NCT07584317

Brief Summary

This study aims to prospectively validate a retrospective cohort-derived AI-based multimodal model and explore tumor heterogeneity and the immune microenvironment to guide TACE combined with immunotherapy and targeted therapy in HCC.

Trial Health

65
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
1,170

participants targeted

Target at P75+ for all trials

Timeline
29mo left

Started May 2026

Typical duration for all trials

Status
not yet recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress8%
May 2026Dec 2028

First Submitted

Initial submission to the registry

May 7, 2026

Completed
6 days until next milestone

First Posted

Study publicly available on registry

May 13, 2026

Completed
5 days until next milestone

Study Start

First participant enrolled

May 18, 2026

Completed
1 year until next milestone

Primary Completion

Last participant's last visit for primary outcome

May 31, 2027

Expected
1.6 years until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2028

Last Updated

May 13, 2026

Status Verified

May 1, 2026

Enrollment Period

1 year

First QC Date

May 7, 2026

Last Update Submit

May 7, 2026

Conditions

Outcome Measures

Primary Outcomes (1)

  • Prediction Performance of the AI Model

    The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves o f the AI model in predicting the clinical outcomes in patients receiving TACE combined with immunotherapy and targeted therapy.

    From enrollment to approximately 2 years

Secondary Outcomes (4)

  • Objective response rate(ORR)

    up to approximately 2 years

  • Overall Survival(OS)

    up to approximately 2 years

  • Progression free survival(PFS)

    up to approximately 2 years

  • Other prediction performance of the model

    From enrollment to approximately 2 years

Study Arms (2)

Retrospective cohort

Patients with hepatocellular carcinoma who received TACE combined with immunotherapy and targeted therapy, as well as other treatment modalities, will be retrospectively included. Multimodal data from this cohort will be used to develop and train the AI-based model.

Other: Artificial Intelligence

Prospective cohort

Patients with hepatocellular carcinoma who receive TACE combined with immunotherapy and targeted therapy will be prospectively enrolled. Multimodal data, including clinical, imaging, and biospecimen-related data when available, will be collected to validate the AI-based multimodal model.

Other: Artificial Intelligence

Interventions

Investigators utilize a AI-based supportive system to predict clinical outcomes for patients with hepatocellular carcinoma who received TACE combined with immunotherapy and targeted therapy

Prospective cohortRetrospective cohort

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

In the retrospective cohort, patients with HCC who received TACE combined with immunotherapy and targeted therapy, as well as other treatment modalities, will be retrospectively included. Multimodal data from this cohort will be used to develop and train the AI-based model. In the prospective cohort, patients with HCC who receive TACE combined with immunotherapy and targeted therapy will be prospectively enrolled. Multimodal data, including clinical, imaging, and biospecimen-related data when available, will be collected to validate the AI-based multimodal model.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Related Publications (3)

  • Zhong BY, Fan W, Guan JJ, Peng Z, Jia Z, Jin H, Jin ZC, Chen JJ, Zhu HD, Teng GJ. Combination locoregional and systemic therapies in hepatocellular carcinoma. Lancet Gastroenterol Hepatol. 2025 Apr;10(4):369-386. doi: 10.1016/S2468-1253(24)00247-4. Epub 2025 Feb 21.

    PMID: 39993404BACKGROUND
  • Jin ZC, Wei J, Xiao YD, Si A, Chen JJ, Zhu XL, Li JZ, Nie F, Ding R, Zhou HF, Ding W, Zhong BY, Xie Y, Hu HT, Yin GW, Ji JS, Zhang WH, Shi HB, Wu JB, Xu GH, Yuan CW, Yang WZ, Liu RB, Wu YM, Zheng CS, Xu AB, Huang MS, Li JP, Chen L, Wen SW, Wang YQ, Gu SZ, Li D, Wang D, Zhou GH, Wang WD, Peng Z, Wang X, Zhu HD, Tian J, Teng GJ. Decoding tumor heterogeneity with imaging biomarkers predicts response to TACE plus immunotherapy and targeted therapy in HCC (CHANCE2204). Hepatology. 2025 Nov 10. doi: 10.1097/HEP.0000000000001593. Online ahead of print.

    PMID: 41213031BACKGROUND
  • Vithayathil M, Koku D, Campani C, Nault JC, Sutter O, Ganne-Carrie N, Aboagye EO, Sharma R. Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma. J Hepatol. 2025 Oct;83(4):959-970. doi: 10.1016/j.jhep.2025.04.017. Epub 2025 Apr 17.

    PMID: 40246150BACKGROUND

MeSH Terms

Conditions

Carcinoma, Hepatocellular

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

AdenocarcinomaCarcinomaNeoplasms, Glandular and EpithelialNeoplasms by Histologic TypeNeoplasmsLiver NeoplasmsDigestive System NeoplasmsNeoplasms by SiteDigestive System DiseasesLiver Diseases

Intervention Hierarchy (Ancestors)

AlgorithmsMathematical Concepts

Central Study Contacts

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
OTHER
Target Duration
24 Months
Sponsor Type
OTHER
Responsible Party
SPONSOR INVESTIGATOR
PI Title
President

Study Record Dates

First Submitted

May 7, 2026

First Posted

May 13, 2026

Study Start

May 18, 2026

Primary Completion (Estimated)

May 31, 2027

Study Completion (Estimated)

December 31, 2028

Last Updated

May 13, 2026

Record last verified: 2026-05

Data Sharing

IPD Sharing
Will not share