Evin Cécile, Razakamanantsoa Léo, Gardavaud François, Papillon Léa, Boulaala Hamza, Ferrer Loïc, Gallinato Olivier, Colin Thierry, Moussa Sondos Ben, Harfouch Yara, Foulquier Jean-Noël, Guillerm Sophie, Bibault Jean-Emmanuel, Huguet Florence, Wagner Mathilde, Rivin Del Campo Eleonor
Department of Radiation Oncology, Tenon University Hospital, APHP, Sorbonne University, Paris, France; Laboratoire d'Imagerie Biomédicale UMR 7371 - U1146, Sorbonne University, Paris, France.
Department of Radiology Imaging and Interventional Radiology (IRIS), Tenon University Hospital, APHP, Sorbonne University, Paris, France.
Clin Lung Cancer. 2025 Mar;26(2):93-103.e1. doi: 10.1016/j.cllc.2024.11.003. Epub 2024 Nov 20.
Treatment of locally advanced non small cell lung cancer (LA-NSCLC) is based on (chemo)radiotherapy, which may cause acute lung toxicity: radiation pneumonitis (RP). Its frequency seems to increase by the use of adjuvant durvalumab therapy.
To identify clinical, dosimetric, and radiomic factors associated with grade (G)≥2 RP and build a prediction model based on selected parameters.
This is a retrospective multicenter cohort study including patients receiving radiation therapy between 2015 and 2019 for LA-NSCLC. Baseline computed tomography scanners were segmented to extract radiomic features from the "Lung - Tumor" volume. Variables associated with the risk of G≥2 RP in the descriptive analysis were then selected for explanatory analysis, followed by predictive analysis.
153 patients were included in 4 centers (51 with G≥2 RP). Factors associated with G≥2 RP included a high initial hemoglobin level, dosimetric factors (mean dose to healthy lungs, lung V20Gy and V13Gy), the addition of maintenance durvalumab, and 7 radiomic features (intensity distribution and texture). Other factors were associated with an increased risk of G≥2 RP in our explanatory model, such as older age, low Tiffeneau ratio, and a decreased initial platelet count. The best-performing predictive model was a random forest-based learning model using clinical, dosimetric, and radiomic variables, with an area under the ROC curve of 0.72 (95%CI [0.63; 0.80]) versus 0.64 for models using one type of data.
The addition of radiomic features to clinical and dosimetric ones improves prediction of the occurrence of G≥2 RP in patients receiving radiotherapy for lung cancer.
局部晚期非小细胞肺癌（LA - NSCLC）的治疗以（化疗）放疗为基础，这可能会导致急性肺毒性：放射性肺炎（RP）。使用辅助度伐利尤单抗治疗似乎会增加其发生率。
确定与≥2级RP相关的临床、剂量学和放射组学因素，并基于选定参数建立预测模型。
这是一项回顾性多中心队列研究，纳入了2015年至2019年间接受LA - NSCLC放疗的患者。对基线计算机断层扫描进行分割，以从“肺 - 肿瘤”体积中提取放射组学特征。然后在描述性分析中选择与≥2级RP风险相关的变量进行解释性分析，随后进行预测性分析。
4个中心共纳入153例患者（51例发生≥2级RP）。与≥2级RP相关的因素包括初始血红蛋白水平高、剂量学因素（健康肺脏的平均剂量、肺V20Gy和V13Gy）、添加维持度伐利尤单抗以及7个放射组学特征（强度分布和纹理）。在我们的解释模型中，其他因素也与≥2级RP风险增加相关，如年龄较大、蒂芬诺比率较低以及初始血小板计数降低。表现最佳的预测模型是使用临床、剂量学和放射组学变量的基于随机森林的学习模型，其ROC曲线下面积为0.72（95%CI [0.63; 0.80]），而使用单一类型数据的模型为0.64。
将放射组学特征添加到临床和剂量学特征中可改善对接受肺癌放疗患者发生≥2级RP的预测。
