From Imaging to Intervention: A Multicenter-Validated Radiomics Pipeline for Guiding Femoral Neck Fracture Surgical Management.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China (L.M., K.L., Z.M., H.X., M.L., D.D., H.Z.).
- School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, Jilin Province, China (Y.L.).
- Department of Radiology, The Eighth Affiliated Hospital of Sun Yat-sen University (Shenzhen Futian), Shenzhen 518000, Guangdong Province, China (Y.X.).
- Department of Orthopaedics, The Chinese PLA General Hospital, Beijing 100853, China (H.L.).
- Department of Radiology, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China (L.M., K.L., Z.M., H.X., M.L., D.D., H.Z.). Electronic address: [email protected].
Abstract
To develop and evaluate a femoral neck fracture (FNF) pipeline model for diagnosing fracture stability and aiding surgical decision-making. Patients with confirmed FNFs were enrolled in the study. An automatic segmentation algorithm was employed to initially delineate fracture-displaced regions revealed using CT images, with subsequent manual refinement. A logistic-regression model was first trained on selected radiomic features to generate a Rad-score for fracture-stability classification. The Rad_score was then fed into a downstream model to guide surgical decision-making. The internal and external validation with multi-center data were used to assess the generalizability of the pipeline model. The internal dataset for fracture stability and surgical decision-making included 624 and 410 patients, respectively. The corresponding external test sets, included 364 and 186 patients enrolled from 32 centers. The radiomics model for FNF stability exhibited robust performance, achieving an area under the curve (AUC) of 0.905 (95% confidence interval [CI]: 0.853-0.944) and 0.821 (95% CI: 0.778-0.859) for the internal and external test sets, respectively. The AUCs for the surgical decision-making models were 0.881 (95% CI: 0.810-0.932) and 0.820 (95% CI: 0.757-0.873) for the internal and external test sets, respectively. The radiomics pipeline model exhibited robust performance in classifying fracture stability and aiding surgical decision-making in the test sets across 33 centers. Our model incorporates explainable artificial intelligence in fracture quantification analysis, supporting doctors in making objective clinical decisions.