ArticlePhysics and imaging in radiation oncology2026
An efficient automated approach for accumulated dose estimation in prostate cancer radiotherapy.
Article in Physics and imaging in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background and Purpose: Anatomical variations during prostate radiotherapy can cause discrepancies between planned and delivered doses, which may become more pronounced in (ultra-)hypofractionated regimens. However, a standardized method for accumulated dose assessment is lacking. This study presented and validated an efficient automated workflow for retrospective dose accumulation in moderately hypofractionated prostate treatments, avoiding deformable image registration and daily dose recalculation. Materials and Methods: Twenty prostate cancer patients were analyzed retrospectively. The workflow combined daily cone-beam computed tomography (CBCT) imaging, online rigid registration, and artificial intelligence-based auto-contouring with planned dose data to estimate accumulated doses. Dose-volume metrics were analyzed for prostate, rectum, and bladder. Anatomical variations were assessed through volume and position analysis. Method accuracy was validated against CBCT-based dose recalculations in selected cases representing minimal and maximal variations. Results: The workflow required about one minute per fraction. Interfractional volume variability (coefficient of variation) was 35.2 %, 20.6 %, and 9.5 % for bladder, rectum, and prostate, respectively. Mean prostate displacement was 3.49 ± 2.07 mm, and the dice similarity coefficient averaged 0.83 ± 0.08. Bladder dose metrics (V Conclusions: Our automated workflow provides a robust, time-efficient method for monitoring interfractional dose variations in prostate radiotherapy. While constrained by CBCT image quality, the accuracy and practicality of the proposed method support its integration into clinical workflows, potentially improving offline adaptive decision-making.
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