Hepatocellular Carcinoma (HCC)NCT07153783
Interventional AI-Human Collaboration for Liver Tumor Diagnosis
A study testing AI-human collaboration for CE-CTs diagnosis in people with Hepatocellular Carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (Icc), run by Shengjing Hospital at 1 site in China.
Trial summary
Recent advances in artificial intelligence (AI), particularly deep learning technology, have transformed medical imaging analysis. AI systems have demonstrated diagnostic performance comparable to or exceeding that of expert radiologists in specific tasks. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Hepatocellular Carcinoma (HCC), Intrahepatic Cholangiocarcinoma (Icc), Hepatic Metastasis
- What is being tested?
- AI-human collaboration for CE-CTs diagnosis
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 10,333 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2025to 7 Nov 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
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
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Age range 18 years and above
- Underwent dynamic contrast-enhanced abdominal CT examination with liver coverage
- Imaging must include at least three required phases: non-contrast, arterial phase, and venous phase; an delayed phase is optional
- Complete imaging data that meet AI system analysis requirements.
Who cannot join
- History of recent upper-abdominal surgery (within 30 days) or major hepatobiliary-pancreatic surgery affecting liver evaluation (e.g., liver transplantation or Whipple procedure); patients with prior simple cholecystectomy or single-lesion interventional procedures are not excluded
- History of recent hepatic trauma (within 30 days)
- Poor image quality or severe noise artifacts (e.g., metal or motion artifacts)
- Missing required imaging phases (required at least non-contrast, arterial, and venous phases) or inadequate scan range (e.g., lower-abdomen CT such as pelvic or rectal scans not covering the liver)
Trial locations and contacts
1 site in 1 country.
China1 site
Shengjing Hospital of China Medical University
Shenyang, Liaoning, 110004
About this study
Official title: AI-human Collaborative Diagnosis of Liver Tumors Using CE-CT
Recent advances in artificial intelligence (AI), particularly deep learning technology, have transformed medical imaging analysis. AI systems have demonstrated diagnostic performance comparable to or exceeding that of expert radiologists in specific tasks. Liver-focused AI diagnostic systems have achieved promising results in multi-center validations; however, these retrospective studies have not yet addressed two critical gaps. First, large-scale prospective trials are required to establish real-world clinical effectiveness. Second, it remains unclear whether AI can be organically embedded into clinical diagnostic workflows to reshape diagnostic and therapeutic pathways, particularly by enhancing the detection and follow-up of hepatic malignancies and ultimately improving patient outcomes.
Read the detailed description
This study aims to evaluate the effectiveness of AI-human collaboration in liver tumor diagnosis by embedding real-time AI analysis into conventional multiphasic contrast-enhanced CT (CE-CT) workflows. Specifically, this prospective validation trial will assess diagnostic performance in detecting and characterizing hepatic lesions, particularly malignancies, evaluate the feasibility and efficiency of workflow integration, and determine the potential clinical impact on treatment decision-making and patient management.
- Conditions
- Hepatocellular Carcinoma (HCC), Intrahepatic Cholangiocarcinoma (Icc), Hepatic Metastasis, Hepatic Hemangioma, Cyst, Focal Nodular Hyperplasia
- Being tested
- AI-human collaboration for CE-CTs diagnosis
- Keywords
- Dynamic Contrast-Enhanced CT, Focal Liver Lesions
- Medical subject headings
- Carcinoma, Hepatocellular, Cholangiocarcinoma, Cirrhosis, Familial, with Pulmonary Hypertension, Cysts, Focal Nodular Hyperplasia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-human collaboration for CE-CTs diagnosisDiagnostic test
The system automatically processes all eligible same-day scans and generates results for review the following day. To maintain efficient AI-human collaboration while preserving the standard clinical workflow, the conventional radiological interpretation process remains unchanged (first-line radiologists provide initial reports followed by senior radiologists' review). A dedicated senior radiologist then evaluates any discordances between AI findings and primary radiological report. For complex cases, the review process escalates to a consensus review panel (i.e., pre-designated senior radiologists, Multidisciplinary Team (MDT)). The MDT can recommend clinical interventions including follow-up (e.g., additional imaging examinations, active surveillance), surgical procedures, or adjustments to adjuvant therapy (initiation or modification of treatment regimens). All discordant cases and their outcomes are systematically documented for longitudinal tracking and follow-up analysis.
| Study group | Role | What they receive |
|---|---|---|
| AI-human collaboration in CE-CT diagnosis for liver lesions | Experimental | In the prospective analysis phase, patients undergo routine Multiphasic Contrast-Enhanced Computed Tomography (CE-CT) imaging. The scans are evaluated through two parallel pathways: standard radiologist interpretation (without AI input) and independent AI analysis. When diagnostic discrepancies occur, a senior radiologist or multidisciplinary expert panel reviews the case and provides the definitive diagnosis. Receives: Diagnostic Test: AI-human collaboration for CE-CTs diagnosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of the AI System for malignancy diagnosis
Measures the patient-level diagnostic accuracy of the AI system for differentiating malignant vs. non-malignant lesions. The primary metric is the Area under the Receiver Operating Characteristic Curve (AUC). The primary analysis will test the one-sided superiority hypothesis H1: AUC > 0.90 against H0: AUC <= 0.90. The trial will be considered successful if the lower bound of the 95% Confidence Interval (CI) for the AUC is greater than 0.90.
Measured: Up to 90 days
Secondary outcomes (4)
Secondary diagnostic performance
Measures the patient-level diagnostic performance of the AI system for malignant versus non-malignant classification. Metrics include sensitivity, specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV). These will be calculated from the continuous probability score using a fixed operating point prior to prospective analysis.
Measured: Up to 90 days
Lesion screening performance
Measures the patient-level screening ability of the AI system to distinguish patients with any lesion from those with no lesions. This is a binary classification task (AUC) comparing lesion patient (malignant or benign) versus no lesion (normal liver or diffuse disease only).
Measured: Up to 90 days
Detection discordance
Measures the number of FLLs identified by the AI-human collaborative workflow that were overlooked by the initial radiologist report. An overlooked lesion is defined as an event meeting all three criteria: (1) detected by the AI system; (2) not described in the initial radiological report; (3) confirmed as a true lesion by senior radiologist/MDT re-review.
Measured: Up to 90 days
Amended radiological report
Measures the number of formal addenda issued to finalized radiology reports. An amended report is defined as a formal addendum that explicitly corrects a diagnosis or adds a previously missed finding based on the AI-human collaborative review.
Measured: Up to 90 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Sep 2025
- Study start1 Sep 2025
- Primary completion29 Oct 2025
- Study completion7 Nov 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Hepatocellular Carcinoma (HCC)Studies of the same condition, open and under way.
- More trials run by Shengjing HospitalThe sponsor's other registered studies.
- 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
- Masking
- None (open label)
- Enrolment
- 10,333 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shengjing Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yu Shi, Shengjing Hospital
- Investigators
- Yu Shi, MD PhD, Principal investigator, Shengjing Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07153783
- Sponsor's protocol ID
- SH-CMU-FLL-Intervention
- Status last verified
- Nov 2025
- First submitted
- 26 Aug 2025
- Last updated
- 18 Nov 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: We plan to share IPD related to abdominal dynamic-contrast enhanced CT scans and clinical outcomes for hepatic tumor diagnosis.
Publications
Ding W, Meng Y, Ma J, Pang C, Wu J, Tian J, Yu J, Liang P, Wang K. Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions. J Hepatol. 2025 Aug;83(2):426-439. doi: 10.1016/j.jhep.2025.01.011. Epub 2025 Jan 21.
PubMed 39848548Ying H, Liu X, Zhang M, Ren Y, Zhen S, Wang X, Liu B, Hu P, Duan L, Cai M, Jiang M, Cheng X, Gong X, Jiang H, Jiang J, Zheng J, Zhu K, Zhou W, Lu B, Zhou H, Shen Y, Du J, Ying M, Hong Q, Mo J, Li J, Ye G, Zhang S, Hu H, Sun J, Liu H, Li Y, Xu X, Bai H, Wang S, Cheng X, Xu X, Jiao L, Yu R, Lau WY, Yu Y, Cai X. A multicenter clinical AI system study for detection and diagnosis of focal liver lesions. Nat Commun. 2024 Feb 7;15(1):1131. doi: 10.1038/s41467-024-45325-9.
PubMed 38326351Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med. 2023 Dec;29(12):3033-3043. doi: 10.1038/s41591-023-02640-w. Epub 2023 Nov 20.
PubMed 37985692Zhang X, Li C, Han X, Yin X, Zhang C, Zhou J, Cao K, Liu Q, Hong Y, Gao Y, Cui B, Zhang Q, Qiao J, Sun M, Liu W, Gao Y, Zhong S, Bai R, Jiang T, Humbert O, Ourselin S, Zuluaga MA, Yan K, Zhang Q, Hou Y, Liang T, Zhang L, Shi Y. Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial. Nat Med. 2026 Aug 19. doi: 10.1038/s41591-026-04589-y. Online ahead of print.
PubMed 42618635
Study at a glance
From the registry record, last updated by the sponsor on 18 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-human Collaborative Diagnosis of Liver Tumors Using CE-CT
- ClinicalTrials.gov ID
- NCT07153783
- Acronym / sponsor ID
- SH-CMU-FLL-Intervention
- Conditions
- Hepatocellular Carcinoma (HCC), Intrahepatic Cholangiocarcinoma (Icc), Hepatic Metastasis, Hepatic Hemangioma, Cyst, Focal Nodular Hyperplasia
- Keywords
- Dynamic Contrast-Enhanced CT, Focal Liver Lesions
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 10,333 participants (actual)
- Lead sponsor
- Shengjing HospitalUniversity, hospital or other
- Study start
- 1 Sep 2025 (actual)
- Primary completion
- 29 Oct 2025 (actual)
- Study completion
- 7 Nov 2025 (actual)
- First posted
- 4 Sep 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.
Recent advances in artificial intelligence (AI), particularly deep learning technology, have transformed medical imaging analysis. AI systems have demonstrated diagnostic performance comparable to or exceeding that of expert radiologists in specific tasks. Liver-focused AI diagnostic systems have achieved promising results in multi-center validations; however, these retrospective studies have not yet addressed two critical gaps. First, large-scale prospective trials are required to establish real-world clinical effectiveness. Second, it remains unclear whether AI can be organically embedded into clinical diagnostic workflows to reshape diagnostic and therapeutic pathways, particularly by enhancing the detection and follow-up of hepatic malignancies and ultimately improving patient outcomes.
Detailed description
This study aims to evaluate the effectiveness of AI-human collaboration in liver tumor diagnosis by embedding real-time AI analysis into conventional multiphasic contrast-enhanced CT (CE-CT) workflows. Specifically, this prospective validation trial will assess diagnostic performance in detecting and characterizing hepatic lesions, particularly malignancies, evaluate the feasibility and efficiency of workflow integration, and determine the potential clinical impact on treatment decision-making and patient management.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-human collaboration in CE-CT diagnosis for liver lesions | Experimental | Diagnostic Test: AI-human collaboration for CE-CTs diagnosisIn the prospective analysis phase, patients undergo routine Multiphasic Contrast-Enhanced Computed Tomography (CE-CT) imaging. The scans are evaluated through two parallel pathways: standard radiologist interpretation (without AI input) and independent AI analysis. When diagnostic discrepancies occur, a senior radiologist or multidisciplinary expert panel reviews the case and provides the definitive diagnosis. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Age range 18 years and above
- Underwent dynamic contrast-enhanced abdominal CT examination with liver coverage
- Imaging must include at least three required phases: non-contrast, arterial phase, and venous phase; an delayed phase is optional
- Complete imaging data that meet AI system analysis requirements.
Key exclusion criteria (4 listed)
- History of recent upper-abdominal surgery (within 30 days) or major hepatobiliary-pancreatic surgery affecting liver evaluation (e.g., liver transplantation or Whipple procedure); patients with prior simple cholecystectomy or single-lesion interventional procedures are not excluded
- History of recent hepatic trauma (within 30 days)
- Poor image quality or severe noise artifacts (e.g., metal or motion artifacts)
- Missing required imaging phases (required at least non-contrast, arterial, and venous phases) or inadequate scan range (e.g., lower-abdomen CT such as pelvic or rectal scans not covering the liver)
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 |
|---|---|---|
| AI-human collaboration for CE-CTs diagnosisThe system automatically processes all eligible same-day scans and generates results for review the following day. To maintain efficient AI-human collaboration while preserving the standard clinical workflow, the conventional radiological interpretation process remains unchanged (first-line radiologists provide initial reports followed by senior radiologists' review). A dedicated senior radiologist then evaluates any discordances between AI findings and primary radiological report. For complex cases, the review process escalates to a consensus review panel (i.e., pre-designated senior radiologists, Multidisciplinary Team (MDT)). The MDT can recommend clinical interventions including follow-up (e.g., additional imaging examinations, active surveillance), surgical procedures, or adjustments to adjuvant therapy (initiation or modification of treatment regimens). All discordant cases and their outcomes are systematically documented for longitudinal tracking and follow-up analysis. | Diagnostic test | AI-human collaboration in CE-CT diagnosis for liver lesions |
Primary outcome
Diagnostic accuracy of the AI System for malignancy diagnosis
Time frame: Up to 90 days
Measures the patient-level diagnostic accuracy of the AI system for differentiating malignant vs. non-malignant lesions. The primary metric is the Area under the Receiver Operating Characteristic Curve (AUC). The primary analysis will test the one-sided superiority hypothesis H1: AUC > 0.90 against H0: AUC <= 0.90. The trial will be considered successful if the lower bound of the 95% Confidence Interval (CI) for the AUC is greater than 0.90.
4 secondary outcomes
Secondary diagnostic performance
Time frame: Up to 90 days
Lesion screening performance
Time frame: Up to 90 days
Detection discordance
Time frame: Up to 90 days
Amended radiological report
Time frame: Up to 90 days
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Nov 2025.
- Background: PubMed 39848548Ding W, Meng Y, Ma J, Pang C, Wu J, Tian J, Yu J, Liang P, Wang K. Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions. J Hepatol. 2025 Aug;83(2):426-439. doi: 10.1016/j.jhep.2025.01.011. Epub 2025 Jan 21.
- Background: PubMed 38326351Ying H, Liu X, Zhang M, Ren Y, Zhen S, Wang X, Liu B, Hu P, Duan L, Cai M, Jiang M, Cheng X, Gong X, Jiang H, Jiang J, Zheng J, Zhu K, Zhou W, Lu B, Zhou H, Shen Y, Du J, Ying M, Hong Q, Mo J, Li J, Ye G, Zhang S, Hu H, Sun J, Liu H, Li Y, Xu X, Bai H, Wang S, Cheng X, Xu X, Jiao L, Yu R, Lau WY, Yu Y, Cai X. A multicenter clinical AI system study for detection and diagnosis of focal liver lesions. Nat Commun. 2024 Feb 7;15(1):1131. doi: 10.1038/s41467-024-45325-9.
- Background: PubMed 37985692Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med. 2023 Dec;29(12):3033-3043. doi: 10.1038/s41591-023-02640-w. Epub 2023 Nov 20.
- Derived from this study: PubMed 42618635Zhang X, Li C, Han X, Yin X, Zhang C, Zhou J, Cao K, Liu Q, Hong Y, Gao Y, Cui B, Zhang Q, Qiao J, Sun M, Liu W, Gao Y, Zhong S, Bai R, Jiang T, Humbert O, Ourselin S, Zuluaga MA, Yan K, Zhang Q, Hou Y, Liang T, Zhang L, Shi Y. Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial. Nat Med. 2026 Aug 19. doi: 10.1038/s41591-026-04589-y. Online ahead of print.
- Individual participant dataSharing planned: Yes. We plan to share IPD related to abdominal dynamic-contrast enhanced CT scans and clinical outcomes for hepatic tumor diagnosis.
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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.
Yu Shi, MD PhD principal investigator
Shengjing Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-human collaboration for CE-CTs diagnosis in people with Hepatocellular Carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (Icc), run by Shengjing 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 4 inclusion and 4 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: finished, as of the sponsor's last update on 18 Nov 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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 AI System for malignancy diagnosis (time frame: Up to 90 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07153783, which also lists 4 linked publications.
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 18 Nov 2025.