See the published version of this article at Journal of Translational Medicine.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS
Dengbin Wang
Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9529-4870
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Translational Medicine.
Background
Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model.
Methods
This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneunomia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists with CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA).
Results
Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model.
Conclusions
The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.
Keywords
COVID-19, Computed Tomography, Pneumonia, Radiomics, Machine learning
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This is a list of supplementary files associated with this preprint. Click to download.
- OnlineFigureS1.Png
Radiomics feature selection using LASSO logistic regression model. (a) The hyper parameter (λ) was selected via ten-fold cross-validation based on minimum criteria. Log (λ) is plotted on the x-axis, and binomial deviance is plotted on the y-axis. The dotted vertical lines indicate optimal values determined using the minimum criteria and one standard error of the minimum criteria (1-SE). log (λ)=-6.71. (b) LASSO coefficient profiles of the radiomics features. Coefficient profiles are plotted against log (λ). The optimal 8 non-zero coefficients were generated at the value selected using ten-fold cross-validation.
- OnlineFigureS2.Png
The selected radiomics features and their coefficients.
- OnlineFigureS3.Png
Calibration curves of the clinical and combined radiomics model in the training cohort (a) and validation cohort (b).
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 22 Sep, 2020
Published
On 07 Jan, 2021
No community comments so far
Review #1 received
Received 05 Dec, 2020
Editorial decision: Major revision
On 05 Dec, 2020
Reviewer #2 agreed
On 30 Nov, 2020
Review #2 received
Received 30 Nov, 2020
Reviewer #1 agreed
On 12 Nov, 2020
Reviewers invited
Invitations sent on 27 Sep, 2020
Editor assigned
On 25 Sep, 2020
Editor invited
On 24 Sep, 2020
Submission checks complete
On 16 Sep, 2020
First submitted
On 12 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Critical Care & Emergency Medicine
Learn more about our company.
This preprint is under consideration at Journal of Translational Medicine. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS
Dengbin Wang
Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9529-4870
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 22 Sep, 2020
Published
On 07 Jan, 2021
No community comments so far
Review #1 received
Received 05 Dec, 2020
Editorial decision: Major revision
On 05 Dec, 2020
Reviewer #2 agreed
On 30 Nov, 2020
Review #2 received
Received 30 Nov, 2020
Reviewer #1 agreed
On 12 Nov, 2020
Reviewers invited
Invitations sent on 27 Sep, 2020
Editor assigned
On 25 Sep, 2020
Editor invited
On 24 Sep, 2020
Submission checks complete
On 16 Sep, 2020
First submitted
On 12 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Critical Care & Emergency Medicine
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Translational Medicine.
Background
Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model.
Methods
This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneunomia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists with CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA).
Results
Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model.
Conclusions
The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6