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Not yet recruiting NCT07379489

Adjuvant Anti-PD-1 Therapy in Resected Hepatocellular Carcinoma

Observational HCC Adjuvant Therapy Recurrence Immune Checkpoint Inhibitor

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: PD-1 Inhibitors.
Who it may be relevant to
Registry conditions: HCC, Adjuvant Therapy, Recurrence, Immune Checkpoint Inhibitor. Basic parameters: 18 years — 75 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Center list to be confirmed — check the primary protocol.
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Official title

Efficacy of Postoperative Adjuvant PD-1 Inhibitors Guided by a Deep Learning Model: a Multicenter, Prospective Cohort Study

Overview

Early hepatocellular carcinoma (HCC) recurrence (driven by residual tumors) and late recurrence (driven by de novo tumors) exhibit distinct biological behaviors, suggesting differential therapeutic vulnerabilities. The beneficiaries of adjuvant PD-1 inhibitors (aPD-1) and their efficacy across these temporally divergent recurrence patterns remains unestablished.

Detailed description

Hepatocellular carcinoma (HCC), a leading cause of global cancer-related mortality, continues to rise in incidence and lethality despite advancements in early detection and surgical techniques. Curative liver resection, while the cornerstone of therapeutic management, is frequently undermined by postoperative recurrence, a phenomenon observed in up to 70% of patients within five years, with early (≤2 years) and late (\>2 years) recurrences reflecting distinct biological origins. Early recurrences predominantly stem from residual micro-metastases of the primary tumor, strongly associated with aggressive histopathological features such as microvascular invasion (MVI), multifocality, and satellite nodules. In contrast, late recurrences often arise de novo from the cirrhotic liver microenvironment, driven by persistent viral activity or chronic hepatic inflammation rather than the index tumor's biological behavior. Despite decades of research, postoperative adjuvant strategies, including antiviral therapy, transarterial chemoembolization, and traditional agents like Huaier granules, have yielded inconsistent results or lack robust evidence for standardization. The emergence of immune checkpoint inhibitors (ICIs) has reignited hope, yet recent randomized controlled trials (RCT) underscore unresolved challenges. The IMbrave050 trial initially demonstrated reduced recurrence with adjuvant Atezolizumab-Bevacizumab (median follow-up of 17 months). However, with longer follow-up (35 months), results shifted to negative. Another RCT has shown promising outcomes for patients with MVI-positive HCC who received adjuvant therapy with Sintilimab. Nevertheless, the median follow-up was only 23 months, which does not provide adequate resolution of late recurrence. Similarly, a recent prospective cohort study reported positive results of adjuvant immunotherapy in high-risk patients. These studies, limited by follow-up durations insufficient to capture late-recurrence dynamics, leave critical questions unanswered: Do adjuvant ICIs durably suppress recurrence, or merely delay its onset? In addition, there is currently no gold standard for defining high recurrence risk. Common pathological factors include MVI and satellite nodules13, but the same patient may have multiple high-risk factors simultaneously. Machine learning (ML) is increasingly being used in the construction of predictive models, and its performance often exceeds that of models based on standard statistical methods and traditional staging systems14, 15. Therefore, there is great potential for using ML to integrate clinical and pathological characteristics and quantify these risk factors to accurately identify high-risk populations and guide postoperative management strategies.

In order to fill these research gaps, we constructed an ML model to predict the risk of HCC recurrence through previous studies, and found that high-risk groups were more likely to be the potential benefit population of HCC. Therefore, this study aimed to verify the value of ML model in guiding postoperative adjuvant PD-1 inhibitors.

Interventions

  • Drug PD-1 Inhibitors
    Patients in the adjuvant cohort received at least one cycle of PD-1 inhibitors.

Primary outcome measures

  • Disease free survival [Time frame: From date of surgery until the date of first documented recurrence or date of death from any cause, whichever came first, assessed up to 96months.]
Secondary outcome measures (1)
  • Overall survival [Time frame: From date of enrollment until the date of death from any cause, assessed up to 96 months.]

Eligibility criteria

Inclusion criteria

  • Aged between 18 and 75;
  • achieved complete tumor resection;
  • histological verification of HCC;
  • liver function classified as Child-Pugh grade A or B;
  • No other serious systemic disease or organ dysfunction.

Exclusion criteria

  • history of other malignancies or recurrent HCC;
  • extrahepatic metastasis;
  • prior treatments for HCC;
  • ongoing severe postoperative complications;
  • mixed or other types of liver cancer;
  • received other adjuvant therapy.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Study design

Observational model
Cohort

Study locations

Center list to be confirmed — check the primary protocol.

Identifiers

NCT: NCT07379489 · MLbased-adjuvant01

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗