NCT06311916

Efficacy and Safety of Neoadjuvant Therapy in Patients With Resectable HCC Screened by a Multimodal Deep Learning Model.

Efficacy and Safety of Neoadjuvant HAIC Combined With Immunotherapy and Targeted Therapy in Patients With Resectable Intermediate-stage HCC Screened by a Multimodal Deep Learning Model: a Multicenter Randomized Controlled Trial.

• Status: Not Yet Recruiting
• Phase: Phase 4
• Study type: Interventional
• Enrollment: 312 (estimated)
• Sponsor: Chen Xiaoping · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

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.

Conditions

• HCC

Interventions

Type	Name	Description
DRUG	HAIC + Tirelizumab +lenvatinib +liver resection	Patients in the neoadjuvant group received two cycles of neoadjuvant hepatic arterial infusion chemotherapy (HAIC, adoption of the FOFOLX6 program, Folinic acid+5-fluorouracil+Oxaliplatin, 21 days between second HAIC treatments with a window of ±3 days) + Tirelizumab (First treatment with Tirelizumab was started 0-1 days after HAIC, 200 mg IV, followed by a second treatment 21 days later)+ lenvatinib (Oral 8 mg or 12mg once a day depending body weight). Assessment of tumor status and surgical safety after receiving neoadjuvant therapy, and eligible patients then underwent surgical resection.
PROCEDURE	liver resection	Direct liver resection or laparoscopic liver resection depending on tumor status.

Timeline

• Start date: 2024-05-01
• Primary completion: 2027-12-31
• Completion: 2028-12-31
• First posted: 2024-03-15
• Last updated: 2024-03-15