Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients.

PURPOSE: This study aimed to identify predictive (bio-)markers for COVID-19 severity derived from automated quantitative thin slice low dose volumetric CT analysis, clinical chemistry and lung function testing. METHODS: Seventy-four COVID-19 patients admitted between March 16th and June 3rd 2020 to the Asklepios Lung Clinic Munich-Gauting, Germany, were included in the study. Patients were categorized in a non-severe group including patients hospitalized on general wards only and in a severe group including patients requiring intensive care treatment. Fully automated quantification of CT scans was performed via IMBIO CT Lung Texture analysis™ software. Predictive biomarkers were assessed with receiver-operator-curve and likelihood analysis. RESULTS: Fifty-five patients (44% female) presented with non-severe COVID-19 and 19 patients (32% female) with severe disease. Five fatalities were reported in the severe group. Accurate automated CT analysis was possible with 61 CTs (82%). Disease severity was linked to lower residual normal lung (72.5% vs 87%, p = 0.003), increased ground glass opacities (GGO) (8% vs 5%, p = 0.031) and increased reticular pattern (8% vs 2%, p = 0.025). Disease severity was associated with advanced age (76 vs 59 years, p = 0.001) and elevated serum C-reactive protein (CRP, 92.2 vs 36.3 mg/L, p < 0.001), lactate dehydrogenase (LDH, 485 vs 268 IU/L, p < 0.001) and oxygen supplementation (p < 0.001) upon admission. Predictive risk factors for the development of severe COVID-19 were oxygen supplementation, LDH >313 IU/L, CRP >71 mg/L, <70% normal lung texture, >12.5% GGO and >4.5% reticular pattern. CONCLUSION: Automated low dose CT analysis upon admission might be a useful tool to predict COVID-19 severity in patients.

[Pulmonary involvement in cancers]. / Lungenbeteiligung bei Tumorkrankheiten.

The lungs are often involved in tumors and are affected in a wide variety of ways. Lung cancer comprises one of the most common cancer entities and has been characterized by a vast expansion of treatment approaches in recent years. Moreover, the lungs are a common metastatic site of multiple other cancer entities. Various treatment modalities, such as tyrosine kinase inhibitors, checkpoint inhibitors, chimeric antigen receptor cell therapy, and radiotherapy approaches can cause pulmonary side effects. Finally, many patients suffer from pulmonary comorbidities which may mutually impact the clinical course and prognosis of the cancer disease. As examples, various aspects, such as pulmonary veno-occlusive disease, chronic obstructive pulmonary disease, and idiopathic pulmonary fibrosis are discussed.