Primary liver cancer is one of the most common malignant tumors in the world, and more than 90% of primary liver cancers are pathologically characterized as hepatocellular carcinoma (HCC). The intermediate stage (BCLC-B) HCC is highly heterogeneous, and there is no consensus on the treatment of this stage of the tumor in Western and Eastern countries. New tools are urgently needed to guide the choice of treatment options for patients with this stage of the tumor in order to reduce the risk of postoperative recurrence and the overall survival rate.

Primary liver cancer is one of the most common malignant tumors in the world, and more than 90% of primary liver cancers are pathologically characterized as hepatocellular carcinoma (HCC). Intermediate stage (BCLC-B) HCC is heterogeneous, and there is no uniform consensus on the treatment of this stage of the tumor in Western and Chinese countries, while the European guidelines recommend liver transplantation, transarterial chemoembolization (TACE), and systemic medication as the first line of treatment. In Eastern countries, such as China, BCLC-B is further categorized into stages IIa and IIb, and surgical resection is recommended as the first-line treatment option for stage IIa, while surgical resection can also be considered for stage IIb. Retrospective studies have found that surgical resection has an overall better prognosis than non-surgical treatment. However, the rate of postoperative recurrence is higher than that of early HCC. To address this issue, new tools are urgently needed to guide the selection of appropriate treatment regimens to reduce the risk of postoperative recurrence and overall survival.

Our multidisciplinary team used deep learning technology to construct an artificial intelligence prediction model of neoadjuvant therapy benefit based on pre-treatment genetic testing data, digital pathology slides and imaging data (enhanced MRI) of 536 intermediate-stage HCC patients treated with HAIC in combination with lenvatinib and PD-1 monoclonal antibody in six centers, and external center data validated the model's good ability to identify the beneficiary population of the combination regimen ( AUC 0.89, Accuracy 0.86). The aim of this study is to study the effectiveness and safety of New-adj-Net in improving the progression of intermediate-stage HCC patients during neoadjuvant therapy and postoperative recurrence by observing the benefit of the combined neoadjuvant regimen in patients who are potentially benefited from neoadjuvant therapy and direct surgery from the perspective of precision therapy.

Inclusion Criteria:

1. Aged 18-75.
2. No previous local or systemic treatment for hepatocellular carcinoma.
3. Child-Pugh liver function score ≤ 7.
4. ECOG PS 0-1.
5. No serious organic diseases of the heart, lungs, brain, kidneys, etc.
6. Enhanced MRI determines that the tumor stage is intermediate (BCLC stage B) and is safe for radical hepatectomy.
7. Pathologic type of hepatocellular carcinoma confirmed by puncture biopsy.
8. Multimodal Deep Learning Model Screening Based on Pathology, Imaging, and Genetic Data Suggests Benefit from HAIC in Combination with Lenvatinib and PD-1 inhibitors.

Exclusion Criteria:

1. Pregnant and lactating women.
2. Tumor distribution in two liver lobes, diffuse growth, or other reasons why radical R0 resection is not possible.
3. Suffering from a condition that interferes with the absorption, distribution, metabolism, or clearance of the study drug (e.g., severe vomiting, chronic diarrhea, intestinal obstruction, impaired absorption, etc.).
4. A history of gastrointestinal bleeding within the previous 4 weeks or a definite predisposition to gastrointestinal bleeding (e.g., known locally active ulcer lesions, fecal occult blood ++ or more, or gastroscopy if persistent fecal occult blood +) that has not been targeted, or other conditions that may have caused gastrointestinal bleeding (e.g., severe fundoplication/esophageal varices), as determined by the investigator.
5. Active infection.
6. Other significant clinical and laboratory abnormalities that affect the safety evaluation.
7. Inability to follow the study protocol for treatment or follow up as scheduled.