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

• URL: http://arxiv.org/abs/2606.11012v1
• Date: Tue, 09 Jun 2026 15:52:58 GMT
• Title: An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer
• Abstract summary: oART enables daily plan adaptation to interfraction anatomical variations, but cumulative dose estimation remains limited.<n>We introduce IMPACT-DoseAcc, an uncertainty-aware dose accumulation framework, within IMPACT for semantic feature-driven image analysis.<n>Nine cervical cancer patients were retrospectively analyzed using daily CBCT-derived virtual CTs for dose recalculation.
• Score: 0.6532395471196993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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