Liver Cancer, AdultNCT07042841
AI-Driven Quantitative Decision and Surgical Planning System for Liver Cancer
An observational study of Liver Cancer, Adult, run by Beijing Tsinghua Chang Gung Hospital at 1 site in China.
Trial summary
This study aims to develop and validate an integrated AI-powered system for liver cancer that includes intelligent tumor boundary detection, micro-metastasis prediction, staging, treatment decision-making, and surgical planning. The system builds upon prior 3D reconstructions of liver, vessels, and bile ducts. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Liver Cancer, Adult
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 30 Jun 2015to 30 Oct 2027
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study aims to include adult patients (≥18 years old) who have undergone dynamic contrast-enhanced CT imaging of the liver. The population will consist of three groups: HCC-positive group (Group 1): Patients with liver lesions diagnosed as suspected hepatocellular carcinoma (HCC) based on CT imaging reports, and with complete clinical records for assessment of CNLC staging, Child-Pugh score, and extrahepatic metastasis. HCC-negative with liver lesion group (Group 2): Patients with liver lesions identified on CT imaging but without HCC diagnosis, and without clear evidence of extrahepatic metastasis. Non-lesion control group (Group 3): Patients with no liver lesions identified on CT imaging, serving as the control group. All participants will have a CT imaging slice thickness of ≤5 mm, and must be capable of providing complete imaging reports and medical records. The study will target approximately 300 participants across these groups.
Who may be able to join
- 1. Dynamic contrast-enhanced CT scan of the liver is available. 2. Age ≥ 18 years. 3. CT image slice thickness ≤ 5 mm. 4. An official CT report is available. 5. Meets at least one of the following subcategories:HCC-positive with liver lesion: CT report suggests suspected hepatocellular carcinoma (HCC), and complete hospitalization records are available to assess CNLC staging (including performance status, Child-Pugh classification, and extrahepatic metastasis). HCC-negative with liver lesion: CT report indicates at least one liver-occupying lesion, but not suspected to be HCC. No liver lesion: CT report indicates no liver-occupying lesion.
Who cannot join
- 1. Incomplete liver coverage in CT images. 2. Severe noise artifacts in CT images. 3. History of liver resection or significant anatomical/volume alterations of the liver. 4. Deemed unsuitable for inclusion by the investigator.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Beijing Tsinghua Chang Gung Hospital
Beijing, Beijing Municipality, 102218
About this study
Official title: the Study on AI-Integrated Fully Quantitative Treatment Decision and Surgical Planning System for Liver Cancer
This study aims to develop and validate an integrated AI-powered system for liver cancer that includes intelligent tumor boundary detection, micro-metastasis prediction, staging, treatment decision-making, and surgical planning. The system builds upon prior 3D reconstructions of liver, vessels, and bile ducts. In a retrospective multi-center, self-controlled, fully crossed multi-reader multi-case clinical trial, the researchers will compare diagnostic accuracy, staging, and planning performance between AI-assisted reads and conventional reads using CT images and pathological gold standards.
Read the detailed description
Background Precise tumor boundary definition on preoperative imaging is often unreliable, and regional disparities in healthcare resources limit personalized decision-making. Currently, no comprehensive AI system integrates imaging, pathology, staging, decision, and surgical planning for liver cancer.
Objectives 1: Develop a pathological-tumor-boundary evaluation system based on whole-slide pathology and CT registration. 2: Build models to predict tumor boundary and satellite micro-metastasis from CT images using deep learning and radiomics. 3: Integrate staging modules (Child-Pugh, ECOG-PS, CNLC/BCLC) and generate individualized treatment recommendations. 4: Create a surgical planning platform that calculates liver remnant volume, vascular invasion metrics, anatomical variants, and performs virtual resections. 5: Validate the system in a retrospective self-controlled multi-reader multi-case study across multiple centers.
Methods Tumor boundary \& micro-metastasis prediction: Register 3D whole-slide pathology of resected specimens to preoperative CT using multiplanar reconstruction; pathologists annotate tumor and satellite lesions; train deep-learning models to predict pathological boundaries and micro-metastasis regions. Validate in 100 cases with pathological CT comparison. Decision modules: Automatically compute Child-Pugh and ECOG-PS scores from labs and records; integrate with tumor metrics and PV invasion to achieve CNLC/BCLC staging and generate decision suggestions. Surgical planning: Calculate functional liver volume requirements per consensus, estimate standard liver volume (SLV), tumor-bearing segment volume, future liver remnant (FLR/SLV), flag unsafe resections; analyze vascular invasion level, length, and perfusion territory; detect portal/bile anatomical variants for injury warnings; perform virtual anatomical and non-anatomical resections with margin control and risk predictions. Clinical validation: Conduct a retrospective, fully-crossed multi-reader multi-case trial: randomized CT reading by surgeons/radiologists with and without software assistance, separated by washout periods. Evaluate primary endpoints: AFROC-AUC for lesion detection, LROC-AUC for HCC diagnosis. Secondary endpoints include sensitivity, specificity, ROC-AUC, diagnostic consistency (Kappa), size/count accuracy (<5% error), staging concordance, and reading time.
Study population Adult participants with dynamic contrast-enhanced liver CT, including HCC-positive, HCC-negative lesion-positive, and lesion-negative cases. Exclude cases with incomplete liver imaging, heavy noise, prior liver resection, or unreadable CT.
Statistical analysis Compare AUCs between AI-assisted and manual reading; non-inferiority established if lower bound of 95% CI > 0. Secondary metrics include sensitivity, specificity, consistency, reading time, and staging accuracy.
- Conditions
- Liver Cancer, Adult
- Keywords
- Liver Cancer, Artificial Intelligence, Computer-Aided Diagnosis, Multimodal Image Fusion, Pathology-CT Registration
- Medical subject headings
- Carcinoma, Hepatocellular, Liver Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| HCC Positive Group | HCC-positive with liver lesion | |
| HCC Negative Lesion Group | HCC-negative with liver lesion | |
| Non-Lesion Control Group | No liver lesion |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of the intelligent liver cancer diagnosis and surgical planning system
The diagnostic accuracy will be evaluated by comparing the system's prediction of tumor boundaries and microvascular invasion with pathological gold standards.
Measured: At the time of surgery.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jun 2025
- Study start30 Jun 2015
- Primary completion30 Dec 2024
- Study completion30 Oct 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Liver Cancer, AdultStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 300 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- Beijing Tsinghua Chang Gung Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Shuo Jin, Beijing Tsinghua Chang Gung Hospital
- Investigators
- Shuo Jin, PhD, Principal investigator, Beijing Tsinghua Chang Gung Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07042841
- Sponsor's protocol ID
- 25293-0-01
- Status last verified
- Jun 2025
- First submitted
- 18 Jun 2025
- Last updated
- 29 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- the Study on AI-Integrated Fully Quantitative Treatment Decision and Surgical Planning System for Liver Cancer
- ClinicalTrials.gov ID
- NCT07042841
- Acronym / sponsor ID
- 25293-0-01
- Conditions
- Liver Cancer, Adult
- Keywords
- Liver Cancer, Artificial Intelligence, Computer-Aided Diagnosis, Multimodal Image Fusion, Pathology-CT Registration
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Beijing Tsinghua Chang Gung HospitalUniversity, hospital or other
- Study start
- 30 Jun 2015 (actual)
- Primary completion
- 30 Dec 2024 (actual)
- Study completion
- 30 Oct 2027 (estimated)
- First posted
- 29 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to develop and validate an integrated AI-powered system for liver cancer that includes intelligent tumor boundary detection, micro-metastasis prediction, staging, treatment decision-making, and surgical planning. The system builds upon prior 3D reconstructions of liver, vessels, and bile ducts. In a retrospective multi-center, self-controlled, fully crossed multi-reader multi-case clinical trial, the researchers will compare diagnostic accuracy, staging, and planning performance between AI-assisted reads and conventional reads using CT images and pathological gold standards.
Detailed description
Background Precise tumor boundary definition on preoperative imaging is often unreliable, and regional disparities in healthcare resources limit personalized decision-making. Currently, no comprehensive AI system integrates imaging, pathology, staging, decision, and surgical planning for liver cancer.
Objectives 1: Develop a pathological-tumor-boundary evaluation system based on whole-slide pathology and CT registration. 2: Build models to predict tumor boundary and satellite micro-metastasis from CT images using deep learning and radiomics. 3: Integrate staging modules (Child-Pugh, ECOG-PS, CNLC/BCLC) and generate individualized treatment recommendations. 4: Create a surgical planning platform that calculates liver remnant volume, vascular invasion metrics, anatomical variants, and performs virtual resections. 5: Validate the system in a retrospective self-controlled multi-reader multi-case study across multiple centers.
Methods Tumor boundary \& micro-metastasis prediction: Register 3D whole-slide pathology of resected specimens to preoperative CT using multiplanar reconstruction; pathologists annotate tumor and satellite lesions; train deep-learning models to predict pathological boundaries and micro-metastasis regions. Validate in 100 cases with pathological CT comparison. Decision modules: Automatically compute Child-Pugh and ECOG-PS scores from labs and records; integrate with tumor metrics and PV invasion to achieve CNLC/BCLC staging and generate decision suggestions. Surgical planning: Calculate functional liver volume requirements per consensus, estimate standard liver volume (SLV), tumor-bearing segment volume, future liver remnant (FLR/SLV), flag unsafe resections; analyze vascular invasion level, length, and perfusion territory; detect portal/bile anatomical variants for injury warnings; perform virtual anatomical and non-anatomical resections with margin control and risk predictions. Clinical validation: Conduct a retrospective, fully-crossed multi-reader multi-case trial: randomized CT reading by surgeons/radiologists with and without software assistance, separated by washout periods. Evaluate primary endpoints: AFROC-AUC for lesion detection, LROC-AUC for HCC diagnosis. Secondary endpoints include sensitivity, specificity, ROC-AUC, diagnostic consistency (Kappa), size/count accuracy (<5% error), staging concordance, and reading time.
Study population Adult participants with dynamic contrast-enhanced liver CT, including HCC-positive, HCC-negative lesion-positive, and lesion-negative cases. Exclude cases with incomplete liver imaging, heavy noise, prior liver resection, or unreadable CT.
Statistical analysis Compare AUCs between AI-assisted and manual reading; non-inferiority established if lower bound of 95% CI > 0. Secondary metrics include sensitivity, specificity, consistency, reading time, and staging accuracy.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Year
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| HCC Positive Group | – | –HCC-positive with liver lesion |
| HCC Negative Lesion Group | – | –HCC-negative with liver lesion |
| Non-Lesion Control Group | – | –No liver lesion |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study aims to include adult patients (≥18 years old) who have undergone dynamic contrast-enhanced CT imaging of the liver. The population will consist of three groups: HCC-positive group (Group 1): Patients with liver lesions diagnosed as suspected hepatocellular carcinoma (HCC) based on CT imaging reports, and with complete clinical records for assessment of CNLC staging, Child-Pugh score, and extrahepatic metastasis. HCC-negative with liver lesion group (Group 2): Patients with liver lesions identified on CT imaging but without HCC diagnosis, and without clear evidence of extrahepatic metastasis. Non-lesion control group (Group 3): Patients with no liver lesions identified on CT imaging, serving as the control group. All participants will have a CT imaging slice thickness of ≤5 mm, and must be capable of providing complete imaging reports and medical records. The study will target approximately 300 participants across these groups.
Key inclusion criteria (1 listed)
- 1. Dynamic contrast-enhanced CT scan of the liver is available. 2. Age ≥ 18 years. 3. CT image slice thickness ≤ 5 mm. 4. An official CT report is available. 5. Meets at least one of the following subcategories:HCC-positive with liver lesion: CT report suggests suspected hepatocellular carcinoma (HCC), and complete hospitalization records are available to assess CNLC staging (including performance status, Child-Pugh classification, and extrahepatic metastasis). HCC-negative with liver lesion: CT report indicates at least one liver-occupying lesion, but not suspected to be HCC. No liver lesion: CT report indicates no liver-occupying lesion.
Key exclusion criteria (1 listed)
- 1. Incomplete liver coverage in CT images. 2. Severe noise artifacts in CT images. 3. History of liver resection or significant anatomical/volume alterations of the liver. 4. Deemed unsuitable for inclusion by the investigator.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic accuracy of the intelligent liver cancer diagnosis and surgical planning system
Time frame: At the time of surgery.
The diagnostic accuracy will be evaluated by comparing the system's prediction of tumor boundaries and microvascular invasion with pathological gold standards.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jun 2025.
- Individual participant dataSharing planned: No
Related trials for Liver Cancer, Adult
RecruitingPhase 2
DB-1311 in Combination With BNT327 or DB-1305 in Advanced/Metastatic Solid Tumors
Solid Tumors
RecruitingPhase 2
18F-Fibroblast Activation Protein Inhibitor ([18F]FAPI-74) PET Imaging for Cancer Detection
Sarcoma, Small Cell Lung Cancer or Extrapulmonary Neuroendocrine Cancer (EP-NEC)
Not yet recruitingPhase 3
A Study of TACE in Combination With SHR-8068, Adebrelimab, and Bevacizumab Versus TACE in Participants With Incurable Hepatocellular Carcinoma
Hepatocellular Carcinoma
Not yet recruitingObservational study
Immune-Related Adverse Events and Clinical Outcomes of First-Line Immunotherapy in Egyptian Patients With Unresectable Hepatocellular Carcinoma
Hepatocellular Carcinoma, Hepatocellular Carcinoma (HCC)
Not yet recruitingPhase 2
An Exploratory Study of Single-Dose Neoadjuvant Nivolumab Without Surgical Delay in Patients With Resectable HCC
Hepato Cellular Carcinoma (HCC), Immune Checkpoint Inhibitor
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Shuo Jin, PhD principal investigator
Beijing Tsinghua Chang Gung Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Liver Cancer, Adult, run by Beijing Tsinghua Chang Gung Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 29 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of the intelligent liver cancer diagnosis and surgical planning system (time frame: At the time of surgery.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jun 2025; AzTrials last refreshed its copy on 30 Oct 2026.