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Updated: Sep 14, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Computed tomography-based radiomics prediction model for differentiating invasive pulmonary aspergillosis and
Zhiguo Peng1, Xingzhe Gao2, Miao He3
1Department of Organ Transplantation, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Background:
Pneumocystis 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.
Methods:
In 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.
Results:
Classifiers 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).
Conclusions:
This 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.
Insights
A new CT-based radiomics model effectively differentiates invasive pulmonary aspergillosis (IPA) from Pneumocystis jirovecii pneumonia (PJP). Including ground-glass opacity (GGO) in the region of interest improved diagnostic performance, aiding clinical decision-making.
Area of Science:
- Medical imaging and artificial intelligence
- Pulmonary infectious diseases
- Radiomics and machine learning applications
Background:
- Pneumocystis jirovecii pneumonia (PJP) and invasive pulmonary aspergillosis (IPA) are significant fungal lung infections.
- Distinguishing PJP from IPA on CT scans is challenging due to overlapping imaging features.
- Accurate differentiation is crucial as treatments for PJP and IPA differ significantly.
Purpose of the Study:
- To develop and validate a CT-based radiomics prediction model for differentiating IPA from PJP.
- To assess the diagnostic performance of the radiomics model in distinguishing between these two pulmonary infections.
Main Methods:
- A cohort of 97 patients (51 IPA, 46 PJP) underwent non-enhanced chest CT.
- Patients were divided into training and validation sets (7:3 ratio).
- Radiomics features were extracted from segmented regions of interest (consolidation with surrounding GGO, or consolidation alone) using the RadCloud platform.
- Six supervised learning classifiers were employed to build the prediction model, evaluated using ROC curves, AUC, sensitivity, specificity, precision, and F1-score.
Main Results:
- Segmentation including consolidation with surrounding ground-glass opacity (GGO) yielded superior prediction efficacy compared to using consolidation area alone.
- The XGBoost classifier demonstrated the best performance in the validation cohort.
- The optimal model achieved an Area Under the Curve (AUC) of 0.808 (95% CI, 0.655-0.961).
Conclusions:
- The developed radiomics model shows significant potential to assist in the differential diagnosis between PJP and IPA.
- Defining the region of interest to include both consolidation and surrounding GGO areas is more effective for accurate segmentation and diagnosis.
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