Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China.
Department of Radiology, The People's Hospital of Chongqing Liang Jiang New Area, Chongqing 401121, China.
World J Gastroenterol. 2024 Sep 28;30(36):4044-4056. doi: 10.3748/wjg.v30.i36.4044.
Radiomics has been used in the diagnosis of cirrhosis and prediction of its associated complications. However, most current studies predict the risk of esophageal variceal bleeding (EVB) based on image features at a single level, which results in incomplete data. Few studies have explored the use of global multi-organ radiomics for non-invasive prediction of EVB secondary to cirrhosis.
To develop a model based on clinical and multi-organ radiomic features to predict the risk of first-instance secondary EVB in patients with cirrhosis.
In this study, 208 patients with cirrhosis were retrospectively evaluated and randomly split into training ( = 145) and validation ( = 63) cohorts. Three areas were chosen as regions of interest for extraction of multi-organ radiomic features: The whole liver, whole spleen, and lower esophagus-gastric fundus region. In the training cohort, radiomic score (Rad-score) was created by screening radiomic features using the inter-observer and intra-observer correlation coefficients and the least absolute shrinkage and selection operator method. Independent clinical risk factors were selected using multivariate logistic regression analyses. The radiomic features and clinical risk variables were combined to create a new radiomics-clinical model (RC model). The established models were validated using the validation cohort.
The RC model yielded the best predictive performance and accurately predicted the EVB risk of patients with cirrhosis. Ascites, portal vein thrombosis, and plasma prothrombin time were identified as independent clinical risk factors. The area under the receiver operating characteristic curve (AUC) values for the RC model, Rad-score (liver + spleen + esophagus), Rad-score (liver), Rad-score (spleen), Rad-score (esophagus), and clinical model in the training cohort were 0.951, 0.930, 0.801, 0.831, 0.864, and 0.727, respectively. The corresponding AUC values in the validation cohort were 0.930, 0.886, 0.763, 0.792, 0.857, and 0.692.
In patients with cirrhosis, combined multi-organ radiomics and clinical model can be used to non-invasively predict the probability of the first secondary EVB.
放射组学已被用于肝硬化的诊断和其相关并发症的预测。然而，目前大多数研究都是基于单一层面的图像特征来预测食管静脉曲张破裂出血（EVB）的风险，这导致数据不完整。很少有研究探讨使用全局多器官放射组学对肝硬化继发 EVB 进行非侵入性预测。
建立基于临床和多器官放射组学特征的模型，预测肝硬化患者首次继发 EVB 的风险。
本研究回顾性评估了 208 例肝硬化患者，并将其随机分为训练集（n=145）和验证集（n=63）。选择三个区域作为提取多器官放射组学特征的感兴趣区：整个肝脏、整个脾脏和食管胃底区域。在训练集中，通过观察者间和观察者内相关系数以及最小绝对收缩和选择算子法筛选放射组学特征，创建放射组学评分（Rad-score）。使用多变量逻辑回归分析选择独立的临床危险因素。将放射组学特征和临床风险变量相结合，创建新的放射组学-临床模型（RC 模型）。使用验证集对建立的模型进行验证。
RC 模型具有最佳的预测性能，能够准确预测肝硬化患者的 EVB 风险。腹水、门静脉血栓形成和血浆凝血酶原时间被确定为独立的临床危险因素。RC 模型、Rad-score（肝+脾+食管）、Rad-score（肝）、Rad-score（脾）、Rad-score（食管）和临床模型在训练集中的受试者工作特征曲线下面积（AUC）值分别为 0.951、0.930、0.801、0.831、0.864 和 0.727，在验证集中的 AUC 值分别为 0.930、0.886、0.763、0.792、0.857 和 0.692。
在肝硬化患者中，联合多器官放射组学和临床模型可用于无创预测首次继发 EVB 的概率。
