LEAF(Liver Tumor dEtection And classiFication AI)

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.

Eligibility

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.

Conditions4

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