Department of Radiology, The Affiliated Hospital of Southwest Medical University, NO. 25, Taiping Road, Jiangyang District, Luzhou City, Sichuan, China.
Pharmaceutical Diagnostics, GE Healthcare, Beijing, China.
BMC Med Imaging. 2024 Oct 29;24(1):292. doi: 10.1186/s12880-024-01461-8.
Rebleeding after endoscopic treatment for esophagogastric varices (EGVs) in cirrhotic patients remains a significant clinical challenge, with high mortality rates and limited predictive tools. Current methods, relying on clinical indicators, often lack precision and fail to provide personalized risk assessments. This study aims to develop and validate a novel, non-invasive prediction model based on CT radiomics to predict rebleeding risk within one year of treatment, integrating radiomic features from key organs and clinical data.
123 patients were enrolled and divided into rebleeding (n = 44) and non-bleeding group (n = 79) within 1 year after endoscopic treatment of EGVs. The liver, spleen, and the lower part of the esophagus were segmented and the extracted radiomics features were selected to construct liver/spleen/esophagus radiomics signatures based on logistic regression. Clinic-radiomics combined models and multi-organ combined radiomics models were constructed based on independent model scores using logistic regression. The model performance was evaluated by ROC analysis, calibration and decision curves. The continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices were analyzed.
The clinical-liver combined model had the highest AUC of 0.931 (95% CI: 0.887-0.974), which was followed by the liver-based model with AUC of 0.891 (95% CI: 0.835-0.74). The decision curves also showed that the clinical-liver combined model afforded a greater net benefit compared to other models within the threshold probability of 0.45 to 0.80. Significant improvements in discrimination (IDI, P < 0.05) and reclassification (NRI, P < 0.05) were obtained for clinical-liver combined model compared with the independent ones.
The independent and combined liver-based CT radiomics models performed well in predicting rebleeding within 1 year after endoscopic treatment of EGVs.
肝硬化患者内镜治疗食管胃静脉曲张（EGV）后的再出血仍然是一个重大的临床挑战，死亡率高，且预测工具有限。目前的方法依赖于临床指标，往往缺乏准确性，无法提供个性化的风险评估。本研究旨在开发和验证一种新的、基于 CT 放射组学的非侵入性预测模型，该模型基于关键器官的放射组学特征和临床数据，预测治疗后 1 年内的再出血风险。
纳入 123 例患者，根据内镜治疗 EGV 后 1 年内是否再出血分为再出血组（n=44）和未出血组（n=79）。对肝脏、脾脏和食管下段进行分割，提取放射组学特征，基于逻辑回归构建肝脏/脾脏/食管放射组学特征。基于独立模型评分，利用逻辑回归构建临床-放射组学联合模型和多器官联合放射组学模型。通过 ROC 分析、校准和决策曲线评估模型性能。分析连续净重新分类改善（NRI）和综合区分改善（IDI）指数。
临床-肝脏联合模型的 AUC 最高为 0.931（95%CI：0.887-0.974），其次是肝脏模型，AUC 为 0.891（95%CI：0.835-0.74）。决策曲线也表明，在 0.45 至 0.80 的阈值概率范围内，临床-肝脏联合模型的净获益大于其他模型。与独立模型相比，临床-肝脏联合模型在区分度（IDI，P<0.05）和重新分类（NRI，P<0.05）方面有显著改善。
独立和联合的基于肝脏的 CT 放射组学模型在预测内镜治疗 EGV 后 1 年内再出血方面表现良好。
