CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients

Cai, Wenli; Liu, Tianyu; Xue, Xing; Luo, Guibo; Wang, Xiaoli; Shen, Yihong; Fang, Qiang; Sheng, Jifang; Chen, Feng; Liang, Tingbo

OBJECTIVE: This study was to investigate the CT quantification of COVID-19 pneumonia and its impacts on the assessment of disease severity and the prediction of clinical outcomes in the management of COVID-19 patients. MATERIALS AND METHODS: Ninety-nine COVID-19 patients who were confirmed by positive nucleic acid test (NAT) of RT-PCR and hospitalized from January 19, 2020 to February 19, 2020 were collected for this retrospective study. All patients underwent arterial blood gas test, routine blood test, chest CT examination, and physical examination on admission. In addition, fellow-up clinical data including the disease severity, clinical treatment, and clinical outcomes were collected for each patient. Lung volume, lesion volume, non-lesion lung volume (NLLV) (lung volume – lesion volume), and fraction of non-lesion lung volume (%NLLV) (non-lesion lung volume / lung volume) were quantified in CT images by using two U-Net models trained for segmentation of lung and COVID-19 lesions in CT images. Furthermore, we calculated 20 histogram textures for lesions volume and non-lesion lung volumes, respectively. To investigate the validity of CT quantification in the management of COVID-19, we built Random Forest (RF) models for the purpose of classification and regression to assess the disease severity (Moderate, Severe, and Critical) and to predict the need and length of ICU stay, the duration of oxygen inhalation, hospitalization, sputum NAT-positive, and patient prognosis. The performance of RF classifiers was evaluated using the area under the receiver operating characteristic (ROC) curves (AUC) and that of RF regressors using the root-mean-square error (RMSE). RESULTS: Patients were classified into three groups of disease severity: moderate (n=25), severe (n=47) and critical (n=27), according to the clinical staging. Of which, a total of 32 patients, 1 (1/25) moderate, 6 (6/47) severe and 25 critical (25/27), respectively, were admitted to ICU. The median values of ICU stay were 0, 0, 12 days, the duration of oxygen inhalation 10, 15, and 28 days, the hospitalization 12, 16, and 28 days, and the sputum NAT-positive 8, 9, 13 days, in three severity groups, respectively. The clinical outcomes were complete recovery (n=3), partial recovery with residual pulmonary damage (n=80), prolonged recovery (n=15), and death (n=1). The %NLLV in three severity groups were 92.18±9.89%, 82.94±16.49%, and 66.19±24.15% with p-value <0.05 among each two groups. The AUCs of RF classifiers were 0.927 and 0.929 in classification of moderate vs (severe + critical), and severe vs critical, respectively, which were significantly higher than both radiomics models and clinical models (p<0.05). The RMSEs of RF regressors were 0.88 weeks for prediction of duration of hospitalization (mean: 2.60 ± 1.01 weeks), 0.92 weeks for duration of oxygen inhalation (mean: 2.44 ± 1.08 weeks), 0.90 weeks for duration of sputum NAT-positive (mean: 1.59 ± 0.98 weeks), and 0.69 weeks for stay of ICU (mean: 1.32 ± 0.67 weeks), respectively. The AUCs for prediction of ICU treatment and prognosis (partial recovery vs prolonged recovery) were 0.945 and 0.960, respectively. CONCLUSION: CT quantification and machine-learning models shows great potentials for assisting decision-making in the management of COVID-19 patients by assessing disease severity and predicting clinical outcomes.