Uncertainty-Aware Framework for CT Radiation Dose Optimization in the Active Surveillance of Small Renal Masses:...

Background: Active surveillance of small renal masses is challenged by cumulative radiation exposure from repeated CT imaging, raising long-term health concerns. Low-dose CT protocols offer a strategy to mitigate this risk but are limited by uncertainty regarding measurement accuracy and potential effects on clinical decision-making. Methods: We propose an uncertainty-aware analytical framework using a multi-observer dataset of 40 paired CT cases (low-dose vs. standard-dose). The methodology combines statistical agreement assessment (concordance correlation coefficient, intraclass correlation coefficient), multi-algorithm machine learning prediction (linear regression, random forest, gradient boosting, and SVR), and integrated uncertainty quantification to evaluate equivalence across imaging protocols. Results: Comparative analysis demonstrates near-perfect concordance between protocols (concordance correlation coefficient = 0.9930). Linear regression achieved the highest predictive performance (R² = 0.9933, MAE = 0.4239 mm, MAPE = 2.07%), outperforming more complex ensemble models, highlighting that interpretable models can achieve superior accuracy without compromising reliability. Conclusions: Clinically, the framework supports the safe adoption of low-dose CT for longitudinal tumor assessment, preserving measurement fidelity and diagnostic confidence essential for timely intervention or continued surveillance. Radiologically, it ensures robust lesion characterization across protocols while minimizing cumulative radiation exposure, particularly in younger patients. By integrating uncertainty quantification, this approach enhances transparency, informs clinical decision-making, and facilitates personalized, evidence-based surveillance strategies, promoting safer, dose-optimized imaging in the management of small renal masses.