肺梗死模拟社区获得性肺炎的放射组学研究。

Radiomics study on pulmonary infarction mimicking community-acquired pneumonia.

机构信息

Department of Radiology, Peking University First Hospital, Beijing, China.

Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.

出版信息

Clin Respir J. 2021 Jun;15(6):661-669. doi: 10.1111/crj.13341. Epub 2021 Mar 8.

DOI:10.1111/crj.13341

PMID:33686798

Abstract

INTRODUCTION AND OBJECTIVES

Pulmonary infarction (PI) shares similar symptoms and imaging presentations with community-acquired pneumonia (CAP), which might delay diagnosis and lead to devastating consequences. Noncontrast computed tomography (CT) is the first-line examination for the patients with the respiratory symptoms. This study aimed to investigate a radiomics method to differentiate PI from CAP using noncontrast-enhanced CT.

METHODS

Noncontrast-enhanced CT images of 54 patients with PI and 64 patients with CAP were retrospectively selected. All patients were confirmed using computed tomography pulmonary angiography (CTPA). A radiomics model was built with 18 texture features that showed significant differences between PI and CAP patients. For comparison, a clinical model using clinical biomarkers and an integrated model combining the radiomics and clinical biomarkers were also generated. An experienced radiologist performed diagnoses using the noncontrast-enhanced CT images. The parameters of the models were generated using a training dataset of 61 patients, whereas the performance of the models was evaluated using receiver operating characteristic (ROC) analysis and Harrell's concordance index (C-index) applied to a separate validation dataset of 57 patients.

RESULTS

The integrated model achieved the best performance (C-index 0.760, sensitivity 0.703, specificity 0.867, positive predictive value [PPV] 0.826, and negative predictive value [NPV] 0.765). The radiomics model was better than both the clinical model and the radiologist's interpretations (C-index 0.721, 0.707, 0.665, respectively; sensitivity 0.667, 0.630, 0.593; specificity 0.800, 0.785, 0.733; PPV 0.750, 0.739, 0.667; and NPV 0.727, 0.706, 0.667).

CONCLUSIONS

Radiomics features generated from noncontrast-enhanced CT images allow PI to be differentiated from CAP with considerable accuracy. The radiomics-based method could provide useful information in clinical practice.

摘要

介绍和目的

肺梗死（PI）与社区获得性肺炎（CAP）具有相似的症状和影像学表现，这可能会延迟诊断并导致严重后果。非增强 CT 是有呼吸系统症状患者的一线检查。本研究旨在探讨一种利用非增强 CT 区分 PI 与 CAP 的放射组学方法。

方法

回顾性选择 54 例 PI 患者和 64 例 CAP 患者的非增强 CT 图像。所有患者均经 CT 肺动脉造影（CTPA）证实。从 PI 与 CAP 患者中提取了 18 个有显著差异的纹理特征，建立了放射组学模型。为了比较，还生成了使用临床生物标志物的临床模型和结合放射组学和临床生物标志物的综合模型。一位有经验的放射科医生使用非增强 CT 图像进行诊断。使用 61 例患者的训练数据集生成模型参数，使用 57 例患者的独立验证数据集评估模型性能，采用接受者操作特征（ROC）分析和 Harrell 一致性指数（C 指数）进行评估。

结果

综合模型的性能最佳（C 指数 0.760、灵敏度 0.703、特异性 0.867、阳性预测值 [PPV] 0.826、阴性预测值 [NPV] 0.765）。放射组学模型优于临床模型和放射科医生的解释（C 指数分别为 0.721、0.707、0.665；灵敏度分别为 0.667、0.630、0.593；特异性分别为 0.800、0.785、0.733；PPV 分别为 0.750、0.739、0.667；NPV 分别为 0.727、0.706、0.667）。