The study will prospectively recruit patients with chronic liver disease and liver cancer for static CT scans to establish a high-definition CT database. Combining clinical data and pathological information, artificial intelligence technology will be utilized to construct models for assessing liver function and liver cirrhosis, as well as predicting microvascular invasion (MVI).

Liver cancer is a common disease that seriously endangers public health in China, and CT technology is particularly critical in the diagnosis and treatment of liver cancer. Most patients with liver cancer in China are complicated with liver cirrhosis, and the treatment principle is a comprehensive model based on surgical resection. The main problem in the perioperative period of hepatectomy is to accurately evaluate the grade of cirrhosis, liver reserve function and predict microvascular invasion (MVI) before operation. In view of these problems, this project plans to develop a set of auxiliary decision-making system for the perioperative period of hepatectomy of liver cancer based on static CT and artificial intelligence technology and combined with expert consensus. The system will first establish a set of high-quality liver health/disease image database based on static CT (slice thickness 0.165mm, 2048×2048 scanning/reconstruction matrix, multi-energy spectrum), providing high-quality data source for clinical application development; Then, use artificial intelligence technology to optimize the output high-quality data, data mining and learning, and carry out targeted analysis from the aspects of liver cirrhosis grading, liver reserve function and MVI evaluation; Finally, on the basis of evidence-based medicine and expert consensus, intelligently fuse the multimodal biomedical information to form a set of auxiliary decision-making system for the perioperative period of hepatectomy for liver cancer, which provides a new method for further standardizing the diagnosis and treatment behavior of liver cancer and improving the surgical treatment effect of liver cancer.

Inclusion Criteria:

1. Patients who are clinically diagnosed with primary liver cancer and other space-occupying liver lesions preoperatively and are planned for surgical resection, or those with underlying liver diseases and cirrhosis;
2. Aged 18-75 years;
3. Willing to participate in this study and have signed the informed consent.

Exclusion Criteria:

1. Planned or unplanned pregnancy and pregnant women;
2. Glomerular filtration rate (GFR) ≤60 ml/min;
3. History of contrast media allergy.

Patients who are clinically diagnosed with primary liver cancer and other space-occupying liver lesions preoperatively and are planned for surgical resection, or those with underlying liver diseases and cirrhosis

Individual participant data will not to be shared to protect patient privacy.