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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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1534988/full |
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| author | Mei Yang Quanhui Tan Tingting Li Jie Chen Weiwei Hu Yi Zhang Xiaohua Chen Jiangfeng Wang Chentian Shen Zhenghao Tang |
| author_facet | Mei Yang Quanhui Tan Tingting Li Jie Chen Weiwei Hu Yi Zhang Xiaohua Chen Jiangfeng Wang Chentian Shen Zhenghao Tang |
| author_sort | Mei Yang |
| collection | DOAJ |
| description | 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). |
| format | Article |
| id | doaj-art-4c540cb43bdf4131998abf56dc858878 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-4c540cb43bdf4131998abf56dc8588782025-08-20T03:40:54ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-03-011210.3389/fmed.2025.15349881534988FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory markersMei Yang0Quanhui Tan1Tingting Li2Jie Chen3Weiwei Hu4Yi Zhang5Xiaohua Chen6Jiangfeng Wang7Chentian Shen8Zhenghao Tang9Department of Infectious Diseases, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Infectious Diseases, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Infectious Diseases, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Infectious Diseases, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Infectious Diseases, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Infectious Diseases, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Infectious Diseases, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Infectious Diseases, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaPurposeThe 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).https://www.frontiersin.org/articles/10.3389/fmed.2025.1534988/full18F-FDG PET/CTlaboratory biomarkersfracture-related infectionnomogrammodel |
| spellingShingle | Mei Yang Quanhui Tan Tingting Li Jie Chen Weiwei Hu Yi Zhang Xiaohua Chen Jiangfeng Wang Chentian Shen Zhenghao Tang FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory markers Frontiers in Medicine 18F-FDG PET/CT laboratory biomarkers fracture-related infection nomogram model |
| title | FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory markers |
| title_full | FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory markers |
| title_fullStr | FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory markers |
| title_full_unstemmed | FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory markers |
| title_short | FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory markers |
| title_sort | frid pi a machine learning model for diagnosing fracture related infections based on 18f fdg pet ct and inflammatory markers |
| topic | 18F-FDG PET/CT laboratory biomarkers fracture-related infection nomogram model |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1534988/full |
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