Interventional AI-Human Collaboration for Liver Tumor Diagnosis

NCT ID: NCT07153783

Last Updated: 2025-11-18

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 10333 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-09-01
• Study Completion Date: 2025-11-07

Brief 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. 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.

Conditions

Hepatocellular Carcinoma (HCC); Intrahepatic Cholangiocarcinoma (Icc); Hepatic Metastasis; Hepatic Hemangioma; Cyst; Focal Nodular Hyperplasia

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

AI-human collaboration in CE-CT diagnosis for liver lesions

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.

Group Type: EXPERIMENTAL

AI-human collaboration for CE-CTs diagnosis

Intervention Type: DIAGNOSTIC_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.

Interventions

AI-human collaboration for CE-CTs diagnosis

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

1. Age range 18 years and above

2. Underwent dynamic contrast-enhanced abdominal CT examination with liver coverage

3. Imaging must include at least three required phases: non-contrast, arterial phase, and venous phase; an delayed phase is optional

4. Complete imaging data that meet AI system analysis requirements.

Exclusion Criteria

1. 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

2. History of recent hepatic trauma (within 30 days)

3. Poor image quality or severe noise artifacts (e.g., metal or motion artifacts)

4. 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)

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Shengjing Hospital

OTHER

Sponsor Role: lead

Responsible Party

Yu Shi

Deputy director of department of radiology

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Yu Shi, MD PhD

Role: PRINCIPAL_INVESTIGATOR

Shengjing Hospital

Locations

Shengjing Hospital of China Medical University

Shenyang, Liaoning, China

Countries

China

References

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.

Reference Type: BACKGROUND

PMID: 39848548 (View on PubMed)

Ying 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.

Reference Type: BACKGROUND

PMID: 38326351 (View on PubMed)

Cao 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.

Reference Type: BACKGROUND

PMID: 37985692 (View on PubMed)

Other Identifiers

SH-CMU-FLL-Intervention

Identifier Type: -

Identifier Source: org_study_id