Noninvasive variceal risk stratification systems have not been validated in patients with hepatocellular carcinoma (HCC), which presents logistical barriers for patients in the setting of systemic HCC therapy. We aimed to develop and validate a noninvasive algorithm for the prediction of varices in patients with unresectable HCC. We performed a retrospective cohort study in 21 centers in the United States including adult patients with unresectable HCC and Child-Pugh A5-B7 cirrhosis diagnosed between 2007 and 2019. We included patients who completed an esophagogastroduodonoscopy (EGD) within 12 months of index imaging but before HCC treatment. We divided the cohort into a 70:30 training set and validation set, with the goal of maximizing negative predictive value (NPV) to avoid EGD in low-risk patients. We included 707 patients (median age, 64.6 years; 80.6% male; 74.0% White). Median time from HCC diagnosis to EGD was 47 (interquartile range, 114) days, with 25.0% of patients having high-risk varices. A model using clinical variables alone achieved an NPV of 86.3% in the validation cohort, whereas a model integrating clinical and imaging variables had an NPV 97.4% in validation. The clinical and imaging model would avoid EGDs in more than half of low-risk patients while misclassifying 7.7% of high-risk patients. A model incorporating clinical and imaging data can accurately predict the absence of high-risk varices in patients with HCC and avoid EGD in many low-risk patients before the initiation of systemic therapy, thus expediting their care and avoiding treatment delays.

中文翻译：

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用于检测不可切除的 HCC 患者高风险静脉曲张的无创模型的开发和验证


无创性静脉曲张风险分层系统尚未在肝细胞癌 (HCC) 患者中得到验证，这给患者进行全身性 HCC 治疗带来了后勤障碍。我们的目的是开发和验证一种非侵入性算法，用于预测不可切除的 HCC 患者的静脉曲张。我们在美国 21 个中心进行了一项回顾性队列研究，其中包括 2007 年至 2019 年间诊断出的不可切除的 HCC 和 Child-Pugh A5-B7 肝硬化的成年患者。我们纳入了在索引成像后 12 个月内完成食管胃十二指肠镜检查 (EGD) 但HCC 治疗前。我们将队列分为 70:30 的训练集和验证集，目标是最大化阴性预测值 (NPV)，以避免低风险患者接受 EGD。我们纳入了 707 名患者（中位年龄 64.6 岁；80.6% 为男性；74.0% 为白人）。从 HCC 诊断到 EGD 的中位时间为 47（四分位距，114）天，25.0% 的患者患有高危静脉曲张。仅使用临床变量的模型在验证队列中的 NPV 为 86.3%，而整合临床和影像变量的模型在验证中的 NPV 为 97.4%。临床和影像模型将避免对一半以上的低风险患者进行 EGD，同时错误分类 7.7% 的高风险患者。结合临床和影像数据的模型可以准确预测 HCC 患者是否存在高风险静脉曲张，并在许多低风险患者开始全身治疗之前避免 EGD，从而加快他们的护理并避免治疗延误。