ArticleRadiological physics and technology2026
Application of support vector machines for modeling dosimetric uncertainty in radiotherapy planning.
Article in Radiological physics and technology, 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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6 authors.
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Abstract
We introduce two models to describe the robustness of radiotherapy plans. First, we propose an analytical model to assess coverage probabilities as a function of target and margin size. Second, we present a novel numerical framework for the assessment of planning technique robustness. This framework is intended to be implemented at the time of commissioning a new planning technique and is not specific to a particular modality. An uncertainty-aware machine learning model is built to estimate the sensitivity of dosimetric end points to geometric errors. A Monte Carlo method is then implemented to assess the probability of meeting different coverage objectives. The numerical framework is applied to our institution’s single-isocenter multi-target cranial stereotactic radiosurgery technique. For our four-arc planning technique, our numerical model recommends a total geometric uncertainty tolerance of [Formula: see text] mm to achieve a PTV coverage [Formula: see text] with probability [Formula: see text].
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