Clinical trial NCT06420440: Neoadjuvant Therapy in Patients With Resectable HCC Screened by a Multimodal Deep Learning Model
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.
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: PHASE2
Conditions: HCC
Keywords: Hepatocellular carcinoma
Neoadjuvant tehrapy
recurrence
Deep learning
Lead sponsor: Tongji Hospital
Sponsor class: OTHER
First posted: 2024-05-20
Enrollment: 160
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: TREATMENT

Brief 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, the early recurrence rate of high-risk HCC is very high, which seriously affects the overall therapeutic effect.

Detailed description: The protocol was revised in April 2025 (V1.1). 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, the early recurrence rate of high-risk HCC is very high, which seriously affects the overall therapeutic effect. Although the tumor is in BCLC-A stage, when the tumor diameter is more than 5cm, the effect of surgery is worse than that of single small HCC due to the large resection range, the high risk of surgery and residual disease. In addition, BCLC-stage B and C tumors have a high recurrence rate due to multiple lesions or macrovascular invasion. 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.

The investigators 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.

Eligibility criteria: 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 is technically resectable but at high risk for recurrence(BCLC-A tumor diameter more than or equal to 5cm; BCLC-B; BCLC-C) ; without distant metastasis.
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. 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.).
3. 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.
4. Active infection.
5. Other significant clinical and laboratory abnormalities that affect the safety evaluation.
6. Inability to follow the study protocol for treatment or follow up as scheduled.

Primary outcomes:
measure: Median event-free survival (EFS)
timeFrame: From date of randomization until the date of first documented recurrence or date of death from any cause, whichever came first, assessed up to 60 months.
Secondary outcomes:
measure: Safety Assessment
timeFrame: Baseline up to 12 months
measure: Overall Survival (OS)
timeFrame: From date of randomization until the date of death from any cause, assessed up to 60 months.
Other outcomes:
Locations:
city: Xiangyang
country: China
facility: Huapeng Sun
state: Hubei
city: Jinan
country: China
facility: Enyu Liu
state: Shandong
Study officials:
References: