新的血液签名用于肝细胞癌查
K-M Chueng1, K-N Kwok2, S J-L Lam3
1Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong SAR, China.
ESMO gastrointestinal oncology
|February 6, 2026
概括
常规血液检查显示了肝细胞癌 (HCC) 患者的独特特征,为早期癌症检测提供了新的途径. 这种基于血液的方法显示了人工智能在改善HCC查方面的前景.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 在瘤学瘤学.
- 生物标志物 生物标志物
背景情况:
- 阿尔法-胎蛋白 (AFP) 是用于肝细胞癌 (HCC) 查的标准但有限的生物标志物.
- 常规的血液检查可以提供各种癌症状况的见解.
- 使用常规血液检测开发基于AI的工具可以提高早期HCC检测.
研究的目的:
- 在肝细胞癌 (HCC) 患者中识别出鲜明的血液特征.
- 探索常规血液检测的潜力,以开发人工智能驱动的HCC检测模型.
- 为了区分HCC患者和慢性肝病 (CLD) 控制者,使用血液检测概况.
主要方法:
- 一个回顾性分析来自香港的223,862名患者记录 (2000-2018).
- 包括患有慢性肝病 (CLD),有或没有HCC的患者,不包括无补偿病例.
- 在HCC诊断后一个月内分析常规血液检查,包括全血细胞计,肝功能,功能和凝血概况.
主要成果:
- 与CLD对照组相比,HCC患者表现出显著的血液特征 (P <0.05).
- 关键的差异包括肝功能障碍,全身炎症标志物,出血倾向和缓解症指标.
- 观察到肝酶 (ALT,ALP,AST,白血素) 升高,白蛋白降低,淋巴细胞数量降低,红细胞分布宽度改变,凝血时间延长,肌素/尿素减少.
结论:
- 使用广泛的临床数据确定了用于检测HCC的新型血液特征.
- 这种血液特征的独特光谱特征可以区分HCC和CLD.
- 这些发现支持机器学习模型的潜力,用于基于常规血液检测的HCC早期检测.
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