Personalized prediction model for scar response after radionuclide therapy: development and validation in a Chinese cohort

Background Scarring represents a persistent clinical and psychosocial challenge, with considerable variability in treatment response among patients. While both clinical and morphologic factors can influence outcomes, robust, individualized prediction of scar treatment efficacy remains elusive. Objective To develop and validate an integrated predictive model for scar treatment outcomes using a combination of clinical and image-derived features in a Chinese cohort, and to translate this model into a web-based calculator for practical clinical application. This model requires validation in other ethnicities. Methods We retrospectively analyzed 117 Chinese patients with scars treated at a single center, dividing them into a training ( n = 83) and validation cohort ( n = 34). Clinical data (including age, scar height) and quantitative features extracted from standardized scar photographs (solidity and mean saturation [S_mean]) were used to construct clinical, image-based, and combined predictive models. Feature selection was performed via LASSO regression, and models were developed using multivariate logistic regression. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration metrics (Brier score, log loss, HL test), and decision curve analysis (DCA). Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated. A user-friendly web calculator was subsequently developed. Results Scar height and age (clinical factors) as well as solidity and S_mean (image-derived metrics) were identified as independent predictors of poor treatment outcome. The combined model demonstrated superior discrimination (AUC 0.970 [training], 0.908 [test]), calibration, and clinical utility compared to clinical or image-based models alone. Calibration curves and metrics indicated excellent agreement between predicted and observed probabilities for the combined model. DCA, NRI, and IDI analyses further highlighted the incremental value and net benefit of the integrated approach. A web-based calculator was developed to enable individualized outcome prediction and support clinical decision-making. Conclusion Integration of clinical and image-derived features enables robust, individualized prediction of scar treatment outcomes in this Chinese cohort. Our validated combined model, accessible via an easy-to-use web-based calculator, may enhance treatment planning, risk stratification, and patient counseling in scar management. Validation in diverse ethnic populations is essential.