DataSheet_1_Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept.docx

BackgroundImmune checkpoint inhibitors (ICI) may cause pneumonitis, resulting in potentially fatal lung inflammation. However, distinguishing pneumonitis from pneumonia is time-consuming and challenging. To fill this gap, we build an image-based tool, and further evaluate it clinically alongside relevant blood biomarkers.
Materials and methodsWe studied CT images from 97 patients with pneumonia and 29 patients with pneumonitis from acute myeloid leukemia treated with ICIs. We developed a CT-derived signature using a habitat imaging algorithm, whereby infected lungs are segregated into clusters (“habitats”). We validated the model and compared it with a clinical-blood model to determine whether imaging can add diagnostic value.
ResultsHabitat imaging revealed intrinsic lung inflammation patterns by identifying 5 distinct subregions, correlating to lung parenchyma, consolidation, heterogenous ground-glass opacity (GGO), and GGO-consolidation transition. Consequently, our proposed habitat model (accuracy of 79%, sensitivity of 48%, and specificity of 88%) outperformed the clinical-blood model (accuracy of 68%, sensitivity of 14%, and specificity of 85%) for classifying pneumonia versus pneumonitis. Integrating imaging and blood achieved the optimal performance (accuracy of 81%, sensitivity of 52% and specificity of 90%). Using this imaging-blood composite model, the post-test probability for detecting pneumonitis increased from 23% to 61%, significantly (p = 1.5E − 9) higher than the clinical and blood model (post-test probability of 22%).
ConclusionHabitat imaging represents a step forward in the image-based detection of pneumonia and pneumonitis, which can complement known blood biomarkers. Further work is needed to validate and fine tune this imaging-blood composite model and further improve its sensitivity to detect pneumonitis.

Background
免疫检查点抑制剂（immune checkpoint inhibitors, ICI）可引发免疫相关肺炎（pneumonitis），导致潜在致命性肺部炎症。然而，区分免疫相关肺炎与普通肺炎耗时且极具挑战。为填补这一研究空白，我们开发了一款基于影像的辅助工具，并联合相关血液生物标志物开展临床评估。
Materials and methods
我们纳入了97例普通肺炎患者与29例接受免疫检查点抑制剂治疗的急性髓系白血病（acute myeloid leukemia）合并免疫相关肺炎患者的计算机断层扫描（CT）图像。我们采用栖息地成像（habitat imaging）算法构建了CT衍生特征签名，将受感染的肺部区域分割为多个聚类（即“栖息地”）。随后对该模型进行验证，并与临床-血液生物标志物模型进行对比，以评估影像特征是否可提升诊断价值。
Results
栖息地成像算法通过识别5个独立亚区，揭示了肺部炎症的固有模式，这些亚区分别对应肺实质、肺实变、混杂性磨玻璃密度影（ground-glass opacity, GGO）以及磨玻璃影-实变过渡区。本研究提出的栖息地成像模型准确率达79%、灵敏度为48%、特异度为88%，在区分普通肺炎与免疫相关肺炎的任务中优于临床-血液生物标志物模型（准确率68%、灵敏度14%、特异度85%）。整合影像与血液标志物的联合模型实现了最优性能：准确率81%、灵敏度52%、特异度为90%。使用该影像-血液联合模型时，免疫相关肺炎的检测后概率从23%提升至61%，显著高于临床-血液模型的检测后概率（22%，P=1.5×10^-9）。
Conclusion
栖息地成像算法为基于影像的普通肺炎与免疫相关肺炎鉴别提供了重要进展，可作为现有血液生物标志物的有效补充。未来仍需开展更多研究以验证并优化该影像-血液联合模型，进一步提升其检测免疫相关肺炎的灵敏度。