Yang Mei, Tan Quanhui, Li Tingting, Chen Jie, Hu Weiwei, Zhang Yi, Chen Xiaohua, Wang Jiangfeng, Shen Chentian, Tang Zhenghao
Department of Infectious Diseases, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Med (Lausanne). 2025 Mar 26;12:1534988. doi: 10.3389/fmed.2025.1534988. eCollection 2025.
PURPOSE: The diagnosis of fracture-related infection (FRI) especially patients presenting without clinical confirmatory criteria in clinical settings poses challenges with potentially serious consequences if misdiagnosed. This study aimed to construct and evaluate a novel diagnostic nomogram based on F-fluorodeoxyglucose positron emission tomography /computed tomography (F-FDG PET/CT) and laboratory biomarkers for FRI by machine learning. METHODS: A total of 552 eligible patients recruited from a single institution between January 2021 and December 2022 were randomly divided into a training (60%) and a validation (40%) cohort. In the training cohort, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model analysis and multivariate Cox regression analysis were utilized to identify predictive factors for FRI. The performance of the model was assessed using the area under the Receiver Operating Characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis in both training and validation cohorts. RESULTS: A nomogram model (named FRID-PE) based on the maximum standardized uptake value (SUV) from F-FDG PET/CT imaging, Systemic Immune-Inflammation Index (SII), Interleukin - 6 and erythrocyte sedimentation rate (ESR) were generated, yielding an AUC of 0.823 [95% confidence interval (CI), 0.778-0.868] in the training test and 0.811 (95% CI, 0.753-0.869) in the validation cohort for the diagnosis of FRI. Furthermore, the calibration curves and decision curve analysis proved the potential clinical utility of this model. An online webserver was built based on the proposed nomogram for convenient clinical use. CONCLUSION: This study introduces a novel model (FRID - PI) based on SUV and inflammatory markers, such as SII, IL - 6, and ESR, for diagnosing FRI. Our model, which exhibits good diagnostic performance, holds promise for future clinical applications. CLINICAL RELEVANCE STATEMENT: The study aims to construct and evaluate a novel diagnostic model based on F-fluorodeoxyglucose positron emission tomography /computed tomography (F-FDG PET/CT) and laboratory biomarkers for fracture-related infection (FRI).
目的：骨折相关感染（FRI）的诊断，尤其是在临床环境中无临床确诊标准的患者，若误诊可能会导致严重后果，因此颇具挑战性。本研究旨在通过机器学习构建并评估一种基于F-氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（F-FDG PET/CT）和实验室生物标志物的新型FRI诊断列线图。 方法：2021年1月至2022年12月期间从单一机构招募的552例符合条件的患者被随机分为训练组（60%）和验证组（40%）。在训练组中，使用最小绝对收缩和选择算子（LASSO）回归模型分析和多变量Cox回归分析来确定FRI的预测因素。在训练组和验证组中，使用受试者工作特征（ROC）曲线下面积（AUC）、校准曲线和决策曲线分析来评估模型的性能。 结果：生成了一个基于F-FDG PET/CT成像的最大标准化摄取值（SUV）、全身免疫炎症指数（SII）、白细胞介素-6和红细胞沉降率（ESR）的列线图模型（命名为FRID-PE），在训练测试中诊断FRI的AUC为0.823 [95%置信区间（CI），0.778-0.868]，在验证组中为0.811（95%CI，0.753-0.869）。此外，校准曲线和决策曲线分析证明了该模型的潜在临床实用性。基于所提出的列线图构建了一个在线网络服务器，以便于临床使用。 结论：本研究引入了一种基于SUV和炎症标志物（如SII、IL-6和ESR）的新型模型（FRID-PI）来诊断FRI。我们的模型具有良好的诊断性能，有望在未来的临床应用中发挥作用。 临床相关性声明：本研究旨在构建并评估一种基于F-氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（F-FDG PET/CT）和实验室生物标志物的新型骨折相关感染（FRI）诊断模型。
Front Med (Lausanne). 2025-3-26
Eur J Nucl Med Mol Imaging. 2018-12-7
Zotero 插件
