BACKGROUND To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children. METHODS This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n = 86, validation cohort n = 29). A total of 970 radiomic features were extracted from CT images and key features were screened out to build radiomic signatures using the least absolute shrinkage and selection operator algorithm. A predictive nomogram was developed based on the signatures and clinical factors, and its performance was assessed by the receiver operating characteristic curve, calibration curve, and decision curve analysis. RESULTS Initially, 5 and 6 key features were selected to establish a radiomic signature from the pulmonary consolidation region (RS1) and a signature from lymph node region (RS2), respectively. A predictive nomogram was built combining RS1, RS2, and a clinical factor (duration of fever). Its classification performance (AUC = 0.971, 95% confidence interval [CI]: 0.912-1) was better than the senior radiologist's clinical judgment (AUC = 0.791, 95% CI: 0.636-0.946), the clinical factor (AUC = 0.832, 95% CI: 0.677-0.987), and the combination of RS1 and RS2 (AUC = 0.957, 95% CI: 0.889-1). The calibration curves indicated a good consistency of the nomogram. Decision curve analysis demonstrated that the nomogram was useful in clinical settings. CONCLUSIONS A CT-based predictive nomogram was proposed and could be conveniently used to differentiate pulmonary TB from CAP in children.

中文翻译：

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基于计算机断层扫描的预测列线图，用于区分儿童原发性进行性肺结核与社区获得性肺炎。

背景技术探讨预测列线图在优化基于计算机断层扫描（CT）的儿童社区获得性肺炎（CAP）原发性进行性肺结核（TB）的鉴别诊断中的价值。方法这项回顾性研究包括53例临床确诊的肺结核患者和62例CAP患者。将患者按照3：1的比例随机分组（主要队列n = 86，验证队列n = 29）。从CT图像中总共提取了970个放射学特征，并使用最小绝对收缩和选择算子算法筛选出关键特征以建立放射学特征。根据特征和临床因素开发了预测列线图，并通过接收器工作特性曲线，校准曲线，和决策曲线分析。结果最初，选择了5个关键特征和6个关键特征以分别从肺巩固区域（RS1）和淋巴结区域（RS2）建立放射学特征。建立了包含RS1，RS2和临床因素（发烧持续时间）的预测列线图。其分类表现（AUC = 0.971，95％置信区间[CI]：0.912-1）优于高级放射科医生的临床判断（AUC = 0.791，95％CI：0.636-0.946），是临床因素（AUC = 0.832， 95％CI：0.677-0.987），以及RS1和RS2的组合（AUC = 0.957，95％CI：0.889-1）。校准曲线表明列线图具有良好的一致性。决策曲线分析表明，列线图在临床环境中很有用。