A Generative Model-based System for Predicting Survival and Guiding Treatment Decisions in Patients With Unresectable Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization in Combination With Immunotherapy and Targeted Therapy
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 550
- 试验地点
- 1
- 主要终点
- Overall survival
研究概览
简要总结
The entry point of this study is the proposition of "generative longitudinal prediction," which utilizes only pre-treatment imaging to create high-fidelity predictions of post-treatment imaging. This approach effectively overcomes the clinical challenge of acquiring genuine longitudinal follow-up data. This paradigm shift not only tackles the scarcity of longitudinal data but also introduces an innovative method for treatment simulation using digital twins. Clinicians can intuitively assess the potential efficacy of various treatment plans before intervention through virtually generated multi-timepoint imaging, providing a visual foundation for personalized treatment decisions. This research merges generative AI with dynamic risk models to achieve: 1) a transition from static assessment to dynamic simulation; 2) earlier survival predictions; and 3) personalized optimization of treatment plans. By eliminating dependence on longitudinal data, we aim to deliver more precise and individualized treatment decision support for advanced liver cancer patients, ultimately enhancing survival outcomes and quality of life.
详细描述
The entry point of this study is the proposition of "generative longitudinal prediction," which utilizes only pre-treatment imaging to create high-fidelity predictions of post-treatment imaging. This approach effectively overcomes the clinical challenge of acquiring genuine longitudinal follow-up data. This paradigm shift not only tackles the scarcity of longitudinal data but also introduces an innovative method for treatment simulation using digital twins. Clinicians can intuitively assess the potential efficacy of various treatment plans before intervention through virtually generated multi-timepoint imaging, providing a visual foundation for personalized treatment decisions. This research merges generative AI with dynamic risk models to achieve: 1) a transition from static assessment to dynamic simulation; 2) earlier survival predictions; and 3) personalized optimization of treatment plans. By eliminating dependence on longitudinal data, we aim to deliver more precise and individualized treatment decision support for advanced liver cancer patients, ultimately enhancing survival outcomes and quality of life. The model was developed in a retrospective cohort, with validation and testing conducted in multiple retrospective and prospective cohorts, respectively.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Diagnosed as unresectable hepatocellular carcinoma (HCC) by histopathology and/or clinical diagnosis (typical imaging features, clinical manifestations, laboratory tests, etc.) (Reference: Guidelines for Diagnosis and Treatment of Primary Liver Cancer (2024 Edition));
- •Patients with unresectable HCC receiving TACE combined with targeted immunotherapy;
- •Liver function classified as Child-Pugh A or B;
- •Aged 18 or above, regardless of gender;
- •Expected survival time ≥3 months;
- •ECOG PS score ≤2;
- •Meeting the following laboratory parameters: a) Hematologic function: Absolute neutrophil count ≥1.0×10⁹/L; Platelet count ≥50×10⁹/L; Hemoglobin ≥90 g/L; International normalized ratio (INR) <1.7 or prothrombin time prolongation ≤4 seconds; b) Liver function: ALT/AST ≤5× upper limit of normal (ULN); Total bilirubin ≤210 μmol/L [≤2.38 mg/dL]; Albumin ≥28 g/L; c) Renal function: Serum creatinine ≤1.5× ULN.
排除标准
- •Concurrent presence of other malignant tumors besides HCC;
- •Moderate to severe ascites (ascites scoring 3 points on the Child-Pugh scale); - - Receipt of other first-line, second-line, or third-line systemic therapies (including any regimen of systemic treatment) or any local therapies (including transcatheter interventional therapy, ablation therapy, internal/external radiotherapy, etc.), as well as surgical resection or herbal medicine within 4 weeks prior to TACE combined with targeted immunotherapy;
- •Incomplete data, such as missing baseline laboratory test results, unavailable or poor-quality imaging data, or lack of prognostic information;
- •Severe liver dysfunction: e.g., decompensated cirrhosis or other liver diseases significantly affecting bilirubin levels;
- •Severe comorbidities: e.g., refractory hypertension (blood pressure remaining above 150/100 mm Hg despite optimal medication), persistent arrhythmia (CTCAE grade 2 or higher), atrial fibrillation of any degree, prolonged QTc interval (>450 ms in males or >470 ms in females), renal insufficiency, etc.;
- •Co-infection with human immunodeficiency virus (HIV) or acquired immunodeficiency syndrome (AIDS);
- •Pregnant or breastfeeding women;
- •Acute or chronic psychiatric disorders (including those affecting participant enrollment, treatment intervention, or follow-up).
结局指标
主要结局
Overall survival
时间窗: Through study completion, an average of 20 months
defined as the time from the initial of combined therapy to death from any cause
次要结局
未报告次要终点
研究者
Gao-jun Teng
Professor of Radiology Zhongda Hospital, Southeast University
Zhongda Hospital