Predicting craniofacial fibrous dysplasia growth status: an exploratory study of a hybrid radiomics and deep learning model based on computed tomography images.

Objective: This study aimed to develop 3 models based on computed tomography (CT) images of patients with craniofacial fibrous dysplasia (CFD): a radiomics model (Model Rad), a deep learning (DL) model (Model DL), and a hybrid radiomics and DL model (Model Rad+DL), and evaluate the ability of these models to distinguish between adolescents with active lesion progression and adults with stable lesion progression.

Methods: We retrospectively analyzed preoperative CT scans from 148 CFD patients treated at Shanghai Ninth People's Hospital. The images were processed using 3D-Slicer software to segment and extract regions of interest for radiomics and DL analysis. Feature selection was performed using t-tests, mutual information, correlation tests, and the least absolute shrinkage and selection operator algorithm to develop the 3 models. Model accuracy was evaluated using measurements including the area under the curve (AUC) derived from receiver operating characteristic analysis, sensitivity, specificity, and F1 score. Decision curve analysis (DCA) was conducted to evaluate clinical benefits.

Results: In total, 1,130 radiomics features and 512 DL features were successfully extracted. Model Rad+DL demonstrated superior AUC values compared to Model Rad and Model DL in the training and validation sets. DCA revealed that Model Rad+DL offered excellent clinical benefits when the threshold probability exceeded 20%.

Conclusions: Model Rad+DL exhibits superior potential in evaluating CFD progression, determining the optimal surgical timing for adult CFD patients.