Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China.
Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China.
BMC Pulm Med. 2024 Jan 2;24(1):11. doi: 10.1186/s12890-023-02827-4.
Pneumocystis jirovecii pneumonia (PCP) could be fatal to patients without human immunodeficiency virus (HIV) infection. Current diagnostic methods are either invasive or inaccurate. We aimed to establish an accurate and non-invasive radiomics-based way to identify the risk of PCP infection in non-HIV patients with computed tomography (CT) manifestation of pneumonia.
This is a retrospective study including non-HIV patients hospitalized for suspected PCP from January 2010 to December 2022 in one hospital. The patients were randomized in a 7:3 ratio into training and validation cohorts. Computed tomography (CT)-based radiomics features were extracted automatically and used to construct a radiomics model. A diagnostic model with traditional clinical and CT features was also built. The area under the curve (AUC) were calculated and used to evaluate the diagnostic performance of the models. The combination of the radiomics features and serum β-D-glucan levels was also evaluated for PCP diagnosis.
A total of 140 patients (PCP: N = 61, non-PCP: N = 79) were randomized into training (N = 97) and validation (N = 43) cohorts. The radiomics model consisting of nine radiomic features performed significantly better (AUC = 0.954; 95% CI: 0.898-1.000) than the traditional model consisting of serum β-D-glucan levels (AUC = 0.752; 95% CI: 0.597-0.908) in identifying PCP (P = 0.002). The combination of radiomics features and serum β-D-glucan levels showed an accuracy of 95.8% for identifying PCP infection (positive predictive value: 95.7%, negative predictive value: 95.8%).
Radiomics showed good diagnostic performance in differentiating PCP from other types of pneumonia in non-HIV patients. A combined diagnostic method including radiomics and serum β-D-glucan has the potential to provide an accurate and non-invasive way to identify the risk of PCP infection in non-HIV patients with CT manifestation of pneumonia.
ClinicalTrials.gov (NCT05701631).
肺孢子菌肺炎（PCP）可对无人类免疫缺陷病毒（HIV）感染的患者造成致命影响。目前的诊断方法要么具有侵袭性，要么不够准确。我们旨在建立一种准确且非侵入性的基于放射组学的方法，以识别 CT 表现为肺炎的非 HIV 患者发生 PCP 感染的风险。
这是一项回顾性研究，纳入了 2010 年 1 月至 2022 年 12 月期间在一家医院因疑似 PCP 住院的非 HIV 患者。患者按 7:3 的比例随机分为训练和验证队列。自动提取基于 CT 的放射组学特征，并用于构建放射组学模型。还构建了包含传统临床和 CT 特征的诊断模型。计算曲线下面积（AUC），用于评估模型的诊断性能。还评估了放射组学特征与血清β-D-葡聚糖水平相结合对 PCP 诊断的效果。
共纳入 140 例患者（PCP：N=61，非 PCP：N=79），随机分为训练（N=97）和验证（N=43）队列。由 9 个放射组学特征组成的放射组学模型的表现明显优于由血清β-D-葡聚糖水平组成的传统模型（AUC=0.954；95%CI：0.898-1.000），用于识别 PCP（P=0.002）。放射组学特征与血清β-D-葡聚糖水平的组合对识别 PCP 感染的准确率为 95.8%（阳性预测值：95.7%，阴性预测值：95.8%）。
放射组学在区分非 HIV 患者的 PCP 与其他类型肺炎方面具有良好的诊断性能。包括放射组学和血清β-D-葡聚糖的联合诊断方法有可能提供一种准确且非侵入性的方法，以识别 CT 表现为肺炎的非 HIV 患者发生 PCP 感染的风险。
ClinicalTrials.gov（NCT05701631）。
