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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.
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 research will be conducted over a period of two weeks. 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. for cases where the model indicates malignancy and CT report malignancy clinical Soc procedure will be followed, and all patients with none malignany reported by model or CT report will be follow up via telephone every three mouths. The ultimate goal is to establish a superior AI model for early screening of liver cancer based on CT imaging.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| LEAF | Experimental | Patients diagnosed with liver cirrosis or those with extrahepatic malignant tumors will be consecutively included, those who have already received treatment for hepatic malignancy and those with poor-quality CT images will be excluded. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| LEAF(Liver tumor dEtection And classiFication AI) | Device | Using the LEAF(Liver tumor dEtection And classiFication AI)model to assist in image interpretation, patients with positive results are recalled for further examination based on the LEAF output information and the original image interpretation, to obtain pathological results and long-term follow-up. |
| Measure | Description | Time Frame |
|---|---|---|
| Detection efficiency in liver tumor assisted by LEAF(Liver tumor dEtection And classiFication AI) | Sensitivity、Specificity、PPV | 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 |
| Detection efficiency in liver tumor assisted by LEAF(Liver tumor dEtection And classiFication AI) | PPV、NPV | 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 |
| Detection efficiency in liver tumor assisted by LEAF(Liver tumor dEtection And classiFication AI) | AUC | 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 | Description | Time Frame |
|---|---|---|
| TNM stage | Staging of liver cancer | 1 day (evaluate through CT imaging before surgery) |
| OS | overall survival | From diagnosis of liver cancer to 5 years later |
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Inclusion Criteria:
Age range 18 years and above; Patients with an established diagnosis of cirrhosis Patients with an established diagnosis of extrahepatic cancer
Exclusion criteria:
Patients who have been diagnosed with malignant liver tumor Patients who underwent liver transplantation. Low quality image, severe artifacts and noise
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| the First Affiliated Hospital, School of Medicine, Zhejiang University | Hangzhou | Zhejiang | 310009 | China |
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| ID | Term |
|---|---|
| D006528 | Carcinoma, Hepatocellular |
| ID | Term |
|---|---|
| D000230 | Adenocarcinoma |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
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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.
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| D009369 | Neoplasms |
| D008113 | Liver Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D004066 | Digestive System Diseases |
| D008107 | Liver Diseases |