A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias?

Abstract

Purpose To evaluate the discrimination of parenchymal lesions between COVID-19 and other atypical pneumonia (AP) by using only radiomics features. Methods In this retrospective study, 301 pneumonic lesions (150 ground-glass opacity [GGO], 52 crazy paving [CP], 99 consolidation) obtained from nonenhanced thorax CT scans of 74 AP (46 male and 28 female; 48.25±13.67 years) and 60 COVID-19 (39 male and 21 female; 48.01±20.38 years) patients were segmented manually by two independent radiologists, and Location, Size, Shape, and First- and Second-order radiomics features were calculated. Results Multiple parameters showed significant differences between AP and COVID-19-related GGOs and consolidations, although only the Range parameter was significantly different for CPs. Models developed by using the Bayesian information criterion (BIC) for the whole group of GGO and consolidation lesions predicted COVID-19 consolidation and AP GGO lesions with low accuracy (46.1% and 60.8%, respectively). Thus, instead of subjective classification, lesions were reclassified according to their skewness into positive skewness group (PSG, 78 AP and 71 COVID-19 lesions) and negative skewness group (NSG, 56 AP and 44 COVID-19 lesions), and group-specific models were created. The best AUC, accuracy, sensitivity, and specificity were respectively 0.774, 75.8%, 74.6%, and 76.9% among the PSG models and 0.907, 83%, 79.5%, and 85.7% for the NSG models. The best PSG model was also better at predicting NSG lesions smaller than 3 mL. Using an algorithm, 80% of COVID-19 and 81.1% of AP patients were correctly predicted. Conclusion During periods of increasing AP, radiomics parameters may provide valuable data for the differential diagnosis of COVID-19.

Artificial intelligence-driven assessment of radiological images for COVID-19

“…The model showed that a combination of radiomics and a basic inflammatory index obtained at admission can predict ICU admission. According to Gülbay et al [ 130 ] COVID-19 and atypical pneumonia-associated GGO lesions and consolidation could be predicted with high accuracy (80% in COVID-19 and 81% in atypical pneumonia). Roundness and peripheral location were found to be the most effective characteristics for identifying a GGO lesion with COVID-19, but were both ineffective in predicting lesions in the consolidation stage.…”

Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease

“…Radiomics features can be further integrated into machine learning models with the aim to improve diagnosis and patient management. This approach was recently investigated to improve the detection and the differential diagnosis of COVID-19 pneumonia 20 , 21 , 23 , 24 , 26 – 29 . For example, Zhang et al proposed a CT-based deep learning integrated radiomics model for the differentiation of COVID-19 pneumonia from other community acquired pneumonias.…”

COVID-index: A texture-based approach to classifying lung lesions based on CT images

“…Pleural effusion may also occur in cases of COVID-19, but is less common than the other lesions. It is therefore important to point out some difficulties with this approach, as follows: Although the features of COVID-19 are found in most cases, CT images of some viral pneumonias also show these features, which can ultimately make diagnosis more difficult [2] ; In some cases of COVID-19, biopsies are needed [3] ; Correct classification is required between healthy and diseased regions, especially those with COVID-19 and other more serious diseases such as lung nodules; and According to [4] , [5] , COVID-19 regions are generally more rounded in shape. …”

“…According to [4] , [5] , COVID-19 regions are generally more rounded in shape.…”

“…Through correct classification, our method can provide the individual details of the lesions, which can help the specialist in making decisions regarding the need for biopsies. Furthermore, based on the work in [4] , [5] , we believe that the techniques used in our method of texture characterization can provide more meaningful information for lesion classification, since the shape of the lesion is not considered in the analysis.…”