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MRI-based radiomics explainable model for predicting recurrence of limb chronic osteomyelitis in limb bones treated
Yi Cao1,2, Xuesen Zhao1, Ruofei Wang1
1Department of Biochemistry and Molecular Biology, Key Laboratory of Neural and Vascular Biology, Ministry of Education, and Hebei Key Laboratory of Cardiovascular Homeostasis and Aging, Hebei Medical University, Hebei, China.
Aims:
This study aimed to develop a machine learning model for predicting chronic osteomyelitis recurrence (COR) following the Masquelet technique (MT). The model integrated MRI-based radiomics with clinical characteristics to guide the definitive treatment of bone defects in limb chronic osteomyelitis (LCO).
Methods:
We retrospectively analyzed data from patients with chronic osteomyelitis who underwent debridement and the Masquelet technique (MT) as definitive treatment at two medical centres between 2015 and 2023. The dataset included demographics, MRI scans, clinical characteristics, and infection recurrence, with a two-year follow-up period. Radiomics features were extracted from MRI-images using PyRadiomics. Clinical features were identified by logistic regression analyses. A COR predictive model was developed using machine learning algorithms and SHapley Additive exPlanations (SHAP). Additionally, a web-based application was also constructed to support the model.
Results:
Among 279 patients (mean age, 43.19 years (SD 11.87); 195 men and 84 women), 59 (21.15%) patients had COR, involving the hip, lower limb, foot, and upper limb. The radiomic feature "Radscore" was constructed by eight image features. Four significant clinical features (age, surgery times, duration of infection, ESR) were selected, and combined with radiomics feature to construct a predictive model using machine learning algorithms. This integrated model exhibited a superior performance (area under the curve (AUC) = 0.901, 0.952, and 0.910 in training, validation, and external validation cohort, respectively) than only clinical (AUC = 0.862) or radiomics (AUC = 0.684) model. Lastly, a web-based application was developed and validated to predict COR risk in patients with MT treatment.
Conclusion:
A web-based application integrating radiomics and clinical factors was developed to predict risk of COR in patients after MT treatment, allowing for the implementation of preventive interventions and targeted management.
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