Objectives. anatomical changes are inevitable during the course of radiotherapy treatments and, if significant, can severely alter expected dose distributions and affect treatment outcome. Adaptive radiotherapy (ART) is employed to maintain the planned distribution and minimise detriment to predicted treatment outcome. Typically, patients who may benefit from adaptive planning are identified via a re-planning process, i.e., re-simulation, re-contouring, re-planning and treatment plan quality assurance (QA). This time-intensive process significantly increases workload, can introduce delays and increases unnecessary stress to those patients who will not actually gain benefit. We consider it crucial to develop efficient models to predict changes to target coverage and trigger ART, without the need for re-planning. Methods. knowledge-based planning (KBP) models were developed using data for 20 patients’ (400 fractions) to predict changes in PTV V₉₅ coverage Initially, this change in coverage was calculated on the synthetic computerised tomography (sCT) images produced using the Velocity adaptive radiotherapy software. Models were developed using patient (cell death bio-marker) and treatment fraction (PTV characteristic) specific parameters to predict and verified using five patients (100 fractions) data. Results. three models were developed using combinations of patient and fraction specific terms. The prediction accuracy of the model developed using biomarker (PD-L1 expression) and the difference in ‘planning’ and ‘fraction’ PTV centre of the mass (characterised by mean square difference, MSD) had the higher prediction accuracy, predicting the within1.0% for 77% of the total fractions; with 59% for the model developed using, PTV size, PD-L1 and MSD and 48% PTV size and MSD respectively. Conclusion. the KBP models can predict very effectively and efficiently for advanced-stage NSCLC patients treated using volumetric modulated arc therapy and to identify patients who may benefit from adaption for a specific fraction.

目标。在放射治疗过程中，解剖结构的变化是不可避免的，如果显着，可能会严重改变预期的剂量分布并影响治疗结果。适应性放射治疗 (ART) 用于维持计划的分布并最大限度地减少对预测治疗结果的损害。通常，可能受益于适应性规划的患者是通过重新规划过程来识别的，即重新模拟、重新塑造、重新规划和治疗计划质量保证 (QA)。这个耗时的过程会显着增加工作量，可能会导致延误并增加那些实际上不会受益的患者的不必要压力。我们认为开发有效的模型来预测目标覆盖范围的变化并触发 ART 至关重要，而无需重新规划。方法。使用 20 名患者（400 个分数）的数据开发了基于知识的规划 (KBP) 模型，以预测 PTV V ₉₅覆盖范围的变化 最初，这种覆盖范围的变化是根据使用速度自适应生成的合成计算机断层扫描 (sCT) 图像计算的放疗软件。使用患者（细胞死亡生物标志物）和治疗分数（PTV 特征）特定参数开发模型，以使用五名患者（100 个分数）数据进行预测和验证。结果. 使用患者和分数特定术语的组合开发了三个模型。使用生物标志物（PD-L1 表达）开发的模型的预测准确度以及“计划”和“分数”PTV 质心的差异（以均方差，MSD 为特征）具有更高的预测准确度，预测范围内1。 0% 占总分数的 77%；使用 PTV 大小、PD-L1 和 MSD 开发的模型占 59%，PTV 大小和 MSD 分别占 48%。结论。KBP 模型可以非常有效地预测使用容积调节弧治疗的晚期 NSCLC 患者，并识别可能受益于特定分数的适应症的患者。