Target volume delineation uncertainty (DU) is arguably one of the largest geometric uncertainties in radiotherapy that are accounted for using planning target volume (PTV) margins. Geometrical uncertainties are typically derived from a limited sample of patients. Consequently, the resultant margins are not tailored to individual patients. Furthermore, standard PTVs cannot account for arbitrary anisotropic extensions of the target volume originating from DU. We address these limitations by developing a method to measure DU for each patient by a single clinician. This information is then used to produce PTVs that account for each patient’s unique DU, including any required anisotropic component. We do so using a two-step uncertainty evaluation strategy that does not rely on multiple samples of data to capture the DU of a patient’s gross tumour volume (GTV) or clinical target volume. For simplicity, we will just refer to the GTV in the following. First, the clinician delineates two contour sets; one which bounds all voxels believed to have a probability of belonging to the GTV of 1, while the second includes all voxels with a probability greater than 0. Next, one specifies a probability density function for the true GTV boundary position within the boundaries of the two contours. Finally, a patient-specific PTV, designed to account for all systematic errors, is created using this information along with measurements of the other systematic errors. Clinical examples indicate that our margin strategy can produce significantly smaller PTVs than the van Herk margin recipe. Our new radiotherapy target delineation concept allows DUs to be quantified by the clinician for each patient, leading to PTV margins that are tailored to each unique patient, thus paving the way to a greater personalisation of radiotherapy.

靶区描绘不确定性（DU）可以说是放射治疗中最大的几何不确定性之一，使用计划靶区（PTV）裕度来解释。几何不确定性通常源自有限的患者样本。因此，所得的利润并不是针对个体患者量身定制的。此外，标准 PTV 无法解释源自 DU 的目标体积的任意各向异性扩展。我们通过开发一种由一名临床医生测量每位患者 DU 的方法来解决这些局限性。然后，该信息用于生成 PTV，以说明每位患者独特的 DU，包括任何所需的各向异性成分。我们使用两步不确定性评估策略来实现这一点，该策略不依赖于多个数据样本来捕获患者总肿瘤体积 (GTV) 或临床目标体积的 DU。为了简单起见，下文中我们仅指GTV。首先，临床医生描绘出两个轮廓集；第一个边界被认为属于 GTV 的概率为 1 的所有体素，而第二个包括概率大于 0 的所有体素。接下来，一个为 GTV 边界内的真实 GTV 边界位置指定概率密度函数。两个轮廓。最后，使用此信息以及其他系统误差的测量值来创建患者特定的 PTV，旨在考虑所有系统误差。临床例子表明，我们的保证金策略可以产生比 van Herk 保证金配方小得多的 PTV。 我们新的放射治疗靶区描绘概念允许临床医生对每位患者的 DU 进行量化，从而为每位患者量身定制 PTV 裕度，从而为放射治疗的更大个性化铺平道路。