Background and aims

Highly accurate noninvasive methods for predicting gastroesophageal varices needing treatment (VNT) are desired. Radiomics is a newly emerging technology of image analysis. This study aims to develop and validate a novel noninvasive method based on radiomics for predicting VNT in cirrhosis.

Methods

In this retrospective–prospective study, a total of 245 cirrhotic patients were divided as the training set, internal validation set and external validation set. Radiomics features were extracted from portal-phase computed tomography (CT) images of each patient. A radiomics signature (Rad score) was constructed with the least absolute shrinkage and selection operator algorithm and tenfold cross-validation in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model.

Results

The Rad score, consisting of 14 features from the gastroesophageal region and 5 from the splenic hilum region, was effective for VNT classification. The diagnostic performance was further improved by combining the Rad score with platelet counts, achieving an AUC of 0.987 (95% CI 0.969–1.00), 0.973 (95% CI 0.939–1.00) and 0.947 (95% CI 0.876–1.00) in the training set, internal validation set and external validation set, respectively. In efficacy and safety assessment, the radiomics nomogram could spare more than 40% of endoscopic examinations with a low risk of missing VNT (< 5%), and no more than 8.3% of unnecessary endoscopic examinations still be performed.

Conclusions

In this study, we developed and validated a novel, diagnostic radiomics-based nomogram which is a reliable and noninvasive method to predict VNT in cirrhotic patients.

Clinical trials registration

NCT04210297.

中文翻译：

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用于预测肝硬化患者需要治疗的胃食管静脉曲张的新型放射组学-血小板列线图

背景和目标

需要高度准确的非侵入性方法来预测需要治疗的胃食管静脉曲张 (VNT)。放射组学是一种新兴的图像分析技术。本研究旨在开发和验证一种基于放射组学的新型无创方法，用于预测肝硬化中的 VNT。

方法

在这项回顾性-前瞻性研究中，总共 245 名肝硬化患者被分为训练集、内部验证集和外部验证集。从每位患者的门静脉期计算机断层扫描 (CT) 图像中提取放射组学特征。使用最小绝对收缩和选择算子算法以及训练集中的十倍交叉验证构建放射组学特征（Rad 分数）。结合独立危险因素，利用多元逻辑回归模型建立放射组学列线图。

结果

Rad 评分由来自胃食管区域的 14 个特征和来自脾门区域的 5 个特征组成，对 VNT 分类是有效的。通过将 Rad 评分与血小板计数相结合，进一步提高了诊断性能，AUC 为 0.987（95% CI 0.969-1.00）、0.973（95% CI 0.939-1.00）和 0.947（95% CI 0.876-1.00）分别是训练集、内部验证集和外部验证集。在有效性和安全性评估中，影像组学列线图可以节省超过 40% 的内镜检查，漏诊 VNT 的风险较低（< 5%），仍然进行不超过 8.3% 的不必要的内镜检查。

结论

在这项研究中，我们开发并验证了一种新的、基于诊断放射组学的列线图，这是一种可靠且无创的方法来预测肝硬化患者的 VNT。

临床试验注册

NCT04210297。