AUTHOR=Peng Zhiguo , Gao Xingzhe , He Miao , Dong Xinyue , Wang Dongdong , Dai Zhengjun , Yu Dexin , Sun Huaibin , Tian Jun , Hu Yu 

TITLE=Computed tomography-based radiomics prediction model for differentiating invasive pulmonary aspergillosis and Pneumocystis jirovecii pneumonia

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1552556

DOI=10.3389/fcimb.2025.1552556

ISSN=2235-2988

ABSTRACT=BackgroundPneumocystis jirovecii and Aspergillus fumigatus are important pathogens that cause fungal pulmonary infections. Because the manifestations of P. jirovecii pneumonia (PJP) or invasive pulmonary aspergillosis (IPA) are difficult to differentiate on computed tomography (CT) images and the treatment of the two diseases is different, correct imaging for diagnosis is highly significant. The present study developed and validated the diagnostic performance of a CT-based radiomics prediction model for differentiating IPA from PJP.MethodsIn total, 97 patients, 51 with IPA and 46 with PJP, were included in this study. Each patient underwent a non-enhanced chest CT examination. All the patients were randomly divided into two cohorts, training and validation, at a ratio of 7:3 using random seeds automatically generated using the RadCloud platform. Image segmentation, feature extraction, and radiomic feature selection were performed on the RadCloud platform. The regions of interest (ROIs) were manually segmented, including the consolidation area with the surrounding ground-glass opacity (GGO) area and the consolidation area alone. Six supervised-learning classifiers were used to develop a CT-based radiomics prediction model, which was estimated using the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, specificity, precision, and F1-score. The radiomics score was also calculated to compare the prediction performance.ResultsClassifiers trained with the consolidation area and surrounding GGO area as the ROI showed better prediction efficacy than classifiers trained using only the consolidation area as the ROI. The XGBoost model performed better than the other classifiers and radiomics scores in the validation cohort, with an AUC of 0.808 (95% CI, 0.655–0.961).ConclusionsThis radiomics model can effectively assist in the differential diagnosis of PJP and IPA. The consolidation area with the surrounding GGO area was more suitable for ROI segmentation.