Uncertainty-Aware Framework for CT Radiation Dose Optimization in the Active Surveillance of Small Renal Masses: Clinical and Radiological Considerations.
Authors
Affiliations (6)
Affiliations (6)
- Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.
- Computers and Systems Department, Electronics Research Institute, Cairo 12622, Egypt.
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.
- Department of Histology and Cell Biology, Faculty of Medicine, Menoufia University, Shebin El Koum 32511, Egypt.
- Department of Histology, General Medicine Practice Program, Batterjee Medical College, Aseer 61961, Saudi Arabia.
- Department of Internal Medicine, Faculty of Medicine, Jazan University, P.O. Box 114, Jazan 45142, Saudi Arabia.
Abstract
<b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> Comparative analysis demonstrates near-perfect concordance between protocols (concordance correlation coefficient = 0.9930). Linear regression achieved the highest predictive performance (R<sup>2</sup> = 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. <b>Conclusions:</b> 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.