FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory markers
PurposeThe 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...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1534988/full |
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| Summary: | PurposeThe 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.MethodsA 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.ResultsA 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.ConclusionThis 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 statementThe 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). |
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| ISSN: | 2296-858X |