Neoadjuvant Therapy in Patients With Resectable HCC Screened by a Multimodal Deep Learning Model

HCC Clinical Trial

Official title: Efficacy and Safety of Neoadjuvant HAIC Combined With Tislelizumab and Lenvatinib in Patients With Resectable HCC Screened by a Multimodal Deep Learning Model: a Multicenter Randomized Controlled Trial.

Status Not yet recruiting

Clinical Trial Summary

Primary liver cancer is one of the most common malignant tumors in the world, and about 80%~90% of primary liver cancers are pathologically characterized as hepatocellular carcinoma (HCC). Radical surgery is the main method for patients with HCC to obtain long-term survival. However, there is no consensus on surgical treatment for patients with BCLC-stage B or C HCC. New tools are urgently needed to guide the choice of treatment options.

Clinical Trial Description

Primary liver cancer is one of the most common malignant tumors in the world, and about 80%~90% of primary liver cancers are pathologically characterized as hepatocellular carcinoma (HCC). Radical surgery is the main method for patients with HCC to obtain long-term survival. However, there is no consensus on surgical treatment for patients with BCLC-stage B or C HCC. Transarterial chemoembolization and systemic medication were the first line of treatment for this patients. 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. Moreover, many studies have found that patients with BCLC-C stage tumors can also benefit from surgery and the overall prognosis were better 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 improve overall survival.

Our multidisciplinary team used deep learning technology to construct an artificial intelligence prediction model of neoadjuvant therapy (Neoadj-Net) 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). This study is to explore the effectiveness and safety of Neoadj-Net in reducing 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. ;

Related Conditions & MeSH terms

• HCC

• NCT number: NCT06420440
• Study type: Interventional
• Source: Tongji Hospital
• Contact: WanGuang Zhang
• Phone: 13886195965
• Email: wgzhang@tjh.tjmu.edu.cn
• Status: Not yet recruiting
• Phase: Phase 2
• Start date: June 1, 2024
• Completion date: May 31, 2027