National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China.
Med Phys. 2022 Mar;49(3):1547-1558. doi: 10.1002/mp.15451. Epub 2022 Jan 27.
Consolidation immunotherapy after completion of chemoradiotherapy has become the standard of care for unresectable locally advanced non-small cell lung cancer and can induce potentially severe and life-threatening adverse events, including both immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP), which are very challenging for radiologists to diagnose. Differentiating between CIP and RP has significant implications for clinical management such as the treatments for pneumonitis and the decision to continue or restart immunotherapy. The purpose of this study is to differentiate between CIP and RP by a CT radiomics approach.
We retrospectively collected the CT images and clinical information of patients with pneumonitis who received immune checkpoint inhibitor (ICI) only (n = 28), radiotherapy (RT) only (n = 31), and ICI+RT (n = 14). Three kinds of radiomic features (intensity histogram, gray-level co-occurrence matrix [GLCM] based, and bag-of-words [BoW] features) were extracted from CT images, which characterize tissue texture at different scales. Classification models, including logistic regression, random forest, and linear SVM, were first developed and tested in patients who received ICI or RT only with 10-fold cross-validation and further tested in patients who received ICI+RT using clinicians' diagnosis as a reference.
Using 10-fold cross-validation, the classification models built on the intensity histogram features, GLCM-based features, and BoW features achieved an area under curve (AUC) of 0.765, 0.848, and 0.937, respectively. The best model was then applied to the patients receiving combination treatment, achieving an AUC of 0.896.
This study demonstrates the promising potential of radiomic analysis of CT images for differentiating between CIP and RP in lung cancer, which could be a useful tool to attribute the cause of pneumonitis in patients who receive both ICI and RT.
放化疗后巩固免疫治疗已成为不可切除局部晚期非小细胞肺癌的标准治疗方法,可引发潜在严重且危及生命的不良事件,包括免疫检查点抑制剂相关肺炎(CIP)和放射性肺炎(RP),这对放射科医生的诊断极具挑战性。区分 CIP 和 RP 对临床管理具有重要意义,如肺炎的治疗以及继续或重新开始免疫治疗的决策。本研究旨在通过 CT 放射组学方法区分 CIP 和 RP。
我们回顾性收集了接受免疫检查点抑制剂(ICI)单药治疗(n=28)、放疗(RT)单药治疗(n=31)和 ICI+RT 联合治疗(n=14)的肺炎患者的 CT 图像和临床资料。从 CT 图像中提取了三种放射组学特征(强度直方图、灰度共生矩阵[GLCM]基于和词袋[BoW]特征),这些特征可在不同尺度下描述组织纹理。首先,我们使用 10 折交叉验证在仅接受 ICI 或 RT 的患者中开发和测试包括逻辑回归、随机森林和线性 SVM 在内的分类模型,并使用临床医生的诊断作为参考在接受 ICI+RT 的患者中进行进一步测试。
使用 10 折交叉验证,基于强度直方图特征、GLCM 特征和 BoW 特征构建的分类模型的曲线下面积(AUC)分别为 0.765、0.848 和 0.937。然后将最佳模型应用于接受联合治疗的患者,AUC 为 0.896。
本研究表明 CT 图像放射组学分析在区分肺癌中的 CIP 和 RP 方面具有很大的潜力,这可能是一种有用的工具,可归因于接受 ICI 和 RT 联合治疗的患者发生肺炎的原因。