CT Imaging Features of Different Clinical Types of COVID-19: A Chinese Multicenter Study

Hu, Xiaofei; Zeng, Wenbing; Zhang, Yuhan; Zhen, Zhiming; Cheng, Lin; Wang, Xianqi; Luo, Haoran; Liu, Chen; Zhang, Shu; Wu, Zifeng; Sun, Zeyu; Li, Xiuli; Cao, Yang; Xu, Mengyi; Wang, Jian; Chen, Wei

doi:10.2139/ssrn.3550043

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model identified patients with mild disease at risk for deterioration with 79.2% accuracy, with deep learning outperforming the logistic regression model (27). Among 164 patients with confirmed COVID-19, where patients were divided into mild and severe groups based on clinical criteria, an AI pneumonia detection system found that severe cases were more likely to be associated with older age, round lesions, fan-shaped lesions, crazy paving, fibrosis, "white lung," pleural thickening, and enlarged mediastinal lymph nodes (28).…”

Section: Can Trajectory Of Early Imaging Changes Predict Hospital Course?mentioning

confidence: 99%

How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries?

Kundu

Elhalawani

Gichoya

et al. 2020

Radiology: Artificial Intelligence

View full text Add to dashboard Cite

Section: Can Trajectory Of Early Imaging Changes Predict Hospital Course?mentioning

confidence: 99%

How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries?

Kundu

Elhalawani

Gichoya

et al. 2020

Radiology: Artificial Intelligence

View full text Add to dashboard Cite

COVID-19 clinico-radiological mismatch: a proposal for a novel combined morphologic/volumetric CT severity score with blinded validation

Samir

Elnekeidy

Gharraf

et al. 2021

Egypt J Radiol Nucl Med

View full text Add to dashboard Cite

Background Some COVID-19 patients with similar quantitative CT measurements had variable clinical presentation and outcome. The absence of reasonable clinical explanations, such as pre-existing comorbidities or vascular complications, adds to the confusion. The authors believed that neglecting the impact of certain severe morphologic features could be an alternative radiological explanation. This study aims to optimize the initial CT staging of COVID-19 and propose a new combined morphologic/volumetric CT severity index (CTSI) to solve this clinico-radiological mismatch. Results This multi-center study included two major steps. The first step of the study entailed a standardized combined morphologic/volumetric CT severity analyses to propose a new optimized CTSI. This was conducted retrospectively during the period from June till September 2020. It included 379 acutely symptomatic COVID-19 patients. They were clinically classified according to their oxygen saturation and respiratory therapeutic requirements into three groups: group A (mild 298/79%), group B (borderline severity 57/15%), and group C (severe/critical 24/6%). The morphologic and volumetric assessment of their HRCT was analyzed according to severity, by two consultant radiologists in consensus. A new 25 point-CTSI has been created, combining eight morphological CT patterns [M1:M8; 8 points] and four grades of volumetric scores [S1:S4; 17 points]. The addition of the M5 pattern (air bubble sign), M6 pattern (early fibrosis and architectural distortion), or M7 pattern (crazy-paving) proved to increase the clinical severity. The second step of the study entailed a standardized blinded/independent validation analysis for the proposed CTSI. This was prospectively conducted on other 132 patients during October 2020 and independently performed by other two consultant radiologists. Validation results reached 80.2% sensitivity, 91.8% specificity, AUROC-curve = 0.8356, and 90.9% accuracy. Conclusion A new optimized CTSI with accepted validation is proposed for initial staging of COVID-19 patients, using combined morphologic/volumetric assessment instead of the quantitative assessment alone. It could solve the clinico-radiological mismatch among patients with similar quantitative CT results and variable clinical presentation during the absence of pre-existing comorbidities or vascular complications.

show abstract

CT Imaging Features of Different Clinical Types of COVID-19: A Chinese Multicenter Study

Cited by 2 publications

References 0 publications

How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries?

How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries?

COVID-19 clinico-radiological mismatch: a proposal for a novel combined morphologic/volumetric CT severity score with blinded validation

Contact Info

Product

Resources

About