Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Stat Med. 2022 Jun 15;41(13):2338-2353. doi: 10.1002/sim.9358. Epub 2022 Feb 23.
The early detection of hepatocellular carcinoma (HCC) is critical to improving outcomes since advanced HCC has limited treatment options. Current guidelines recommend HCC ultrasound surveillance every 6 months in high-risk patients however the sensitivity for detecting early stage HCC in clinical practice is poor. Blood-based biomarkers are a promising direction since they are more easily standardized and less resource intensive. Combining of multiple biomarkers is more likely to achieve the sensitivity required for a clinically useful screening algorithm and the longitudinal trajectory of biomarkers contains valuable information that should be utilized. We propose a multivariate parametric empirical Bayes (mPEB) screening approach that defines personalized thresholds for each patient at each screening visit to identify significant deviations that trigger additional testing with more sensitive imaging. The Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) trial provides a valuable source of data to study HCC screening algorithms. We study the performance of the mPEB algorithm applied to serum -fetoprotein, a widely used HCC surveillance biomarker, and des- carboxy prothrombin, an HCC risk biomarker that is FDA approved but not used in practice in the United States. Using cross-validation, we found that the mPEB algorithm demonstrated moderate but improved sensitivity compared to alternative screening approaches. Future research will validate the clinical utility of the approach in larger cohort studies with additional biomarkers.
肝细胞癌（HCC）的早期检测对于改善预后至关重要，因为晚期 HCC 的治疗选择有限。目前的指南建议高危患者每 6 个月进行 HCC 超声监测，然而在临床实践中，检测早期 HCC 的灵敏度较差。基于血液的生物标志物是一个很有前途的方向，因为它们更容易标准化，资源密集度更低。结合多个生物标志物更有可能达到临床有用的筛查算法所需的灵敏度，并且生物标志物的纵向轨迹包含有价值的信息，应该加以利用。我们提出了一种多变量参数经验贝叶斯（mPEB）筛查方法，该方法为每个患者在每次筛查访问时定义个性化阈值，以识别触发额外测试的显著偏差，这些测试采用更敏感的成像技术。丙型肝炎抗病毒长期治疗肝硬化（HALT-C）试验为研究 HCC 筛查算法提供了有价值的数据源。我们研究了 mPEB 算法应用于血清甲胎蛋白（一种广泛用于 HCC 监测的生物标志物）和脱羧基凝血酶原（一种 HCC 风险生物标志物，已获得 FDA 批准，但未在美国实际使用）的性能。通过交叉验证，我们发现与替代筛查方法相比，mPEB 算法的灵敏度有所提高，但仍处于中等水平。未来的研究将在更大的队列研究中，结合其他生物标志物，验证该方法的临床实用性。
