NCT07065786

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

A Generative Model-based System for Predicting Survival and Guiding Treatment Decisions in Patients With Unresectable Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization in Combination With Immunotherapy and Targeted Therapy

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 550 (estimated)
• Sponsor: Zhongda Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Detailed description

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. The model was developed in a retrospective cohort, with validation and testing conducted in multiple retrospective and prospective cohorts, respectively.

Conditions

• Transcatheter Arterial Chemoembolization
• Unresectable Hepatocellular Carcinoma

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2024-01-01
• Primary completion: 2026-01-01
• Completion: 2026-02-01
• First posted: 2025-07-15
• Last updated: 2025-07-15

Locations

1 site across 1 country: China