Liver MalignancyLEAFNCT06859840
LEAF (Liver Tumor dEtection And classiFication AI)
A study testing LEAF(Liver tumor dEtection And classiFication AI) in people with Liver Malignancy, run by Zhejiang University at 1 site in China.
Trial summary
This study aims to assess the feasibility of leveraging non-contrast CT and artificial intelligence to detect liver cancer in consecutive real-world patients. To this end, we deploy LEAF in a prospective real-world clinical setting for real-time monitoring, with a particular focus on flagging cases with liver cancer… Read more about this study
- Who is it for?
- Ages 18 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Liver Malignancy
- What is being tested?
- LEAF(Liver tumor dEtection And classiFication AI)
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,500 participants
- Where
- 1 siteChina
- Study dates
- 17 Jul 2026to 10 Nov 2026
- Phase
- Interventional
- 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 to 90 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Age range 18 years and above;
- Underwent non-contrast chest or abdominal CT examination with liver coverage;
- Patients with an established diagnosis of cirrhosis;
- Patients with an established diagnosis of extrahepatic cancer.
Who cannot join
- Patients who have been diagnosed with malignant liver tumor;
- Patients who underwent liver transplantation;
- Low quality image, severe artifacts and noise.
Trial locations and contacts
1 site in 1 country.
China1 site
the First Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, 310009
About this study
Official title: Clinical Research on the Use of Non-contrast CT Combined With AI for Early Screening for Liver Malignancy
This study aims to assess the feasibility of leveraging non-contrast CT and artificial intelligence to detect liver cancer in consecutive real-world patients. To this end, we deploy LEAF in a prospective real-world clinical setting for real-time monitoring, with a particular focus on flagging cases with liver cancer that may be missed by routine clinical workflow.
Read the detailed description
This prospective real-world trial will be conducted at FAHZU, a high-volume tertiary medical center in mainland China.
LEAF will be deployed within the hospital information system through the DAMO Intelligent Medical Imaging interface, allowing it to flag potential liver lesions in real time. Approximately 2500 consecutive patients undergoing non-contrast CT examinations will be enrolled starting in July 2026. All incoming non-contrast chest and abdominal CT scans will be simultaneously reviewed by radiologists in routine clinical workflow and processed by LEAF in real-time. Daily logs of LEAF-positive alerts will be maintained by the research team. A prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case to assess whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice. The standard radiology workflow will not be altered by the study, and LEAF will be evaluated as a risk-stratification and case-flagging tool rather than a replacement for radiologist interpretation.
- Conditions
- Liver Malignancy
- Being tested
- LEAF(Liver tumor dEtection And classiFication AI)
- Keywords
- Artificial Intelligence, liver malignancy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
LEAF(Liver tumor dEtection And classiFication AI)Device
The LEAF (Liver tumor dEtection And classiFication AI) model will assist in image interpretation. Patients with positive results for liver malignancy while not reported in standard-of-care CT report will be reviewed by a prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case and decide whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice, while remaining blinded to the LEAF results. The standard radiology workflow will not be altered by the study, and LEAF will be evaluated as a risk-stratification and case-flagging tool rather than a replacement for radiologist interpretation.
| Study group | Role | What they receive |
|---|---|---|
| LEAF | Experimental | Patients diagnosed with liver cirrosis or those with extrahepatic malignant tumors will be enrolled within three weeks. Non-contrast chest and abdominal CT scans will be simultaneously reviewed by radiologists in routine clinical workflow and processed by LEAF in real-time. Daily logs of LEAF-positive alerts will be maintained by the research team. A prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case to assess whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice. Receives: Device: LEAF(Liver tumor dEtection And classiFication AI) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Detection accuracy in liver tumor assisted by LEAF (Liver tumor dEtection And classiFication AI)
Sensitivity, specificity of liver malignancy identification (defined as liver malignancy vs. liver benign tumor and non-tumor)
Measured: Within 4 weeks after enrollment
Secondary outcomes (2)
AI diagnostic performance: patient-level Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of liver malignancy identification
Measured: Within 4 weeks after enrollment
Clinical utility: number of AI-detected and originally overlooked liver malignant lesions
recalled and pathologically confirmed
Measured: Within 4 weeks after enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Mar 2025
- Study start17 Jul 2026
- Primary completion12 Aug 2026
- Study completion10 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Liver MalignancyStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- LEAF is a deep learning-based model that takes non-contrast CT scans as input and performs both tri-class diagnosis (benign, malignant, or non-tumor) and potential lesion localization for liver tumors.
- Masking
- None (open label)
- Enrolment
- 2,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zhejiang University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, TingBo Liang, Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06859840
- Sponsor's protocol ID
- LEAF
- Acronym
- LEAF
- Status last verified
- Mar 2026
- First submitted
- 28 Feb 2025
- Last updated
- 15 Sep 2026
- 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 15 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Research on the Use of Non-contrast CT Combined With AI for Early Screening for Liver Malignancy
- ClinicalTrials.gov ID
- NCT06859840
- Acronym / sponsor ID
- LEAF / LEAF
- Conditions
- Liver Malignancy
- Keywords
- Artificial Intelligence, liver malignancy
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 2,500 participants (estimated)
- Lead sponsor
- Zhejiang UniversityUniversity, hospital or other
- Study start
- 17 Jul 2026 (actual)
- Primary completion
- 12 Aug 2026 (actual)
- Study completion
- 10 Nov 2026 (estimated)
- First posted
- 5 Mar 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 assess the feasibility of leveraging non-contrast CT and artificial intelligence to detect liver cancer in consecutive real-world patients. To this end, we deploy LEAF in a prospective real-world clinical setting for real-time monitoring, with a particular focus on flagging cases with liver cancer that may be missed by routine clinical workflow.
Detailed description
This prospective real-world trial will be conducted at FAHZU, a high-volume tertiary medical center in mainland China.
LEAF will be deployed within the hospital information system through the DAMO Intelligent Medical Imaging interface, allowing it to flag potential liver lesions in real time. Approximately 2500 consecutive patients undergoing non-contrast CT examinations will be enrolled starting in July 2026. All incoming non-contrast chest and abdominal CT scans will be simultaneously reviewed by radiologists in routine clinical workflow and processed by LEAF in real-time. Daily logs of LEAF-positive alerts will be maintained by the research team. A prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case to assess whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice. The standard radiology workflow will not be altered by the study, and LEAF will be evaluated as a risk-stratification and case-flagging tool rather than a replacement for radiologist interpretation.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- LEAF is a deep learning-based model that takes non-contrast CT scans as input and performs both tri-class diagnosis (benign, malignant, or non-tumor) and potential lesion localization for liver tumors.
| Arm | Type | Receives |
|---|---|---|
| LEAF | Experimental | Device: LEAF(Liver tumor dEtection And classiFication AI)Patients diagnosed with liver cirrosis or those with extrahepatic malignant tumors will be enrolled within three weeks. Non-contrast chest and abdominal CT scans will be simultaneously reviewed by radiologists in routine clinical workflow and processed by LEAF in real-time. Daily logs of LEAF-positive alerts will be maintained by the research team. A prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case to assess whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Age range 18 years and above;
- Underwent non-contrast chest or abdominal CT examination with liver coverage;
- Patients with an established diagnosis of cirrhosis;
- Patients with an established diagnosis of extrahepatic cancer.
Key exclusion criteria (3 listed)
- Patients who have been diagnosed with malignant liver tumor;
- Patients who underwent liver transplantation;
- Low quality image, severe artifacts and noise.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| LEAF(Liver tumor dEtection And classiFication AI)The LEAF (Liver tumor dEtection And classiFication AI) model will assist in image interpretation. Patients with positive results for liver malignancy while not reported in standard-of-care CT report will be reviewed by a prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case and decide whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice, while remaining blinded to the LEAF results. The standard radiology workflow will not be altered by the study, and LEAF will be evaluated as a risk-stratification and case-flagging tool rather than a replacement for radiologist interpretation. | Device | LEAF |
Primary outcome
Detection accuracy in liver tumor assisted by LEAF (Liver tumor dEtection And classiFication AI)
Time frame: Within 4 weeks after enrollment
Sensitivity, specificity of liver malignancy identification (defined as liver malignancy vs. liver benign tumor and non-tumor)
2 secondary outcomes
AI diagnostic performance: patient-level Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of liver malignancy identification
Time frame: Within 4 weeks after enrollment
Clinical utility: number of AI-detected and originally overlooked liver malignant lesions
Time frame: Within 4 weeks after enrollment
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 15 Sep 2026.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing LEAF(Liver tumor dEtection And classiFication AI) in people with Liver Malignancy, run by Zhejiang University 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 4 inclusion and 3 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: under way, not looking for participants, as of the sponsor's last update on 15 Sep 2026. 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?
Detection accuracy in liver tumor assisted by LEAF (Liver tumor dEtection And classiFication AI) (time frame: Within 4 weeks after enrollment). 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 15 Sep 2026; AzTrials last refreshed its copy on 27 Oct 2026.