In patients recovering from COVID-19 infection, four stages of evolution on chest CT were identified: early stage (0-4 days); progressive stage (5-8 days); peak stage (10-13 days); and absorption stage (≥14 days). Key Results1. In patients who recovered from COVID-19 pneumonia, initial lung findings on chest CT were small subpleural ground glass opacities (GGO) that grew larger with crazy-paving pattern and consolidation.2. Lung involvement increased to consolidation up to two weeks after disease onset.3. After two weeks, the lesions were gradually absorbed leaving extensive GGO and subpleural parenchymal bands.This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s Abstract:Background: Chest CT is used to assess the severity of lung involvement in COVID-19 pneumonia. Purpose:To determine the change in chest CT findings associated with COVID-19 pneumonia from initial diagnosis until patient recovery. Materials and Methods:This retrospective review included patients with RT-PCR confirmed COVID-19 infection presenting between 12 January 2020 to 6 February 2020. Patients with severe respiratory distress and/ or oxygen requirement at any time during the disease course were excluded.Repeat Chest CT was obtained at approximately 4 day intervals. The total CT score was the sum of lung involvement (5 lobes, score 1-5 for each lobe, range, 0 none, 25 maximum) was determined.Results: Twenty one patients (6 males and 15 females, age 25-63 years) with confirmed COVID-19 pneumonia were evaluated. These patients underwent a total of 82 pulmonary CT scans with a mean interval of 4±1 days (range: 1-8 days). All patients were discharged after a mean hospitalized period of 17±4 days (range: 11-26 days). Maximum lung involved peaked at approximately 10 days (with the calculated total CT score of 6) from the onset of initial symptoms (R2=0.25), p<0.001). Based on quartiles of patients from day 0 to day 26 involvement, 4 stages of lung CT were defined: Stage 1 (0-4 days): ground glass opacities (GGO) in 18/24 (75%) patients with the total CT score of 2±2; (2) Stage-2 (5-8d days): increased crazy-paving pattern 9/17 patients (53%) with a increase in total CT score (6±4, p=0.002); (3) Stage-3 (9-13days): consolidation 19/21 (91%) patients with the peak of total CT score (7±4) ; (4) Stage-4 (≥14 days): gradual resolution of consolidation 15/20 (75%) patients with a decreased total CT score (6±4) without crazy-paving pattern. Conclusion:In patients recovering from COVID-19 pneumonia (without severe respiratory distress during the disease course), lung abnormalities on chest CT showed greatest severity approximately 10 days after initial onset of symptoms.

Rationale: Up to date, the exploration of clinical features in severe COVID-19 patients were mostly from the same center in Wuhan, China. The clinical data in other centers is limited. This study aims to explore the feasible parameters which could be used in clinical practice to predict the prognosis in hospitalized patients with severe coronavirus disease-19 . Methods: In this case-control study, patients with severe COVID-19 in this newly established isolation center on admission between 27 January 2020 to 19 March 2020 were divided to discharge group and death event group. Clinical information was collected and analyzed for the following objectives: 1. Comparisons of basic characteristics between two groups; 2. Risk factors for death on admission using logistic regression; 3. Dynamic changes of radiographic and laboratory parameters between two groups in the course. Results: 124 patients with severe COVID-19 on admission were included and divided into discharge group (n=35) and death event group (n=89). Sex, SpO2, breath rate, diastolic pressure, neutrophil, lymphocyte, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), and D-dimer were significantly correlated with death events identified using bivariate logistic regression. Further multivariate logistic regression demonstrated a significant model fitting with C-index of 0.845 (p<0.001), in which SpO2≤89%, lymphocyte≤0.64×10 9 /L, CRP>77.35mg/L, PCT>0.20μg/L, and LDH>481U/L were the independent risk factors with the ORs of 2. 959, 4.015, 2.852, 3.554, and 3.185, respectively (p<0.04). In the course, persistently lower lymphocyte with higher levels of CRP, PCT, IL-6, neutrophil, LDH, D-dimer, cardiac troponin I (cTnI), brain natriuretic peptide (BNP), and increased CD4+/CD8+ T-lymphocyte ratio and were observed in death events group, while these parameters stayed stable or improved in discharge group. Conclusions: On admission, the levels of SpO2, lymphocyte, CRP, PCT, and LDH could predict the prognosis of severe COVID-19 patients. Systematic inflammation with induced cardiac dysfunction was likely a primary reason for death events in severe COVID-19 except for acute respiratory distress syndrome.

Background The chest CT manifestations of COVID-19 from hospitalization to convalescence after one year are not known. Purpose To assess chest CT manifestations of COVID-19 up to 1 year after symptom onset. Materials and Methods Patients were enrolled if they were admitted to the hospital due to COVID-19 and underwent CT scans during hospitalization at two isolation centers between 27 January and 31 March 2020. In a prospective study, three serial chest CTs were obtained at approximately 3, 7, and 12 months after symptom onset and longitudinally analyzed. The total CT score of pulmonary lobe involvement from 0 to 25 was assessed (score 1-5 for each lobe). Uni-/multi-variable logistic regression tests were performed to explore independent risk factors for residual CT abnormalities after one year. Results 209 study participants (mean age: 49±13 years, 116 women) were evaluated. At 3 months, 61% of participants (128 of 209) had resolution of CT abnormalities; at 12 months, 75% (156 of 209) had resolution. Of chest CT abnormalities that had not resolved, there were residual linear opacities in 25/209 (12%) and multifocal reticular/cystic lesions in 28/209 (13%) participants. Age≥50 years, lymphopenia, and severe/ARDS aggravation were independent risk factors for residual CT abnormalities at one year (odds ratios of 15.9, 18.9, and 43.9, respectively; P <.001, each). In 53 participants with residual CT abnormalities at 12 months, reticular lesions (41 of 53, 77%) and bronchial dilation (39 of 53, 74%) were observed at discharge and were persistent in 53% (28 of 53) and 45% (24 of 53) of participants, respectively. Conclusion One year after COVID-19 diagnosis, chest CT showed abnormal findings in 25% of participants, with 13% showing subpleural reticular/cystic lesions. Older participants with severe COVID-19 or acute respiratory distress syndrome were more likely to develop lung sequelae that persisted at 1 year. See also the editorial by Lee and Wi .

Rationale:To retrospectively analyze serial chest CT and clinical features in patients with coronavirus disease 2019 for the assessment of temporal changes and to investigate how the changes differ in survivors and nonsurvivors. Methods: The consecutive records of 93 patients with confirmed COVID-19 who were admitted to Wuhan Union Hospital from January 10, 2020, to February 22, 2020, were retrospectively reviewed. A series of chest CT findings and clinical data were collected and analyzed. The serial chest CT scans were scored on a semiquantitative basis according to the extent of pulmonary abnormalities. Chest CT scores in different periods (0 -5 days, 6 -10 days, 11 -15 days, 16 -20 days, and > 20 days) since symptom onset were compared between survivors and nonsurvivors, and the temporal trend of the radiographic-clinical features was analyzed. Results: The final cohort consisted of 93 patients: 68 survivors and 25 nonsurvivors. Nonsurvivors were significantly older than survivors. For both survivors and nonsurvivors, the chest CT scores were not different in the first period (0 -5 days) but diverged afterwards. The mortality rate of COVID-19 monotonously increased with chest CT scores, which positively correlated with the neutrophil-to-lymphocyte ratio, neutrophil percentage, D-dimer level, lactate dehydrogenase level and erythrocyte sedimentation rate, while negatively correlated with the lymphocyte percentage and lymphocyte count. Conclusions: Chest CT scores correlate well with risk factors for mortality over periods, thus they may be used as a prognostic indicator in COVID-19. While higher chest CT scores are associated with a higher mortality rate, CT images taken at least 6 days since symptom onset may contain more prognostic information than images taken at an earlier period.

This study aimed to compare the chest computed tomography (CT) findings between survivors and non-survivors with Coronavirus Disease 2019 (COVID-19). Between 12 January 2020 and 20 February 2020, the records of 124 consecutive patients diagnosed with COVID-19 were retrospectively reviewed and divided into survivor (83/124) and non-survivor (41/124) groups. Chest CT findings were qualitatively compared on admission and serial chest CT scans were semi-quantitively evaluated between two groups using curve estimations. On admission, significantly more bilateral (97.6% vs. 73.5%, p = 0.001) and diffuse lesions (39.0% vs. 8.4%, p < 0.001) with higher total CT score (median 10 vs. 4, p < 0.001) were observed in non-survivor group compared with survivor group. Besides, crazypaving pattern was more predominant in non-survivor group than survivor group (39.0% vs. 12.0%, p < 0.001). From the prediction of curve estimation, in survivor group total CT score increased in the first 20 days reaching a peak of 6 points and then gradually decreased for more than other 40 days (R 2 = 0.545, p < 0.001). In non-survivor group, total CT score rapidly increased over 10 points in the first 10 days and gradually increased afterwards until ARDS occurred with following death events (R 2 = 0.711, p < 0.001). In conclusion, persistent progression with predominant crazy-paving pattern was the major manifestation of COVID-19 in non-survivors. Understanding this CT feature could help the clinical physician to predict the prognosis of the patients. Abbreviations COVID-19 Coronavirus Disease 2019 CT Computed tomography SARS-CoV2 Severe acute respiratory syndrome coronavirus 2 RT-PCR Real-time reverse transcription-polymerase chain reaction ARDS Acute respiratory distress syndrome GGO Ground-glass opacity IQR Inter-quartile range Since December 2019, an outbreak of coronavirus disease 2019 (COVID-19) has emerged in Wuhan, China 1,2. Subsequently, the disease has spread worldwide with a total infected population of more than 6.5 million reported on 5th June 2020 3. The pathogen was confirmed as a novel beta-coronavirus, which has demonstrated rapid human-to-human transmission with a median incubation period of 3 days 4,5. Recent data also suggest a higher transmission capability of this virus than the previously reported coronaviruses 3,6. The clinical characteristics and laboratory findings of COVID-19 patients have been reported including non-specific fever and cough symptoms and lymphopenia 2,4,7-9. Real-time reverse transcription-polymerase

Background: Immunotherapies targeting programmed cell death 1 (PD-1) and programmed death-ligand 1 (PD-L1) have been approved for gastric cancer (GC) patients. However, a large proportion of patients with T-cell-inflamed tumor microenvironment do not respond to the PD-1/PD-L1 blockade. The stromal component of the tumor microenvironment has been associated with immunotherapy. This study aims to explore the clinical significance of the nonimmune cells in the tumor microenvironment and their potential as biomarkers for immunotherapy. Methods: A total of 383 patients with GC from the Cancer Genome Atlas (TCGA) cohort, 300 patients with GC from the GSE62254 cohort in Gene Expression Omnibus (GEO) were included in the study. A stromal score was generated using the ESTIMATE algorithm, and the likelihood of response to PD-1/PD-L1 immunotherapy of GC patients was predicted using the TIDE algorithm. The prognostic value of the stromal score from GC cases was evaluated by the Kaplan-Meier method and Cox regression analysis. Gene set enrichment analysis (GSEA) was also conducted. Results: The stromal score showed significant differences in different molecular subtypes and T stages. Multivariate analyses further confirmed that the stromal score was an independent indicator of overall survival (OS) in the two cohorts. The low stromal score group showed higher tumor mutation burden (TMB) and micro-satellite instability (MSI), and was more sensitive to immune checkpoint inhibitor according to the TIDE algorithm. Activation of the transforming growth factor and epithelial-mesenchymal transition were observed in the high stromal score subtype, which is associated with T-cell suppression, and may be responsible for resistance to PD-1/PD-L1 therapy. BPIFB2 was confirmed as a hub gene relevant to immunotherapy. Conclusion: The stromal score was associated with cancer progression and molecular subtypes, and may serve as a novel biomarker for predicting the prognosis and response to immunotherapy in patients with GC.

Transarterial chemoembolization (TACE)‐induced hypoxia can trigger residual liver cancer cells to present a more aggressive phenotype associated with chemoresistance, but the underlying mechanisms are still unknown. In this study, the human liver cancer cell line HepG2 was pre‐cultured in different oxygen environments to examine the possible mechanisms of hypoxia‐induced doxorubicin resistance. Our study showed that HepG2 cells pre‐cultured in a chronic intermittent hypoxic environment exhibited significant resistance to doxorubicin, evidenced by increased intracellular doxorubicin efflux, relatively higher cell proliferation, lower apoptosis, and decreased DNA damage. These changes were accompanied by high levels of NRF2 and ABCB1 under conditions of both chronic and acute hypoxia and PARP1 gene expression only under conditions of chronic hypoxia. SiRNA‐mediated silencing of NRF2 gene expression downregulated the expression of ABCB1 and increased the intracellular doxorubicin accumulation and cell apoptosis both in acute and chronic hypoxic HepG2 cells. Moreover, silencing of PARP1 gene expression increased the doxorubicin‐induced DNA damage and cell apoptosis in chronic hypoxic cells. On the basis of these findings, we concluded that NRF2/ABCB1‐mediated efflux and PARP1‐mediated DNA repair contribute to doxorubicin resistance in chronic hypoxic HepG2 cells.

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman’s correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.