An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer

2026-06-09

AI Summary

This work addresses the challenge of uncertainty in cumulative dose estimation during online adaptive radiotherapy, which arises from deformable image registration (DIR) errors and anatomical changes. The authors propose IMPACT-DoseAcc, a framework implemented within the IMPACT platform that enables semantic feature–driven, uncertainty-aware dose accumulation. By integrating Bayesian segmentation–guided registration with a multi-model ensemble strategy, the method jointly quantifies both anatomical and epistemic uncertainties associated with DIR and, for the first time, propagates voxel-level uncertainties to cumulative dose metrics. An anatomy variability–weighted mechanism enhances inter-fraction stability, yielding probabilistic dose–volume histograms (DVHs). Experiments demonstrate a strong correlation between DIR uncertainty and geometric error (Pearson’s r = 0.63 for CTVt and 0.66 for bladder), with CTVt probabilistic DVH coverage achieving 96.3 ± 3.9%, confirming the method’s validity and calibration. The framework is integrated into 3D Slicer to support reproducible clinical workflows.

Abstract

Background and purpose: oART enables daily plan adaptation to interfraction anatomical variations, but cumulative dose estimation remains limited by DIR, segmentation, and anatomical uncertainties. We introduce IMPACT-DoseAcc, an uncertainty-aware dose accumulation framework, within IMPACT for semantic feature-driven image analysis. The framework is modality- and disease-agnostic and is applied to CBCT-guided oART for cervical cancer (LACC). Material and Methods: Nine LACC patients were retrospectively analyzed using daily CBCT-derived virtual CTs for dose recalculation. IMPACT-DoseAcc focuses on uncertainty from DIR, without modeling vCT-generation uncertainty. Two DIR uncertainty strategies were tested within IMPACT-Reg: a Bayesian segmentation-guided approach using one probabilistic model to quantify anatomical uncertainty, and an ensemble of segmentation models targeting structures to capture epistemic variability. Voxel-wise uncertainty maps were propagated through dose warping and accumulation to generate probabilistic dose-volume histograms. Ensemble uncertainty was quantified from voxel-wise standard deviation across deformation fields, and geometric error was assessed using surface distance between warped and validated contours. Anatomical-variability weighting refined aggregation. Results: Ensemble DIR uncertainty correlated with geometric error, with Pearson coefficients of 0.63 for CTVt and 0.66 for bladder. For CTVt, pDVHs achieved 96.3 +/- 3.9% coverage, showing calibration of propagated uncertainty. Weighting stabilized estimates across fractions and organs. Conclusions: IMPACT-DoseAcc propagates registration-driven uncertainty to cumulative dose metrics, improving interpretation of accumulated dose under anatomical variations. Its 3DSlicer integration supports reproducible, uncertainty-informed ART workflows.

Problem

• dose accumulation
• uncertainty estimation
• adaptive radiotherapy
• deformable image registration
• cervical cancer

Innovation

• uncertainty quantification
• dose accumulation
• deformable image registration
• adaptive radiotherapy
• probabilistic DVH