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A Generative Model-based System for Predicting Survival and Guiding Treatment Decisions in Patients With Unresectable Hepatocellularcarcinoma Undergoing Transcatheter Arterial Chemoembolization in Combination With Immunotherapy and Targeted Therapy

NCT07065786 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 550

Last updated 2025-07-15

No results posted yet for this study

Summary

The entry point of this study is the proposition of "generative longitudinal prediction," which utilizes only pre-treatment imaging to create high-fidelity predictions of post-treatment imaging. This approach effectively overcomes the clinical challenge of acquiring genuine longitudinal follow-up data. This paradigm shift not only tackles the scarcity of longitudinal data but also introduces an innovative method for treatment simulation using digital twins. Clinicians can intuitively assess the potential efficacy of various treatment plans before intervention through virtually generated multi-timepoint imaging, providing a visual foundation for personalized treatment decisions. This research merges generative AI with dynamic risk models to achieve: 1) a transition from static assessment to dynamic simulation; 2) earlier survival predictions; and 3) personalized optimization of treatment plans. By eliminating dependence on longitudinal data, we aim to deliver more precise and individualized treatment decision support for advanced liver cancer patients, ultimately enhancing survival outcomes and quality of life.

Conditions

Interventions

OTHER

Generative model

Prospectively enroll pretreatment imaging data from patients with unresectable hepatocellular carcinoma undergoing TACE in combination with immunotherapy plus targeted therapy. Utilize a generative model to create virtual images that represent optimal treatment responses, and compare these virtual images with actual treatment response images collected during follow-up to evaluate the reliability of the generative model.

Sponsors & Collaborators

  • Zhongda Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-01-01
Primary Completion
2026-01-01
Completion
2026-02-01

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07065786 on ClinicalTrials.gov