Chen Yang, He Dehuai, Wu Yehua, Li Xiangying, Yang Kaifu, Zhan Yuefu, Chen Jianqiang, Zhou Xiaobo
Department of West China Biomedical Big Data Center and Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, China.
Department of Anesthesiology, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China.
Quant Imaging Med Surg. 2024 Jun 1;14(6):3863-3874. doi: 10.21037/qims-23-1476. Epub 2024 May 10.
Melioidosis pneumonia, caused by the bacterium , is a serious infectious disease prevalent in tropical regions. Chest computed tomography (CT) has emerged as a valuable tool for assessing the severity and progression of lung involvement in melioidosis pneumonia. However, there persists a need for the quantitative assessment of CT characteristics and staging methodologies to precisely anticipate disease progression. This study aimed to quantitatively extract CT features and evaluate a CT score-based staging system in predicting the progression of melioidosis pneumonia.
This study included 97 patients with culture-confirmed melioidosis pneumonia who presented between January 2002 and December 2021. Lung segmentation and annotation of lesions (consolidation, nodules, and cavity) were used for feature extraction. The features, including the involved area, amount, and intensity, were extracted. The CT scores of the lesion features were defined by the feature importance weight and qualitative stage of melioidosis pneumonia. Gaussian process regression (GPR) was used to predict patients with severe or critical melioidosis pneumonia according to CT scores.
The melioidosis pneumonia stages included acute stage (0-7 days), subacute stage (8-28 days), and chronic stage (>28 days). In the acute stage, the CT scores of all patients ranged from 2.5 to 6.5. In the subacute stage, the CT scores for the severe and mild patients were 3.0-7.0 and 2.0-5.0, respectively. In the chronic stage, the CT score of the mild patients fluctuated approximately between 2.5 and 3.5 in a linear distribution. Consolidation was the most common type of lung lesion in those with melioidosis pneumonia. Between stages I and II, the percentage of severe scans with nodules dropped from 72.22% to 47.62% (P<0.05), and the percentage of severe scans with cavities significantly increased from 16.67% to 57.14% (P<0.05). The GPR optimization function yielded area under the receiver operating characteristic curves of 0.71 for stage I, 0.92 for stage II, and 0.87 for all stages.
In patients with melioidosis pneumonia, it is reasonable to divide the period (the whole progression of melioidosis pneumonia) into three stages to determine the prognosis.
由细菌引起的类鼻疽肺炎是热带地区流行的一种严重传染病。胸部计算机断层扫描(CT)已成为评估类鼻疽肺炎肺部受累严重程度和进展的重要工具。然而,仍需要对CT特征进行定量评估并建立分期方法,以准确预测疾病进展。本研究旨在定量提取CT特征,并评估基于CT评分的分期系统对类鼻疽肺炎进展的预测能力。
本研究纳入了97例2002年1月至2021年12月期间经培养确诊的类鼻疽肺炎患者。通过肺部分割和病变(实变、结节和空洞)标注进行特征提取。提取包括受累面积、数量和密度在内的特征。根据类鼻疽肺炎的特征重要性权重和定性分期定义病变特征的CT评分。使用高斯过程回归(GPR)根据CT评分预测重症或危重症类鼻疽肺炎患者。
类鼻疽肺炎分期包括急性期(0 - 7天)、亚急性期(8 - 28天)和慢性期(>28天)。急性期,所有患者的CT评分范围为2.5至6.5。亚急性期,重症和轻症患者的CT评分分别为3.0 - 7.0和2.0 - 5.0。慢性期,轻症患者的CT评分呈线性分布,大致在2.5至3.5之间波动。实变是类鼻疽肺炎患者最常见的肺部病变类型。在I期和II期之间,有结节的重症扫描比例从72.22%降至47.62%(P<0.05),有空洞的重症扫描比例从16.67%显著增加至57.14%(P<0.05)。GPR优化函数得出I期的受试者工作特征曲线下面积为0.71,II期为0.92,所有阶段为0.87。
对于类鼻疽肺炎患者,将病程(类鼻疽肺炎的整个进展过程)分为三个阶段来判断预后是合理的。