基于端到端CT放射组学的CKD患者肾间质纤维化分级预测

IF 3.8 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Academic Radiology Pub Date : 2025-01-16 DOI:10.1016/j.acra.2024.12.050
Yue Ren, Fei Yang, Weiwei Li, Yongsheng Zhang, Shuchao Kang, Feng Cui
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引用次数: 0

摘要

理由和目的:慢性肾病(CKD)患者肾纤维化的无创评估仍然是一个临床挑战。该研究旨在整合放射组学和临床因素,开发端到端预测CKD患者间质纤维化(IF)的管道。材料与方法:本回顾性研究纳入80例CKD患者,其中训练组53例,测试组27例。所有患者均行肾脏计算机断层扫描(CT)和活检。患者根据肾IF的等级分为两组:轻度-中度和重度。从CT图像上自动分割的右肾区域提取放射组学特征,采用单因素分析和多重最小绝对收缩选择算子(LASSO)构建放射组学特征。随后,开发了逻辑回归模型来创建放射组学模型和组合模型。通过判别、校准和决策曲线分析(DCA)对两种模型的预测性能进行评估,并对表现优异的模型构建nomogram。结果:联合模型显著优于放射组学模型,在训练集中交叉验证的AUC为0.935±0.041,而放射组学模型的AUC为0.804±0.024。在测试集中,联合模型优于放射组学模型,AUC为0.918 [95% CI 0.799-1] vs. 0.764 [95% CI 0.549-0.979], p=0.031 (DeLong检验,统计量:-2.152)。校正曲线和DCA表明,联合模型具有良好的校正效果和较好的临床净效益。结论:这种端到端工作流可以作为一种潜在的无创工具来预测CKD患者肾脏IF等级(轻中度vs重度)。
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End-to-End CT Radiomics-Based Pipeline for Predicting Renal Interstitial Fibrosis Grade in CKD Patients.

Rationale and objectives: Non-invasive assessment of renal fibrosis in patients with chronic kidney disease (CKD) remains a clinical challenge. This study aims to integrate radiomics and clinical factors to develop an end-to-end pipeline for predicting interstitial fibrosis (IF) in CKD patients.

Materials and methods: This retrospective study included 80 patients with CKD, with 53 patients in training set and 27 patients in test set. All patients underwent renal computed tomography (CT) scans and biopsy. Patients were classified into two groups based on their renal IF grade: mild-moderate and severe. Radiomics features were extracted from the automatically segmented right renal region on CT images, and univariate analysis along with multiple Least Absolute Shrinkage and Selection Operator (LASSO) was employed to construct the radiomics signature. Subsequently, logistic regression models were developed to create the radiomics model and the combined model. The predictive performance of both models was evaluated through discrimination, calibration, and decision curve analysis (DCA), and a nomogram was constructed for the model demonstrating superior performance.

Results: The combined model significantly outperformed the radiomics model, achieving a cross-validated AUC of 0.935±0.041 in the training set, compared to 0.804±0.024 for the radiomics model. In the test set, the combined model outperformed the radiomics model, with an AUC of 0.918 [95% CI 0.799-1] vs. 0.764 [95% CI 0.549-0.979], p=0.031 (DeLong test, Statistic: -2.152). Calibration curves and DCA indicated that the combined model demonstrated good calibration and better clinical net benefit.

Conclusion: This end-to-end workflow could serve as a potential non-invasive tool to predict renal IF grade (mild-moderate vs. severe) in CKD patients.

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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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