Zhongda hospital
Changzhou, Jiangsu, 213003, China
NCT Number: NCT07065786
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.
This study is active but is not currently recruiting participants.
Notify Me18 year and older
All sexes
Observational
Changzhou, Jiangsu, 213003, China
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
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.
Time frame: Through study completion, an average of 20 months
defined as the time from the initial of combined therapy to death from any cause
Zhongda Hospital
Other
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
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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