A framework for quantitative analysis of Computed Tomography images of viral pneumonitis: radiomic features in COVID and non-COVID patients
This work addresses the need for automated diagnostic tools in radiology during the COVID-19 pandemic, but it is incremental as it applies existing methods (e.g., Gaussian models, MLP classifiers) to a specific medical imaging task.
The researchers tackled the problem of classifying COVID-19 versus non-COVID viral pneumonia from chest CT images by developing a pipeline for extracting quantitative metrics and radiomic features, resulting in diagnostic models with accuracies ranging from 0.76 to 0.87, with the best model achieving 0.81±0.02 accuracy using 21 radiomic features.
Purpose: to optimize a pipeline of clinical data gathering and CT images processing implemented during the COVID-19 pandemic crisis and to develop artificial intelligence model for different of viral pneumonia. Methods: 1028 chest CT image of patients with positive swab were segmented automatically for lung extraction. A Gaussian model developed in Python language was applied to calculate quantitative metrics (QM) describing well-aerated and ill portions of the lungs from the histogram distribution of lung CT numbers in both lungs of each image and in four geometrical subdivision. Furthermore, radiomic features (RF) of first and second order were extracted from bilateral lungs using PyRadiomic tools. QM and RF were used to develop 4 different Multi-Layer Perceptron (MLP) classifier to discriminate images of patients with COVID (n=646) and non-COVID (n=382) viral pneumonia. Results: The Gaussian model applied to lung CT histogram correctly described healthy parenchyma 94% of the patients. The resulting accuracy of the models for COVID diagnosis were in the range 0.76-0.87, as the integral of the receiver operating curve. The best diagnostic performances were associated to the model based on RF of first and second order, with 21 relevant features after LASSO regression and an accuracy of 0.81$\pm$0.02 after 4-fold cross validation Conclusions: Despite these results were obtained with CT images from a single center, a platform for extracting useful quantitative metrics from CT images was developed and optimized. Four artificial intelligence-based models for classifying patients with COVID and non-COVID viral pneumonia were developed and compared showing overall good diagnostic performances