Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
Radiother Oncol. 2024 Jul;196:110319. doi: 10.1016/j.radonc.2024.110319. Epub 2024 May 1.
Recently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model).
Totally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs.
The final selected PET-IBMs were 90 percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively.
The composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.
最近，基于基线口干、腮腺（PG）和颌下腺（SMG）平均剂量，建立了一种全面的口干预测模型。此前，已有研究表明 PG 的正电子发射断层扫描（PET）成像生物标志物（IBMs）可改善口干预测。因此，本研究旨在探索 PG 和 SMG 的额外 PET-IBMs 对近期全面口干预测模型（即参考模型）的潜在改善作用。
共纳入 540 例头颈部癌症患者，分为训练队列和验证队列。基于参考模型，采用Bootstrap 逐步向前选择法筛选 PG 和 SMG 的 PET-IBMs。将选择频率最高的 PG 和 SMG 的 IBM 纳入参考模型，得到 PG-IBM 模型和 SMG-IBM 模型，然后将两者组合成一个复合模型。采用曲线下面积（AUC）评估模型性能。采用似然比检验比较了参考模型和包含 IBM 的模型的预测性能。
最终入选的 PET-IBMs 是 PG SUV 的第 90 百分位数和 SMG SUV 的总能量。复合模型中纳入的另外两个 PET-IBMs 显著提高了参考模型的预测性能。在训练队列中，参考模型和复合模型的 AUC 分别为 0.67 和 0.69，在验证队列中，分别为 0.71 和 0.73。
纳入 PG 和 SMG 两个额外 PET-IBMs 的复合模型显著提高了参考口干模型的预测性能，有助于实现更个性化的预测方法。
