Trial reportBMC pulmonary medicine2024
Computed tomography-based radiomics improves non-invasive diagnosis of Pneumocystis jirovecii pneumonia in non-HIV patients: a retrospective study.
Trial report in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05701631 (Radiomics Model for the Diagnosis of Pneumocystis Jirovecii Pneumonia in Non-HIV Patients), which is not on this map. Cited by 8 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Radiomics Model for the Diagnosis of Pneumocystis Jirovecii Pneumonia in Non-HIV Patients
Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- Development and internal validation of a CT radiomics-based model for severity classification in HIV-associatedJournal of thoracic disease · 2026Article
- Clinical impact and risk prediction of pulmonary barotrauma in non-HIVFrontiers in medicine · 2026Article
- Article
- Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII.European radiology · 2025Article
- Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics.Cancer management and research · 2025Article
- Quantitative Imaging Markers on HRCT Predict Rapid Progression and Adverse Events of Patients with Idiopathic Inflammatory Myopathies-Related Interstitial Lung Disease.International journal of general medicine · 2025Article
- Diagnostic predictive evaluation of pneumocystis jirovecii pneumonia using digital chest CT analysis combined with clinical features.Frontiers in physiology · 2025Article
- Development of a machine learning model in prediction of the rapid progression of interstitial lung disease in patients with idiopathic inflammatory myopathy.Quantitative imaging in medicine and surgery · 2024Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPneumocystis 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.
methodsThis 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.
resultsA 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%).
conclusionsRadiomics 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.
trial registrationClinicalTrials.gov (NCT05701631).
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