Gadsby Alyssa, Liu Tian, Samstein Robert, Zhang Jiahan, Lei Yang, Rosenzweig Kenneth E, Chao Ming
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
Adv Radiat Oncol. 2025 Jul 3;10(8):101825. doi: 10.1016/j.adro.2025.101825. eCollection 2025 Aug.
This study aims to evaluate the impact of varying definitions of normal lung volume on the prediction of radiation pneumonitis (RP) risk in patients with locally advanced non-small cell lung cancer undergoing radiation therapy.
Dosimetric variables V20, V5, and mean lung dose (MLD) were extracted from the treatment plans of 442 patients enrolled in the NRG Oncology Radiation Therapy Oncology Group 0617 trial. Three different definitions of lung volume were evaluated: total lung excluding gross tumor target, total lung excluding clinical target volume, and total lung excluding planning target volume (TL-PTV). Patients were grouped as "no-RP2" ( = 377, grade ≤1 RP) and "RP2" ( = 65, grade ≥2 RP). Statistical analyses were performed to assess the effect of lung volume definition on RP2 prediction. Three supervised machine learning models-logistic regression, k-nearest neighbor (kNN), and eXtreme Gradient Boosting-were used to evaluate predictive performance. Model performance was quantified using the area under the receiver operating characteristic curve, and statistical significance was tested via a bootstrap analysis. Shapley Additive Explanations (SHAP) were applied to interpret feature contributions to model predictions.
Statistical analyses showed that V20 and MLD were significantly associated with RP2, while differences among the lung volume definitions were not statistically significant. Both k-nearest neighbor and eXtreme Gradient Boosting classifiers consistently yielded higher area under the receiver operating characteristic curve values for the TL-PTV definition compared to the other definitions, a finding supported by bootstrap analysis. SHAP analysis further indicated that V20 and MLD were the most influential predictors of RP2.
In line with previous studies, both statistical analysis and SHAP interpretation confirmed that V20 and MLD were associated with RP2. The machine learning models indicated that defining normal lung volume as TL-PTV yielded the highest predictive performance for RP2 risk. Further validation using external data sets are warranted to confirm these findings.
本研究旨在评估正常肺容积的不同定义对接受放射治疗的局部晚期非小细胞肺癌患者放射性肺炎（RP）风险预测的影响。
从参加NRG肿瘤学放射治疗肿瘤学组0617试验的442例患者的治疗计划中提取剂量学变量V20、V5和平均肺剂量（MLD）。评估了三种不同的肺容积定义：排除大体肿瘤靶区的全肺、排除临床靶区体积的全肺和排除计划靶区体积的全肺（TL-PTV）。患者分为“无RP2”（n = 377，RP分级≤1级）和“RP2”（n = 65，RP分级≥2级）两组。进行统计分析以评估肺容积定义对RP2预测的影响。使用三种监督式机器学习模型——逻辑回归、k近邻（kNN）和极端梯度提升——来评估预测性能。使用受试者操作特征曲线下面积对模型性能进行量化，并通过自助分析检验统计学显著性。应用Shapley加性解释（SHAP）来解释特征对模型预测的贡献。
统计分析表明，V20和MLD与RP2显著相关，而肺容积定义之间的差异无统计学意义。与其他定义相比，k近邻和极端梯度提升分类器在TL-PTV定义下始终产生更高的受试者操作特征曲线下面积值，这一发现得到了自助分析的支持。SHAP分析进一步表明，V20和MLD是RP2最有影响力的数据。
与先前的研究一致，统计分析和SHAP解释均证实V20和MLD与RP2相关。机器学习模型表明，将正常肺容积定义为TL-PTV可产生最高的RP2风险预测性能。有必要使用外部数据集进行进一步验证以证实这些发现。
