Abdominal CT Combined With AI for Early Screening of Liver Cancer

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

10000 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-03-15

Study Completion Date

2030-09-15

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Hepatocellular Carcinoma

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

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 Study Purpose

DIAGNOSTIC

Blinding Strategy

SINGLE

Participants

Study Groups

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LIDAR

Group Type EXPERIMENTAL

LIDAR

Intervention Type DEVICE

Using the LIDAR model to assist in image interpretation, patients with positive results are recalled for further examination based on the LIDAR output information and the original image interpretation, to obtain pathological results and long-term follow-up.

Control

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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LIDAR

Using the LIDAR model to assist in image interpretation, patients with positive results are recalled for further examination based on the LIDAR output information and the original image interpretation, to obtain pathological results and long-term follow-up.

Intervention Type DEVICE

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes