Pneumocystis Jirovecii Pneumonia
Conditions
Keywords
Computed tomography, Diagnostic tests, Pneumocystis jirovecii pneumonia, Radiomics
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTRadiomic model
Radiomic model for PCP diagnosis
Sponsors
Chinese PLA General Hospital
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* with HIV infection * taking trimethoprim-sulfamethoxazole for prophylaxis * undiagnosed by qPCR and IF staining tests
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| the diagnostic performance of radiomic model in the PCP diagnosis | 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. |
Countries
China
Outcome results
None listed