Radiologic Risk Factors for Mortality of Patients with COVID-19 Pneumonia in Wuhan, China: A Retrospective Study (preprint)

Background: Computed tomography (CT) characteristics associated with critical outcomes of patients with coronavirus disease 2019 (COVID-19) have been reported. However, CT risk factors for mortality are poorly understood. We aimed to investigate the automatically quantified CT imaging predictors for COVID-19 mortality.Methods: In this retrospective study, laboratory-confirmed COVID-19 patients at Wuhan Central Hospital between December 9, 2019, and March 19, 2020, were included. A novel prognostic biomarker, V-HU score, depicting the volume of total pneumonia infection and the average Hounsfield unit (HU) value of consolidation areas was quantified from CT by an artificial intelligence (AI) system. Cox proportional hazards models were used to investigate risk factors for mortality.Findings: This study included 238 patients (126 survivors and 112 non-survivors). The V-HU marker was an independent predictor (hazard ratio [HR] 2·78, 95% CI 1·50-5·17; p=0·0012) after adjusting for several COVID-19 prognostic indicators significant in univariable analysis. The prognostic performance of the model containing clinical and outpatient laboratory factors was improved by integrating the V-HU marker (c-index: 0·695 versus 0·728; p<0·0001). Older patients (age>=65 years; HR 3·56, 95% CI 1·64-7·71; p=0·0006) and younger patients (age<65 years; HR 4·60, 95% CI 1·92-10·99; p<0·0001) could be risk-stratified by the V-HU marker.Interpretation: A combination of an increased volume of total pneumonia infection and high HU value of consolidation areas showed a strong correlation to COVID-19 mortality, as determined by AI quantified CT. The novel radiologic marker may be used for early risk assessment to prioritize critical care resources for patients at a high risk of mortality.Funding: None.Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: The study was approved by the Research Ethics Commission of Wuhan Central Hospital, and the requirement for writing informed consent was waived by the Ethics Commission for the emergence of infectious diseases.

From Community Acquired Pneumonia to COVID-19: A Deep Learning Based Method for Quantitative Analysis of COVID-19 on thick-section CT Scans (preprint)

Background: Thick-section CT scanners are more affordable for the developing countries. Considering the widely spread COVID-19, it is of great benefit to develop an automated and accurate system for quantification of COVID-19 associated lung abnormalities using thick-section chest CT images. Purpose: To develop a fully automated AI system to quantitatively assess the disease severity and disease progression using thick-section chest CT images. Materials and Methods: In this retrospective study, a deep learning based system was developed to automatically segment and quantify the COVID-19 infected lung regions on thick-section chest CT images. 531 thick-section CT scans from 204 patients diagnosed with COVID-19 were collected from one appointed COVID-19 hospital from 23 January 2020 to 12 February 2020. The lung abnormalities were first segmented by a deep learning model. To assess the disease severity (non-severe or severe) and the progression, two imaging bio-markers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU). The performance of lung abnormality segmentation was examined using Dice coefficient, while the assessment of disease severity and the disease progression were evaluated using the area under the receiver operating characteristic curve (AUC) and the Cohen's kappa statistic, respectively. Results: Dice coefficient between the segmentation of the AI system and the manual delineations of two experienced radiologists for the COVID-19 infected lung abnormalities were 0.74 {+/-} 0.28 and 0.76 {+/-} 0.29, respectively, which were close to the inter-observer agreement, i.e., 0.79 {+/-} 0.25. The computed two imaging bio-markers can distinguish between the severe and non-severe stages with an AUC of 0.9680 (p-value < 0.001). Very good agreement ({kappa} = 0.8220) between the AI system and the radiologists were achieved on evaluating the changes of infection volumes. Conclusions: A deep learning based AI system built on the thick-section CT imaging can accurately quantify the COVID-19 associated lung abnormalities, assess the disease severity and its progressions.