Development and internal validation of prediction model for rebleeding within one year after endoscopic treatment of cirrhotic varices: consideration from organ-based CT radiomics signature.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-10-29 DOI:10.1186/s12880-024-01461-8
Lulu Xu, Jing Zhang, Siyun Liu, Guoyun He, Jian Shu
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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: The independent and combined liver-based CT radiomics models performed well in predicting rebleeding within 1 year after endoscopic treatment of EGVs.

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肝硬化静脉曲张内镜治疗后一年内再出血预测模型的开发和内部验证:基于器官的 CT 放射组学特征的考虑。
背景:肝硬化患者经内镜治疗食管胃静脉曲张(EGVs)后再出血仍是一项重大的临床挑战,死亡率高且预测工具有限。目前的方法依赖于临床指标,往往缺乏精确性,无法提供个性化的风险评估。本研究旨在开发和验证一种基于CT放射组学的新型无创预测模型,该模型综合了关键器官的放射组学特征和临床数据,可预测治疗后一年内的再出血风险。对肝脏、脾脏和食管下段进行分割,并选择提取的放射组学特征,在逻辑回归的基础上构建肝脏/脾脏/食管放射组学特征。根据独立模型得分,利用逻辑回归法构建临床-放射组学组合模型和多器官组合放射组学模型。模型性能通过 ROC 分析、校准和决策曲线进行评估。分析了连续的净再分类改进指数(NRI)和综合辨别改进指数(IDI):临床-肝脏联合模型的AUC最高,为0.931(95% CI:0.887-0.974),其次是基于肝脏的模型,AUC为0.891(95% CI:0.835-0.74)。决策曲线还显示,在 0.45 至 0.80 的阈值概率范围内,与其他模型相比,临床-肝脏联合模型的净获益更大。分辨能力显著提高(IDI,P 结论:临床-肝脏联合模型的分辨能力显著提高:基于肝脏的独立和组合 CT 放射组学模型在预测 EGV 内镜治疗后 1 年内再出血方面表现良好。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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