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.

翻译：背景与目的：在线自适应放疗（oART）可实现针对分次间解剖变异日常计划调整，但累积剂量估算仍受限于DIR（形变配准）、分割和解剖结构不确定性。我们提出IMPACT-DoseAcc——一个不确定性感知的剂量累积框架，集成于IMPACT平台以实现语义特征驱动的图像分析。该框架具有模态和疾病通用性，并应用于宫颈癌（LACC）CBCT引导的oART。材料与方法：回顾性分析9例LACC患者，使用每日CBCT生成的虚拟CT进行剂量重计算。IMPACT-DoseAcc聚焦于DIR引起的不确定性，未建模vCT生成的不确定性。在IMPACT-Reg中测试了两种DIR不确定性策略：一种使用单一概率模型量化解剖不确定性的贝叶斯分割引导方法，以及针对目标结构捕捉认知变异性的分割模型集成方法。体素级不确定性图通过剂量形变与累积过程传播，生成概率性剂量体积直方图。集成不确定性通过形变场间体素标准差量化，几何误差通过形变后轮廓与验证轮廓的表面距离评估。解剖变异性加权优化聚合过程。结果：集成DIR不确定性与几何误差相关，CTVt和膀胱的Pearson系数分别为0.63和0.66。对于CTVt，pDVHs覆盖率达到96.3±3.9%，验证了传播不确定性的校准性。加权方法稳定了各分次和器官的估计值。结论：IMPACT-DoseAcc将配准驱动的不确定性传播至累积剂量指标，提高了在解剖变异下累积剂量解释的准确性。其3DSlicer集成支持可重复、不确定性知情的ART工作流。