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Updated: Aug 2, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification
Marcos A D Machado1,2, Ronnyldo R E Silva2,3, Mauro Namias4
1Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil.
Purpose:
To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans.
Methods:
Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient.
Results:
Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial.
Conclusion:
Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.
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