Clinical trial NCT06859840: Abdominal CT Combined With AI for Early Screening of Liver Cancer
Official title: Clinical Research on the Use of Abdominal CT Combined With AI for Early Screening
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Hepatocellular Carcinoma
Keywords: Artificial Intelligence
Hepatocellular Carcinoma
LIDAR
Lead sponsor: Zhejiang University
Sponsor class: OTHER
First posted: 2025-03-05
Enrollment: 10000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: The AI model will be used to identify imaging findings suggestive of liver space-occupying lesions. Senior liver specialists from our hospital's hepatobiliary-pancreatic surgery and radiology departments, who have expertise in AI research and clinical application, will delineate these liver lesions in conjunction with pathological results to develop and refine the model. After model establishment, external multicenter validation will be conducted to assess the model's stability in detecting focal liver lesions across diverse populations. For cases where the model indicates malignancy without clear evidence from medical history or other data, follow-up will be performed to confirm the true value through pathological results. The primary focus will be to evaluate whether the model can improve the detection rate of focal liver lesions requiring intervention in various complex real-world scenarios.
Primary purpose: DIAGNOSTIC

Brief summary: This study plans to utilize multiphase contrast-enhanced and non-contrast CT（Computed Tomography） images from 10000 pathologically confirmed liver tumor patients at our hospital. An AI（artificial intelligence） model will be used to outline the 3D contours of liver masses, which will then be refined by radiologists and hepatobiliary-pancreatic surgeons to enhance model accuracy. By incorporating more imaging data, the model's recognition capabilities will be improved, laying the groundwork for prospective clinical trials and aiming to establish a superior AI model for early liver cancer screening based on CT imaging.

Detailed description: This research project intends to utilize multiphase contrast-enhanced and non-contrast CT images from 10000 patients with a full spectrum of liver tumors (such as HCC（hepatocellular carcinoma）, ICC（intrahepatic cholangiocarcinoma ）, META（Metastasis）, etc.), confirmed by the pathological gold standard at our hospital. Through a pre-established AI model, the 3D contours of various liver masses will be delineated. In collaboration with senior physicians from our hospital's radiology department and hepatobiliary pancreatic surgery department, the AI-drawn contours will be refined to obtain more accurate 3D mass models, thereby enhancing the validation efficacy of the model. By incorporating more radiological data, the precision of the model will be improved, boosting its recognition capabilities and laying a solid foundation for subsequent prospective clinical trials. The ultimate goal is to establish a superior AI model for early screening of liver cancer based on CTimaging.

Eligibility criteria: Inclusion Criteria:

* From 2019 to 2030, our hospital has collected non-contrast and contrast-enhanced CT images from patients with a full spectrum of liver tumors (such as HCC, ICC, META, etc.), all confirmed by the pathological gold standard

Exclusion Criteria:

* Patients who have undergone upper abdominal surgery. Examples include post-ERCP (Endoscopic Retrograde Cholangiopancreatography) for the pancreas, post-external drainage surgery, esophageal surgery, and gastrectomy, among others.
* Patients who have received systemic treatments such as chemotherapy or traditional Chinese medicine. Examples include chemotherapy for lymphoma, chemotherapy for leukemia, chemotherapy for lung cancer, and comprehensive treatment for liver cancer, etc.
* Patients with poor-quality CT images. Examples include convolution artifacts caused by the inability to place hands on the sides of the body and respiratory artifacts due to poor breath-holding, etc.

Primary outcomes:
measure: Detection efficiency in liver tumor assisted by LIDAR
timeFrame: Complete the statistics within six months after the patient is fully enrolled, and it is expected to take 2 years from the start of the study
measure: Detection efficiency in liver tumor assisted by LIDAR
timeFrame: Complete the statistics within six months after the patient is fully enrolled, and it is expected to take 2 years from the start of the study
measure: Detection efficiency in liver tumor assisted by LIDAR
timeFrame: Complete the statistics within six months after the patient is fully enrolled, and it is expected to take 2 years from the start of the study
Secondary outcomes:
measure: TNM stage
timeFrame: 1 day (evaluate through CT imaging before surgery)
measure: OS
timeFrame: From diagnosis of liver cancer to 5 years later
Other outcomes:
Locations:
city: Hangzhou
country: China
facility: the First Affiliated Hospital, School of Medicine, Zhejiang University
state: Zhejiang
Study officials:
References: