文章：

基于胸部CT与非影像学数据预测免疫治疗相关性肺炎

Predicting Immunotherapy-Induced Pneumonitis Based on Chest CT and Non-Imaging Data

原文发布日期：12 September 2025

DOI: 10.3390/cancers17182980

类型: Article

开放获取: 是

英文摘要：

Background/Objectives:Immune checkpoint inhibitors (ICIs) have been extensively used for the treatment of non-small cell lung cancer patients in recent years, providing a significant survival benefit. However, a major drawback of ICI-related immunotherapy is the risk of developing post-surgical pneumonitis.Methods: In this study, we propose a deep learning-embedded, multi-modality prediction approach to assess whether patients will develop ICI-pneumonitis after receiving ICI-based immunotherapy. This approach utilizes multi-modal data, including clinical data and pre-treatment lung screening computed tomography (CT) images. We extracted three types of features: (1) deep learning features from CT scans using a pre-trained vision transformer; (2) radiomic features from CT scans using pre-defined radiomic algorithms; (3) clinical features from patients’ electronic health records. We then compared ten machine learning algorithms for prediction based on these extracted features.Results: Our experiments demonstrated that using all three types of features leads to the best prediction result, with a prediction accuracy rate of 0.823 and an area under the receiver operating characteristic curve of 0.895.Conclusion:Multimodal approaches can result in superior prediction results compared to single modality approaches. This study demonstrates the feasibility of developing machine learning algorithms to accurately predict ICI-pneumonitis and contributes to the early identification of patients who are at a higher risk of developing pneumonitis.

摘要翻译： 

背景/目的：近年来，免疫检查点抑制剂（ICIs）已广泛应用于非小细胞肺癌患者的治疗，显著提升了患者的生存获益。然而，ICI相关免疫疗法的主要缺点在于存在术后肺炎的发生风险。 方法：本研究提出一种基于深度学习的多模态预测方法，用于评估患者在接受ICI免疫治疗后是否会发生ICI相关性肺炎。该方法整合了多模态数据，包括临床资料及治疗前肺部筛查计算机断层扫描（CT）图像。我们提取了三类特征：（1）通过预训练视觉Transformer从CT影像中提取的深度学习特征；（2）利用预设放射组学算法从CT影像中提取的放射组学特征；（3）从患者电子健康记录中获取的临床特征。随后，我们基于这些特征比较了十种机器学习算法的预测性能。 结果：实验结果表明，联合使用三类特征可获得最佳预测效果，预测准确率达0.823，受试者工作特征曲线下面积为0.895。 结论：与单一模态方法相比，多模态方法能获得更优的预测结果。本研究证实了开发机器学习算法准确预测ICI相关性肺炎的可行性，有助于早期识别肺炎高风险患者。

原文链接：

Predicting Immunotherapy-Induced Pneumonitis Based on Chest CT and Non-Imaging Data