Automatic CT Quantification of Coronavirus Disease 2019 pneumonia: An international collaborative development, validation, and clinical implication

Objectives: We aimed to develop and validate the automatic quantification of COVID-19 pneumonia on CT images. Methods: This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 13 Korean and Chinese institutions. Two experienced radiologists semi-automatically drew pneumonia, preparing 49,830 positive and negative CT slices to develop the 2D U-Net for segmenting pneumonia. The 2D U-Net was distributed as downloadable software. External validation for quantifications’ accuracy was performed using Japanese, Italian, Radiopaedia, Chinese datasets. Primary measures for the accuracy of the network were correlation coefficients for extent (%) and weight (g) of pneumonia. Logistic regression analyses were performed to evaluate the clinical implication of the extent and weight regarding the presence of symptoms in the Japanese dataset and the occurrence of composite outcome in the Spanish dataset. Results: In the internal validation dataset, the intraclass correlation coefficients between the 2D U-Net and reference values for the extent and weight were 0.990 and 0.993, respectively. In the Japanese dataset, the Pearson correlation coefficients between the U-Net outcomes and visual CT severity scores were 0.908 and 0.899, respectively. In the other external validation datasets, the intraclass correlation coefficients between the U-Net and reference values were between 0.951-0.970 (extent) and between 0.970-0.995 (weight), respectively. In multivariate logistic regression analyses, the extent and weight of pneumonia were independently associated with symptoms (OR, 4.142 and 4.434; p=.013 and .009, respectively), and poor prognosis (OR, 7.446 and 4.677; p=.004 and .029, respectively). Conclusions: CT extent and weight of COVID-19 pneumonia were automatically quantifiable and independently associated with symptoms and prognosis.