Interventional AI-Human Collaboration for Liver Tumor Diagnosis

NCT07153783 | Completed DMC

• 1 other identifier: SH-CMU-FLL-Intervention
• Study Type: interventional
• Target: 10,333
• Locations: 1 country
• Sites: 1

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.

Conditions

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

Keywords

Dynamic Contrast-Enhanced CT; Focal Liver Lesions

Outcome Measures

Primary Outcomes (1)

• 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.

  Up to 90 days

Secondary Outcomes (4)

• Secondary diagnostic performance

  Up to 90 days

• Lesion screening performance

  Up to 90 days

• Detection discordance

  Up to 90 days

• Amended radiological report

  Up to 90 days

Study Arms (1)

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.

Diagnostic Test: AI-human collaboration for CE-CTs diagnosis

Interventions

AI-human collaboration for CE-CTs diagnosis 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.

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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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)

Sponsors & Collaborators

• Shengjing Hospital: lead

Study Sites (1)

Shengjing Hospital of China Medical University

Shenyang, Liaoning, 110004, China

Location

Related Publications (3)

• 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.

  PMID: 39848548 BACKGROUND

• 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.

  PMID: 38326351 BACKGROUND

• 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.

  PMID: 37985692 BACKGROUND

MeSH Terms

Conditions

Carcinoma, Hepatocellular; Cholangiocarcinoma; Cirrhosis, Familial, with Pulmonary Hypertension; Cysts; Focal Nodular Hyperplasia

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Liver Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Digestive System Diseases; Liver Diseases; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Officials

• Yu Shi, MD PhD

  Shengjing Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Deputy director of department of radiology

Study Record Dates

• First Submitted: August 26, 2025
• First Posted: September 4, 2025
• Study Start: September 1, 2025
• Primary Completion: October 29, 2025
• Study Completion: November 7, 2025
• Last Updated: November 18, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL

Locations

CN (1)