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ORIGINAL RESEARCH article
Front. Med.
Sec. Nuclear Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1534988
FRID-PI: A Machine Learning Model for Diagnosing Fracture-Associated Infections Based on 18 F-FDG PET/CT and Inflammatory Markers
Provisionally accepted
mei yang 
QUANHUI TAN 
Tingting Li 
Jie Chen Weiwei Hu Yi Zhang 
Xiaohua Chen 
Jiangfeng Wang 
Chentian Shen 
Zhenghao Tang *- Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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 18 F-fluorodeoxyglucose positron emission tomography /computed tomography ( 18 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 (SUVmax) from 18 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 SUVmax 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 18 F-fluorodeoxyglucose positron emission tomography /computed tomography ( 18 F-FDG PET/CT) and laboratory biomarkers for fracture-related infection (FRI).
Keywords: 18F-FDG PET/CT, Laboratory biomarkers, Fracture-related infection, nomogram, Model
Received: 04 Dec 2024; Accepted: 05 Mar 2025.
Copyright: © 2025 yang, TAN, Li, Chen, Hu, Zhang, Chen, Wang, Shen and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhenghao Tang, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.