Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation

Radiologists have used margin characteristics based on routine visual analysis; however, the attenuation changes at the margin of the lesion on CT images have not been quantitatively assessed. We established a CT-based margin analysis method by comparing a target lesion with normal lung attenuation, drawing a slope to represent the attenuation changes. This approach was applied to patients with invasive mucinous adenocarcinoma (n = 40) or bacterial pneumonia (n = 30). Correlations among multiple regions of interest (ROIs) were obtained using intraclass correlation coefficient (ICC) values. CT visual assessment, margin and texture parameters were compared for differentiating the two disease entities. The attenuation and margin parameters in multiple ROIs showed excellent ICC values. Attenuation slopes obtained at the margins revealed a difference between invasive mucinous adenocarcinoma and pneumonia (PP = 0.02), ground-glass opacity (OR, 8.55; 95% CI, 2.09–34.95; P = 0.003), and gradually declining attenuation at the margin (OR, 12.63; 95% CI, 2.77–57.51, P = 0.001). CT-based margin analysis method has a potential to act as an imaging parameter for differentiating invasive mucinous adenocarcinoma and bacterial pneumonia.放射科医师以往多采用基于常规视觉分析的病灶边缘特征开展评估，但CT图像上靶病变（target lesion）边缘的密度变化尚未得到定量评估。本研究建立了一种基于CT的边缘分析方法：通过将靶病变与正常肺密度进行对比，绘制斜率以表征密度变化情况。该方法被应用于浸润性黏液腺癌（invasive mucinous adenocarcinoma，n=40）与细菌性肺炎（bacterial pneumonia，n=30）患者队列。研究采用组内相关系数（intraclass correlation coefficient，ICC）计算多个感兴趣区（region of interest，ROI）间的相关性。随后对比CT视觉评估结果、边缘参数与纹理参数，以鉴别这两种疾病类型。结果显示，多个感兴趣区的密度及边缘参数均展现出优异的组内相关系数值。病灶边缘获取的密度斜率可有效区分浸润性黏液腺癌与肺炎（PP=0.02）、磨玻璃影（ground-glass opacity，优势比（odds ratio，OR）=8.55；95%置信区间（confidence interval，CI）=2.09–34.95；P=0.003）以及边缘渐降性密度改变（OR=12.63；95%CI=2.77–57.51；P=0.001）。综上，基于CT的边缘分析方法具备作为影像学参数鉴别浸润性黏液腺癌与细菌性肺炎的潜力。