Multi-View Whole Lung Radiomics Quantitative Analysis of COVID-19 Pneumonia Based on Machine Learning

Abstract Background: Quantitative and radiomics imaging could realize non-invasive disease diagnosis. This study aimed to evaluate radiomics features of the whole lung for predicting new coronavirus disease 2019(COVID-19) from different views, and to investigate new radiomics features. 75 patients were retrospectively enrolled from December 1, 2019 to December 31, 2020. Both lungs were segmented by an unsupervised hybrid image segmentation approach. Radiomics features of the transverse plane, coronal plane and sagittal plane were separately extracted. After utilizing least absolute shrinkage and selection operator (LASSO), three radiomics models based on key radiomics features were built by machine learning. Meanwhile, the different categories radiomics models were constructed by the particle swarm optimization-deep extreme learning machine(PSO-DELM). Predictive accuracy, sensitivity, specificity and areas under receiver operating characteristic curve (AUC) were evaluated performances of these radiomics models.Results: Training and test cohorts had similar distributions of age and pneumonia type. 13 (transverse plane), 4 (coronal plane) and 8 (sagittal plane) selected features were constructed radiomics models in training cohort. Radiomics models based on PSO-DELM in the transverse plane, coronal plane and sagittal plane showed the favorable performance in the testing cohort(AUC=0.9444, 0.8636 and 0.9444, respectively). The phase congruency feature showed the stable predictive performance (AUC>0.9) among these radiomics features on the three different plane.Conclusions: Multi-view whole lung radiomics features could effectively differentiate COVID-19 from other types of pneumonia. Phase congruency may be attempted as a radiomics biomarker for the identification of pulmonary diseases. Merging the radiomics features into PSO-DELM is a promising direction for future research about medical radiology and deep learning.