ArticleBone & joint research2026
MRI-based radiomics explainable model for predicting recurrence of limb chronic osteomyelitis in limb bones treated by Masquelet technique.
Article in Bone & joint research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.