Interventional AI-Human Collaboration for Liver Tumor Diagnosis

China Medical University

Status: Completed

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

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

Study type: Interventional

Funder types: Other

Identifiers

NCT07153783

SH-CMU-FLL-Intervention

Details and patient eligibility

About

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.

Full 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.

Enrollment: 10,333 patients

Sex: All

Ages: 18 to 75 years old

Volunteers: Accepts Healthy Volunteers

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)

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

---

10,333 participants in 1 patient group

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

Experimental group

Description:

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.

Treatment:

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