Tambas Makbule, van der Laan Hans Paul, van der Schaaf Arjen, Steenbakkers Roel J H M, Langendijk Johannes Albertus
Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands.
Cancers (Basel). 2022 Jan 28;14(3):681. doi: 10.3390/cancers14030681.
Selection of head and neck cancer (HNC) patients for proton therapy (PT) using plan comparison (VMAT vs. IMPT) for each patient is labor-intensive. Our aim was to develop a decision support tool to identify patients with high probability to qualify for PT, at a very early stage (immediately after delineation) to avoid delay in treatment initiation. A total of 151 HNC patients were included, of which 106 (70%) patients qualified for PT. Linear regression models for individual OARs were created to predict the D to the OARs for VMAT and IMPT plans. The predictors were OAR volume percentages overlapping with target volumes. Then, actual and predicted plan comparison decisions were compared. Actual and predicted OAR D (VMAT R = 0.953, IMPT R = 0.975) and NTCP values (VMAT R = 0.986, IMPT R = 0.992) were highly correlated. The sensitivity, specificity, PPV and NPV of the decision support tool were 64%, 87%, 92% and 51%, respectively. The expected toxicity reduction with IMPT can be predicted using only the delineation data. The probability of qualifying for PT is >90% when the tool indicates a positive outcome for PT. This tool will contribute significantly to a more effective selection of HNC patients for PT at a much earlier stage, reducing treatment delay.
为每位头颈癌（HNC）患者使用计划比较（容积调强放疗[VMAT]与调强质子放疗[IMPT]）来选择质子治疗（PT）患者的工作强度很大。我们的目标是开发一种决策支持工具，以便在非常早期阶段（勾画完成后立即）识别有高概率符合质子治疗条件的患者，从而避免治疗开始的延迟。总共纳入了151名头颈癌患者，其中106名（70%）患者符合质子治疗条件。针对各个危及器官（OAR）创建了线性回归模型，以预测VMAT和IMPT计划中危及器官的剂量（D）。预测因素是与靶体积重叠的危及器官体积百分比。然后，比较实际和预测的计划比较决策。实际和预测的危及器官剂量（VMAT的R = 0.953，IMPT的R = 0.975）以及正常组织并发症概率（NTCP）值（VMAT的R = 0.986，IMPT的R = 0.992）高度相关。决策支持工具的敏感性、特异性、阳性预测值和阴性预测值分别为64%、(87%)、92%和51%。仅使用勾画数据就可以预测IMPT预期的毒性降低情况。当该工具表明质子治疗有阳性结果时，符合质子治疗条件的概率>90%。该工具将显著有助于在更早阶段更有效地选择适合质子治疗的头颈癌患者，减少治疗延迟。
