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Updated: Nov 8, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency
Paul Schuster1,2, Amandine Crombé1,3, Hubert Nivet1,2
1Imadis Teleradiology, 48 Rue Quivogne, 69002, Lyon, France.
Insights
Simple clinical and radiological models accurately diagnose Coronavirus disease 2019 (COVID-19) using chest CT scans. These models, including logistic regression and classification trees, aid radiologists in diagnosing COVID-19, improving patient care.
Area of Science:
- Medical Imaging
- Radiology
- Infectious Disease Diagnostics
Background:
- Coronavirus disease 2019 (COVID-19) diagnosis relies on clinical, radiological, and laboratory findings.
- Accurate and timely diagnosis is crucial for patient management and public health.
- Developing practical diagnostic models can support emergency department workflows.
Purpose of the Study:
- To develop and validate simple clinical and radiological models for diagnosing COVID-19.
- To compare the performance of these models against on-call teleradiologist interpretations.
- To assess the utility of these models in a real-life emergency cohort.
Main Methods:
- Development of multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models.
- Training and validation on a cohort of 513 adult patients with suspected COVID-19.
- Utilizing clinical data and chest CT scan features (e.g., ground-glass opacities, distribution patterns).
- Comparison of model performance using Area Under the Receiver Operating Characteristics Curves (AUC) against teleradiologist assessments.
Main Results:
- The CART model using radiological variables alone achieved an AUC of 0.92.
- The Step-LR model incorporating clinical-radiological variables yielded the highest AUC of 0.93.
- Both models demonstrated high sensitivity and specificity in predicting positive RT-PCR results, outperforming teleradiologists in some metrics.
Conclusions:
- Simple clinical and radiological models demonstrate high performance in diagnosing COVID-19.
- These models can effectively support radiologists in decision-making for suspected COVID-19 cases.
- The models offer practical tools for real-world emergency settings, aiding in the diagnosis of positive RT-PCR status.
Abstract:
Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-03-13 to 2020-04-14 were included as long as chest CT-scans and real-time polymerase chain reaction (RT-PCR) results were available (244 [47.6%] with a positive RT-PCR). Immediately after their acquisition, the chest CTs were prospectively interpreted by on-call teleradiologists (OCTRs) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions' semiology, distribution, extent and differential diagnoses. After pre-filtering clinical and radiological features through univariate Chi-2, Fisher or Student t-tests (as appropriate), multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively) through area under the receiver operating characteristics curves (AUC). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC = 0.92 [versus 0.88 for OCTR], sensitivity = 0.77, specificity = 0.94) while step-LR provided the highest AUC with clinical-radiological variables (AUC = 0.93 [versus 0.86 for OCTR], sensitivity = 0.82, specificity = 0.91). Hence, these two simple models, depending on the availability of clinical data, provided high performances to diagnose positive RT-PCR and could be used by any radiologist to support, modulate and communicate their conclusion in case of COVID-19 suspicion. Practically, using clinical and radiological variables (GGO, fever, presence of fibrotic bands, presence of diffuse lesions, predominant peripheral distribution) can accurately predict RT-PCR status.
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