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Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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%).
CONCLUSIONS: 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.
BACKGROUND: ClinicalTrials.gov (NCT05701631).
METHODS: 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.
RESULTS: 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%).
CONCLUSIONS: 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.
BACKGROUND: ClinicalTrials.gov (NCT05701631).
摘要：
背景：对没有人类免疫缺陷病毒（HIV）感染的患者来说，吉罗韦克氏肺孢子菌肺炎（PCP）可能是致命的。当前的诊断方法是侵入性的或不准确的。我们旨在建立一种准确且无创的基于影像组学的方法，以识别具有计算机断层扫描（CT）表现的肺炎的非HIV患者中PCP感染的风险。
方法：这是一项回顾性研究，包括2010年1月至2022年12月在一家医院因疑似PCP住院的非HIV患者。患者以7:3的比例随机分为训练和验证队列。基于计算机断层扫描（CT）的影像组学特征被自动提取并用于构建影像组学模型。还建立了具有传统临床和CT特征的诊断模型。计算曲线下面积(AUC)并用于评价模型的诊断性能。还评估了影像组学特征和血清β-D-葡聚糖水平的组合用于PCP诊断。
结果：共有140名患者（PCP：N=61，非PCP：N=79）被随机分为训练组（N=97）和验证组（N=43）。由9个影像学特征组成的影像组学模型（AUC=0.954；95％CI：0.898-1.000）在识别PCP方面明显优于由血清β-D-葡聚糖水平组成的传统模型（AUC=0.752；95％CI：0.597-0.908）（P=0.002）。影像组学特征和血清β-D-葡聚糖水平的组合显示识别PCP感染的准确性为95.8％（阳性预测值：95.7％，阴性预测值：95.8%）。
结论：影像组学在区分非HIV患者的PCP和其他类型肺炎方面显示出良好的诊断性能。包括放射组学和血清β-D-葡聚糖的组合诊断方法有可能提供一种准确且非侵入性的方法来识别具有肺炎CT表现的非HIV患者中PCP感染的风险。
背景：ClinicalTrials.gov（NCT05701631）。
方法：这是一项回顾性研究，包括2010年1月至2022年12月在一家医院因疑似PCP住院的非HIV患者。患者以7:3的比例随机分为训练和验证队列。基于计算机断层扫描（CT）的影像组学特征被自动提取并用于构建影像组学模型。还建立了具有传统临床和CT特征的诊断模型。计算曲线下面积(AUC)并用于评价模型的诊断性能。还评估了影像组学特征和血清β-D-葡聚糖水平的组合用于PCP诊断。
结果：共有140名患者（PCP：N=61，非PCP：N=79）被随机分为训练组（N=97）和验证组（N=43）。由9个影像学特征组成的影像组学模型（AUC=0.954；95％CI：0.898-1.000）在识别PCP方面明显优于由血清β-D-葡聚糖水平组成的传统模型（AUC=0.752；95％CI：0.597-0.908）（P=0.002）。影像组学特征和血清β-D-葡聚糖水平的组合显示识别PCP感染的准确性为95.8％（阳性预测值：95.7％，阴性预测值：95.8%）。
结论：影像组学在区分非HIV患者的PCP和其他类型肺炎方面显示出良好的诊断性能。包括放射组学和血清β-D-葡聚糖的组合诊断方法有可能提供一种准确且非侵入性的方法来识别具有肺炎CT表现的非HIV患者中PCP感染的风险。
背景：ClinicalTrials.gov（NCT05701631）。
