Combining radiomics of X-rays with patient functional rating scales for predicting satisfaction after radial fracture fixation: a multimodal machine learning predictive model

Abstract

BACKGROUND: Patient satisfaction after one year of distal radius fracture fixation is influenced by various aspects such as the surgical approach, the patient's physical functioning, and psychological factors. Hence, a multimodal machine learning prediction model combining traditional rating scales and postoperative X-ray images of patients was developed to predict patient satisfaction one year after surgery for personalized clinical treatment. METHODS: In this study, we reviewed 385 patients who underwent internal fixation with a palmar plate or external fixation bracket fixation in 2018-2020. After one year of postoperative follow-up, 169 patients completed the patient wrist evaluation (PRWE), EuroQol5D (EQ-5D), and forgotten joint score-12 (FJS-12) questionnaires and were subjected to X-ray capture. The region of interest (ROI) of postoperative X-rays was outlined using 3D Slicer, and the training and test sets were divided based on the satisfaction of the patients. Python was used to extract 848 image features, and random forest embedding was used to reduce feature dimensionality. Also, a machine learning model combining the patient's functional rating scale with the downscaled X-ray-related image features was built, followed by hyperparameter debugging using the grid search method during the modeling process. The stability of the Radiomics and Integrated models was first verified using the five-fold cross-validation method, and then receiver operating characteristic curves, calibration curves, and decision curve analysis were used to evaluate the performance of the model on the training and test sets. RESULTS: The feature dimensionality reduction yielded 16 imaging features. The accuracy of the two models was 0.831, 0.784 and 0.966, 0.804 on the training and test sets, respectively. The area under the curve (AUC) values for the Radiomics and Integrated model were 0.937, 0.673 and 0.997, 0.823 for the training and test sets, respectively. The calibration curves and decision curve analysis (DCA) of the Integrated model for the training and test sets had a more accurate prediction probability and clinical significance than the Radiomics model. CONCLUSIONS: A multimodal machine learning predictive model combining imaging and patient functional rating scales demonstrated optimal predictive performance for one-year postoperative satisfaction in patients with radial fractures, providing a basis for personalized postoperative patient management.

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