Department of Radiation Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Front Immunol. 2022 Apr 26;13:870842. doi: 10.3389/fimmu.2022.870842. eCollection 2022.
The combination of immunotherapy and chemoradiotherapy has become the standard therapeutic strategy for patients with unresected locally advance-stage non-small cell lung cancer (NSCLC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by pretreatment CT radiomics and clinical or radiological parameters.
A total of 126 advance-stage NSCLC patients with pneumonitis were enrolled in this retrospective study and divided into the training dataset ( =88) and the validation dataset ( = 38). A total of 837 radiomics features were extracted from regions of interest based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator. A logistic regression was applied to develop a radiomics nomogram. Receiver operating characteristics curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification.
There was no significant difference between the training and the validation datasets for any clinicopathological parameters in this study. The radiomics signature, named Rad-score, consisting of 11 selected radiomics features, has potential ability to differentiate between CIP and RP with the empirical and α-binormal-based AUCs of 0.891 and 0.896. These results were verified in the validation dataset with AUC = 0.901 and 0.874, respectively. The clinical and radiological parameters of bilateral changes ( < 0.001) and sharp border ( = 0.001) were associated with the identification of CIP and RP. The nomogram model showed good performance on discrimination in the training dataset (AUC = 0.953 and 0.950) and in the validation dataset (AUC = 0.947 and 0.936).
CT-based radiomics features have potential values for differentiating between patients with CIP and patients with RP. The addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.
免疫治疗联合放化疗已成为不可切除局部晚期非小细胞肺癌（NSCLC）患者的标准治疗策略，并导致治疗相关不良反应，特别是免疫检查点抑制剂相关肺炎（CIP）和放射性肺炎（RP）。本研究旨在通过治疗前 CT 放射组学和临床或影像学参数来区分 CIP 和 RP。
本回顾性研究共纳入 126 例患有肺炎的晚期 NSCLC 患者，分为训练数据集（n=88）和验证数据集（n=38）。从 CT 图像的肺实质窗中提取了 837 个放射组学特征。基于预测特征，采用最小绝对值收缩和选择算子构建放射组学特征。应用逻辑回归建立放射组学列线图。受试者工作特征曲线和曲线下面积（AUC）用于评估肺炎病因识别的性能。
在这项研究中，训练数据集和验证数据集中的任何临床病理参数均无显著差异。由 11 个选定放射组学特征组成的放射组学特征命名为 Rad-score，具有区分 CIP 和 RP 的潜在能力，经验和α-双正态 AUC 分别为 0.891 和 0.896。在验证数据集中，这些结果分别为 AUC=0.901 和 0.874。双侧改变（P<0.001）和锐利边界（P=0.001）的临床和影像学参数与 CIP 和 RP 的识别相关。在训练数据集（AUC=0.953 和 0.950）和验证数据集（AUC=0.947 和 0.936）中，列线图模型的鉴别性能均较好。
基于 CT 的放射组学特征在区分 CIP 和 RP 患者方面具有潜在价值。双侧改变和锐利边界的加入提高了分类模型的性能，这可能是改善相关临床决策的有用方法。
