Background CT may play a central role in the diagnosis and management of COVID-19 pneumonia. Purpose To perform a longitudinal study to analyze the serial CT findings over time in patients with COVID-19 pneumonia. Materials and Methods During January 16 to February 17, 2020, 90 patients (male:female, 33:57; mean age, 45 years) with COVID-19 pneumonia were prospectively enrolled and followed up until they were discharged or died, or until the end of the study. A total of 366 CT scans were acquired and reviewed by 2 groups of radiologists for the patterns and distribution of lung abnormalities, total CT scores and number of zones involved. Those features were analyzed for temporal change. Results CT scores and number of zones involved progressed rapidly, peaked during illness days 6-11 (median: 5 and 5), and followed by persistence of high levels. The predominant pattern of abnormalities after symptom onset was ground-glass opacity (35/78 [45%] to 49/79 [62%] in different periods). The percentage of mixed pattern peaked (30/78 [38%]) on illness days 12-17, and became the second most predominant pattern thereafter. Pure ground-glass opacity was the most prevalent sub-type of ground-glass opacity after symptom onset (20/50 [40%] to 20/28 [71%]). The percentage of ground-glass opacity with irregular linear opacity peaked on illness days 6-11 (14/50 [28%)]) and became the second most prevalent subtype thereafter. The distribution of lesions was predominantly bilateral and subpleural. 66/70 (94%) patients discharged had residual disease on final CT scans (median CT scores and zones involved: 4 and 4), with ground-glass opacity (42/70 [60%]) and pure ground-glass opacity (31/42 [74%]) the most common pattern and subtype. Conclusion The extent of lung abnormalities on CT peaked during illness days 6-11. The temporal changes of the diverse CT manifestations followed a specific pattern, which might indicate the progression and recovery of the illness.

Purpose: This study aimed to evaluate the efficacy and safety of camrelizumab plus apatinib with or without stereotactic body radiotherapy (SBRT) as first-line therapy for patients with hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). Patients and Methods: This is a multicenter, open-label, noncomparative, randomized trial that recruited patients with HCC with type II/III/IV PVTT, who had not previously received systemic therapy. Patients were randomly assigned (2:1) to receive camrelizumab (200 mg, every 3 weeks) and apatinib (250 mg, every day) with or without SBRT [95% planning target volume (PTV), 36–40 Gy/6–8 Gy]. The primary endpoint was overall survival (OS), and the secondary endpoints were progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), duration of response, time to progression, and safety. Results: Sixty patients were enrolled and randomly assigned to two prospective cohorts. Median OS were 12.7 months [95% confidence interval (CI), 10.2–not available (NA)] and 8.6 months (95% CI, 5.6–NA), and median PFS were 4.6 months (95% CI, 3.3–7.0) and 2.5 months (95% CI, 2.0–7.6) for the SBRT and non-SBRT cohorts, respectively. The ORR and DCR were 47.5% and 72.5% in the SBRT cohort, and 20.0% and 40.0% in the non-SBRT cohort. The most common treatment-related adverse events of any grade were hypertension (55.0%), hand-foot syndrome (51.7%), and leukopenia (50.0%). Grade ≥ 3 was reported in 13 (21.7%) patients. Conclusions: First-line treatment with camrelizumab–apatinib combined with or without SBRT showed clinical benefits in patients with HCC with PVTT, with an acceptable safety profile. Thus, these combination regimens may be potential options for such patients.

Background: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP). Methods: A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5–63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0–78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves. Results: Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <−500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5–10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance. Conclusions: Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.

Supplementary Figure 1. Study time frame.

Supplementary Figure 1. Study time frame.