Integrating Clinical and Radiomic Features to Predict Outcomes Following Regenerative Periodontal Therapy: An Exploratory Analysis.

This exploratory retrospective analysis investigated whether clinical and radiomic features could provide prognostic information regarding 6-month outcomes following regenerative periodontal treatment. Thirty-nine defects were classified using the Composite Outcome Measure as successful (COM1) or non-successful (COM2-4). Patient- and defect-related variables were obtained from clinical records, while radiomic features were extracted from baseline periapical radiographs using a standardized trapezoidal region of interest. After preprocessing, 29 radiomic features were retained. Univariate analysis, redundancy assessment, and multivariable logistic regression identified independent predictors, which were then combined in supervised machine learning models evaluated by stratified 5-fold cross-validation. Twenty-one defects (53.8%) achieved COM1, whereas 18 (46.2%) were classified as non-successful. Older age, higher sphericity, and lower intensity histogram 90th percentile values were independently associated with non-successful outcomes. The final logistic regression model showed good discrimination (AUC = 0.84; accuracy = 0.74; sensitivity = 0.72; specificity = 0.76). These findings suggest that integrating clinical and radiomic variables may support early prediction of regenerative outcomes, although external validation is required.