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Published on: April 18, 2011
FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory
Mei Yang1, Quanhui Tan1, Tingting Li1
1Department of Infectious Diseases, 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 18F-fluorodeoxyglucose positron emission tomography /computed tomography (18F-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 18F-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 18F-fluorodeoxyglucose positron emission tomography /computed tomography (18F-FDG PET/CT) and laboratory biomarkers for fracture-related infection (FRI).
Insights
This study developed a new diagnostic tool for fracture-related infection (FRI) using 18F-FDG PET/CT scans and inflammatory markers. The FRID-PE model accurately identifies FRI, improving patient diagnosis and care.
Area of Science:
- Nuclear Medicine
- Infectious Diseases
- Machine Learning
Background:
- Diagnosing fracture-related infection (FRI) is challenging, especially in patients lacking clear clinical signs.
- Misdiagnosis of FRI can lead to severe complications and adverse patient outcomes.
Purpose of the Study:
- To create and validate a novel diagnostic nomogram for FRI.
- The nomogram integrates 18F-FDG PET/CT imaging and laboratory biomarkers using machine learning.
Main Methods:
- A machine learning approach was used to analyze data from 552 patients.
- Predictive factors for FRI were identified using LASSO and multivariate Cox regression.
- Model performance was evaluated using ROC curves, calibration, and decision curve analysis.
Main Results:
- The FRID-PE nomogram, incorporating SUVmax, SII, IL-6, and ESR, achieved an AUC of 0.823 (training) and 0.811 (validation).
- Calibration and decision curve analyses confirmed the model's clinical utility.
- An online webserver was developed for easy clinical application.
Conclusions:
- A novel diagnostic model (FRID-PI) combining 18F-FDG PET/CT (SUVmax) and inflammatory markers (SII, IL-6, ESR) for FRI diagnosis was developed.
- The model demonstrates strong diagnostic performance and potential for clinical use in managing FRI.
