German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany; OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany; OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Radiother Oncol. 2024 Jan;190:110013. doi: 10.1016/j.radonc.2023.110013. Epub 2023 Nov 14.
Radiation pneumonitis (RP) remains a major complication in non-small cell lung cancer (NSCLC) patients undergoing radiochemotherapy (RCHT). Traditionally, the mean lung dose (MLD) and the volume of the total lung receiving at least 20 Gy (V) are used to predict RP in patients treated with normo-fractionated photon therapy. However, other models, including the actual dose-distribution in the lungs using the effective α/β model or a combination of radiation doses to the lungs and heart, have been proposed for predicting RP. Moreover, the models established for photons may not hold for patients treated with passively-scattered proton therapy (PSPT). Therefore, we here tested and validated novel predictive parameters for RP in NSCLC patient treated with PSPT.
Data on the occurrence of RP, structure files and dose-volume histogram parameters for lungs and heart of 96 NSCLC patients, treated with PSPT and concurrent chemotherapy, was retrospectively retrieved from prospective clinical studies of two international centers. Data was randomly split into a training set (64 patients) and a validation set (32 patients). Statistical analyses were performed using binomial logistic regression.
The biologically effective dose (BED) of the'lungs - GTV' significantly predicted RP ≥ grade 2 in the training-set using both a univariate model (p = 0.019, AUC = 0.72) and a multivariate model in combination with the effective α/β parameter of the heart (p = 0.006, [Formula: see text] = 0.043, AUC = 0.74). However, these results did not hold in the validation-set (AUC = 0.52 andAUC = 0.50, respectively). Moreover, these models were found to neither outperform a model built with the MLD (p = 0.015, AUC = 0.73, AUC = 0.51), nor a multivariate model additionally including the V of the heart (p = 0.039, p = 0.58, AUC = 0.74, AUC = 0.53).
Using the effective α/β parameter of the lungs and heart we achieved similar performance to commonly used models built for photon therapy, such as MLD, in predicting RP ≥ grade 2. Therefore, prediction models developed for photon RCHT still hold for patients treated with PSPT.
放射性肺炎（RP）仍然是非小细胞肺癌（NSCLC）患者接受放化疗（RCHT）的主要并发症。传统上，使用平均肺剂量（MLD）和全肺接受至少 20Gy（V）的体积来预测接受常规分割光子治疗的患者的 RP。然而，已经提出了其他模型，包括使用有效α/β模型的肺部实际剂量分布或肺部和心脏的放射剂量组合，用于预测 RP。此外，为接受被动散射质子治疗（PSPT）的患者建立的模型可能不适用于接受光子治疗的患者。因此，我们在这里测试并验证了用于接受 PSPT 治疗的 NSCLC 患者的 RP 的新型预测参数。
从两个国际中心的前瞻性临床研究中回顾性检索了 96 例 NSCLC 患者接受 PSPT 和同期化疗后发生 RP、肺部和心脏结构文件以及剂量-体积直方图参数的数据。数据随机分为训练集（64 例）和验证集（32 例）。使用二项逻辑回归进行统计分析。
在训练集中，使用单变量模型（p=0.019，AUC=0.72）和结合心脏有效α/β参数的多变量模型，肺部-GTV 的生物有效剂量（BED）显著预测 RP≥2 级（p=0.006，[公式：见文本]=0.043，AUC=0.74）。然而，这些结果在验证集中并不成立（AUC=0.52 和 AUC=0.50）。此外，这些模型的性能既不如 MLD 构建的模型（p=0.015，AUC=0.73，AUC=0.51），也不如另外包括心脏 V 的多变量模型（p=0.039，p=0.58，AUC=0.74，AUC=0.53）。
使用肺部和心脏的有效α/β参数，我们在预测 RP≥2 级方面取得了与光子治疗常用模型（如 MLD）相似的性能。因此，为光子 RCHT 开发的预测模型仍然适用于接受 PSPT 治疗的患者。
