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Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia.
J Comput Assist Tomogr. 2020 Sep/Oct; 44(5):640-646.JC

Abstract

PURPOSE

This study aimed to assess if computed tomography (CT) radiomics can predict the severity and outcome of patients with coronavirus disease 2019 (COVID-19) pneumonia.

METHODS

This institutional ethical board-approved study included 92 patients (mean age, 59 ± 17 years; 57 men, 35 women) with positive reverse transcription polymerase chain reaction assay for COVID-19 infection who underwent noncontrast chest CT. Two radiologists evaluated all chest CT examinations and recorded opacity type, distribution, and extent of lobar involvement. Information on symptom duration before hospital admission, the period of hospital admission, presence of comorbid conditions, laboratory data, and outcomes (recovery or death) was obtained from the medical records. The entire lung volume was segmented on thin-section Digital Imaging and Communication in Medicine images to derive whole-lung radiomics. Data were analyzed using multiple logistic regression with receiver operator characteristic area under the curve (AUC) as the output.

RESULTS

Computed tomography radiomics (AUC, 0.99) outperformed clinical variables (AUC, 0.89) for prediction of the extent of pulmonary opacities related to COVID-19 pneumonia. Type of pulmonary opacities could be predicted with CT radiomics (AUC, 0.77) but not with clinical or laboratory data (AUC, <0.56; P > 0.05). Prediction of patient outcome with radiomics (AUC, 0.85) improved to an AUC of 0.90 with the addition of clinical variables (patient age and duration of presenting symptoms before admission). Among clinical variables, the combination of peripheral capillary oxygen saturation on hospital admission, duration of symptoms, platelet counts, and patient age provided an AUC of 0.81 for predicting patient outcomes.

CONCLUSIONS

Radiomics from noncontrast CT reliably predict disease severity (AUC, 0.99) and outcome (AUC, 0.85) in patients with COVID-19 pneumonia.

Authors+Show Affiliations

From the Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA.Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran.Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran.From the Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA.Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran.Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran.From the Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA.From the Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA.From the Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32842058

Citation

Homayounieh, Fatemeh, et al. "Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia." Journal of Computer Assisted Tomography, vol. 44, no. 5, 2020, pp. 640-646.
Homayounieh F, Babaei R, Karimi Mobin H, et al. Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia. J Comput Assist Tomogr. 2020;44(5):640-646.
Homayounieh, F., Babaei, R., Karimi Mobin, H., Arru, C. D., Sharifian, M., Mohseni, I., Zhang, E., Digumarthy, S. R., & Kalra, M. K. (2020). Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia. Journal of Computer Assisted Tomography, 44(5), 640-646. https://doi.org/10.1097/RCT.0000000000001094
Homayounieh F, et al. Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia. J Comput Assist Tomogr. 2020 Sep/Oct;44(5):640-646. PubMed PMID: 32842058.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia. AU - Homayounieh,Fatemeh, AU - Babaei,Rosa, AU - Karimi Mobin,Hadi, AU - Arru,Chiara D, AU - Sharifian,Maedeh, AU - Mohseni,Iman, AU - Zhang,Eric, AU - Digumarthy,Subba R, AU - Kalra,Mannudeep K, PY - 2020/8/26/pubmed PY - 2020/10/2/medline PY - 2020/8/26/entrez SP - 640 EP - 646 JF - Journal of computer assisted tomography JO - J Comput Assist Tomogr VL - 44 IS - 5 N2 - PURPOSE: This study aimed to assess if computed tomography (CT) radiomics can predict the severity and outcome of patients with coronavirus disease 2019 (COVID-19) pneumonia. METHODS: This institutional ethical board-approved study included 92 patients (mean age, 59 ± 17 years; 57 men, 35 women) with positive reverse transcription polymerase chain reaction assay for COVID-19 infection who underwent noncontrast chest CT. Two radiologists evaluated all chest CT examinations and recorded opacity type, distribution, and extent of lobar involvement. Information on symptom duration before hospital admission, the period of hospital admission, presence of comorbid conditions, laboratory data, and outcomes (recovery or death) was obtained from the medical records. The entire lung volume was segmented on thin-section Digital Imaging and Communication in Medicine images to derive whole-lung radiomics. Data were analyzed using multiple logistic regression with receiver operator characteristic area under the curve (AUC) as the output. RESULTS: Computed tomography radiomics (AUC, 0.99) outperformed clinical variables (AUC, 0.89) for prediction of the extent of pulmonary opacities related to COVID-19 pneumonia. Type of pulmonary opacities could be predicted with CT radiomics (AUC, 0.77) but not with clinical or laboratory data (AUC, <0.56; P > 0.05). Prediction of patient outcome with radiomics (AUC, 0.85) improved to an AUC of 0.90 with the addition of clinical variables (patient age and duration of presenting symptoms before admission). Among clinical variables, the combination of peripheral capillary oxygen saturation on hospital admission, duration of symptoms, platelet counts, and patient age provided an AUC of 0.81 for predicting patient outcomes. CONCLUSIONS: Radiomics from noncontrast CT reliably predict disease severity (AUC, 0.99) and outcome (AUC, 0.85) in patients with COVID-19 pneumonia. SN - 1532-3145 UR - https://www.unboundmedicine.com/medline/citation/32842058/Computed_Tomography_Radiomics_Can_Predict_Disease_Severity_and_Outcome_in_Coronavirus_Disease_2019_Pneumonia_ L2 - https://doi.org/10.1097/RCT.0000000000001094 DB - PRIME DP - Unbound Medicine ER -