tetano
Editor, Senior Moderator
Eur Radiol Exp
. 2023 Jan 24;7(1):3.
doi: 10.1186/s41747-022-00317-6.
Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches
Giulia Zorzi[SUP] 1 2 3 [/SUP], Luca Berta[SUP] 4 [/SUP], Francesco Rizzetto[SUP] 5 6 [/SUP], Cristina De Mattia[SUP] 2 [/SUP], Marco Maria Jacopo Felisi[SUP] 2 [/SUP], Stefano Carrazza[SUP] 3 7 [/SUP], Silvia Nerini Molteni[SUP] 8 [/SUP], Chiara Vismara[SUP] 8 [/SUP], Francesco Scaglione[SUP] 8 9 [/SUP], Angelo Vanzulli[SUP] 10 9 [/SUP], Alberto Torresin[SUP] 2 3 7 [/SUP], Paola Enrica Colombo[SUP] 2 7 [/SUP]
Affiliations
Abstract
Background: To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19).
Methods: Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L).
Results: The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4.
Conclusions: Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.
Keywords: Artificial intelligence; COVID-19; Lung; Radiomics; Tomography (x-ray; computed).
. 2023 Jan 24;7(1):3.
doi: 10.1186/s41747-022-00317-6.
Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches
Giulia Zorzi[SUP] 1 2 3 [/SUP], Luca Berta[SUP] 4 [/SUP], Francesco Rizzetto[SUP] 5 6 [/SUP], Cristina De Mattia[SUP] 2 [/SUP], Marco Maria Jacopo Felisi[SUP] 2 [/SUP], Stefano Carrazza[SUP] 3 7 [/SUP], Silvia Nerini Molteni[SUP] 8 [/SUP], Chiara Vismara[SUP] 8 [/SUP], Francesco Scaglione[SUP] 8 9 [/SUP], Angelo Vanzulli[SUP] 10 9 [/SUP], Alberto Torresin[SUP] 2 3 7 [/SUP], Paola Enrica Colombo[SUP] 2 7 [/SUP]
Affiliations
- PMID: 36690869
- DOI: 10.1186/s41747-022-00317-6
Abstract
Background: To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19).
Methods: Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L).
Results: The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4.
Conclusions: Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.
Keywords: Artificial intelligence; COVID-19; Lung; Radiomics; Tomography (x-ray; computed).