Radiomics Model for the Diagnosis of Pneumocystis Jirovecii Pneumonia in Non-HIV Patients
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 140
- 试验地点
- 1
- 主要终点
- the diagnostic performance of radiomic model in the PCP diagnosis
研究概览
简要总结
To evaluate the performance of radiomics in differentiating Pneumocystis jirovecii pneumonia (PCP) from other types of pneumonia and to improve the diagnostic efficacy of non-invasive tests in non-HIV patients.
详细描述
Retrospective study, including non-HIV patients hospitalized for suspected PCP from January 2010 to December 2022. The included patients were randomized in a 7:3 ratio into training and validation cohorts. Radiomic features were extracted from semi-automatically identified infected areas in computed tomography (CT) scans and used to construct a radiomic model, which was then compared to a clinical-imaging model built with clinical and semantic CT features in terms of diagnostic performance of PCP. The combination of the radiomic model and serum β-D-glucan levels was also evaluated for PCP diagnosis.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •aged over eighteen years;
- •presence of an underlying disease known to be associated with PCP
- •symptoms of lower respiratory tract infection, such as fever, cough or dyspnea
- •signs of lung infection on high resolution CT at the on-set of the disease
- •received BAL examination within three days after CT scans
- •underwent qPCR and IF staining tests on the BAL fluid sample.
排除标准
- •with HIV infection
- •taking trimethoprim-sulfamethoxazole for prophylaxis
- •undiagnosed by qPCR and IF staining tests
结局指标
主要结局
the diagnostic performance of radiomic model in the PCP diagnosis
时间窗: 6 months
The included patients were randomized in a 7:3 ratio into training and validation cohorts. Radiomic features were extracted from semi-automatically identified infected areas in computed tomography (CT) scans and used to construct a radiomic model. Then, the area under the curve (AUC) of the receiver operating characteristic (ROC) curves were calculated and used to evaluate the diagnostic performance (accuracy, sensitivity, specialty, positive predictive value, negative predictive value) of the model for PCP diagnosis in both training and validation cohorts.
次要结局
未报告次要终点
研究者
Chen Liang_An
Professor
Chinese PLA General Hospital