from multiple centers, comprising training/test (n = 1,693) and validation cohorts (n = 933). The XGBoost model was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-, intermediate-, high-, and very high-risk subgroups based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C).

RESULTS

The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-, 12-, and 24-month survival of patients with BCLC-C were 0.800, 0.831, and 0.715, respectively-significantly higher than those of the conventional models, which were consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKI) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab-bevacizumab as the best therapy, particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high- to very high-risk subgroup.

CONCLUSIONS

ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.

Limited Generalizability of Retrospective Single-Center Cohort Study in Comparison to Multicenter Cohort Study on Prognosis of Hepatocellular Carcinoma

Introduction

We aimed to evaluate the generalizability of retrospective single-center cohort studies on prognosis of hepatocellular carcinoma (HCC) by comparing overall survival (OS) after various treatments between a nationwide multicenter cohort and a single-center cohort of HCC patients.

Methods

Patients newly diagnosed with HCC between January 2008 and December 2018 were analyzed using data from the Korean Primary Liver Cancer Registry (multicenter cohort, n=16,443), and the Asan Medical Center HCC registry (single-center cohort, n=15,655). The primary outcome, OS after initial treatment, was compared between the two cohorts for both the entire population and for subcohorts with Child-Pugh A liver function (n=2797 and n=5151, respectively) treated according to the Barcelona-Clinic-Liver-Cancer (BCLC) strategy, using Log rank test and Cox proportional hazard models.

Results

Patients of BCLC stages 0 and A (59.3% vs 35.2%) and patients who received curative treatment (42.1% vs 32.1%) were more frequently observed in the single-center cohort (Ps<0.001). Multivariable analysis revealed significant differences between the two cohorts in OS according to type of treatment: the multicenter cohort was associated with higher risk of mortality among patients who received curative (adjusted hazard ratio [95% confidence interval], 1.48 [1.39-1.59]) and non-curative (1.22 [1.17-1.27]) treatments, whereas the risk was lower in patients treated with systemic therapy (0.83 [0.74-0.92]) and best supportive care (0.85 [0.79-0.91]). Subcohort analysis also demonstrated significantly different OS between the two cohorts, with a higher risk of mortality in multicenter cohort patients who received chemoembolization (1.72 [1.48-2.00]) and ablation (1.44 [1.08-1.92]).

Conclusion

Comparisons of single-center and multicenter cohorts of HCC patients revealed significant differences in OS according to treatment modality after adjustment for prognostic variables. Therefore, the results of retrospective single-center cohort studies of HCC treatments may not be generalizable to real-world practice.

Comparison of atezolizumab plus bevacizumab and lenvatinib for hepatocellular carcinoma with portal vein tumor thrombosis

BACKGROUND/AIM

Atezolizumab plus bevacizumab and lenvatinib are currently available as first-line therapy for the treatment of unresectable hepatocellular carcinoma (HCC). However, comparative efficacy studies are still limited. This study aimed to investigate the effectiveness of these treatments in HCC patients with portal vein tumor thrombosis (PVTT).

METHODS

We retrospectively included patients who received either atezolizumab plus bevacizumab or lenvatinib as first-line systemic therapy for HCC with PVTT. Primary endpoint was overall survival (OS), and secondary endpoints included progressionfree survival (PFS) and disease control rate (DCR) determined by response evaluation criteria in solid tumors, version 1.1.

RESULTS

A total of 52 patients were included: 30 received atezolizumab plus bevacizumab and 22 received lenvatinib. The median follow-up duration was 6.4 months (interquartile range, 3.9-9.8). The median OS was 10.8 months (95% confidence interval [CI], 5.7 to not estimated) with atezolizumab plus bevacizumab and 5.8 months (95% CI, 4.8 to not estimated) with lenvatinib (P=0.26 by log-rank test). There was no statistically significant difference in OS (adjusted hazard ratio [aHR], 0.71; 95% CI, 0.34-1.49; P=0.37). The median PFS was similar (P=0.63 by log-rank test), with 4.1 months (95% CI, 3.3-7.7) for atezolizumab plus bevacizumab and 4.3 months (95% CI, 2.6-5.8) for lenvatinib (aHR, 0.93; 95% CI, 0.51-1.69; P=0.80). HRs were similar after inverse probability treatment weighting. The DCRs were 23.3% and 18.2% in patients receiving atezolizumab plus bevacizumab and lenvatinib, respectively (P=0.74).

CONCLUSION

The effectiveness of atezolizumab plus bevacizumab and lenvatinib was comparable for the treatment of HCC with PVTT.

Liver resection in selective hepatocellular carcinoma with Vp3 or Vp4 portal vein tumor thrombosis improves prognosis

BACKGROUND/AIM

Hepatocellular carcinoma (HCC) tumor thrombi located in the first branch of the portal vein (Vp3) or in the main portal trunk (Vp4) are associated with poor prognosis. This study aimed to investigate the clinicopathological characteristics and risk factors for HCC recurrence and mortality following liver resection (LR) in patients with Vp3 or Vp4 HCC.

METHODS

The study included 64 patients who underwent LR for HCC with Vp3 or Vp4 portal vein tumor thrombosis (PVTT).

RESULTS

Fifty-eight patients (90.6%) had Vp3 PVTT, whereas the remaining six patients exhibited Vp4 PVTT. The median tumor size measured 8 cm, with approximately 36% of patients presented with multiple tumors. Fifty-four patients (84.4%) underwent open LR, whereas 10 patients underwent laparoscopic LR. In the Vp4 cases, combined LR and tumor thrombectomy were performed. The 3-year cumulative disease-free survival rate was 42.8% for the Vp3 group and 22.2% for the Vp4 group. The overall survival (OS) rate at 3 years was 47.9% for the Vp3 group and 60.0% for the Vp4 group. Intrahepatic metastasis has been identified as an important contributor to HCC recurrence. High hemoglobin levels are associated with high mortality.

CONCLUSION

LR is a safe and effective treatment modality for selected patients with Vp3 or Vp4 HCC PVTT. This suggests that LR is a viable option for these patients, with favorable outcomes in terms of OS.