Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

European Respiratory Society Short Guidelines for the use of as-needed ICS/formoterol in mild Asthma.

Recent clinical trials of as-needed fixed-dose combination of ICS/formoterol have provided new evidence that may warrant a reconsideration of current practice.A task force (TF) was set up by the European Respiratory Society to provide evidence-based recommendations on the use of as-needed ICS/formoterol as treatment for mild asthma. The TF defined two questions that were assessed using the Grading of Recommendations, Assessment, Development and Evaluation approach. The TF utilised the outcomes to develop recommendations for a pragmatic guideline for everyday clinical practice.The TF suggests that adults with mild asthma use as-needed ICS/formoterol instead of regular ICS maintenance treatment plus as-needed short-acting beta-2-antagonists (SABAs), and that adolescents with mild asthma use either as-needed ICS/formoterol or ICS maintenance treatment plus as-needed SABA (Conditional Recommendation; Low Certainty of Evidence). The recommendation for adults places a relatively higher value on the reduction of systemic corticosteroid use and the outcomes related to exacerbations and a relatively lower value on the small differences in asthma control. Either treatment options are suggested for adolescent patients as the balance is very close and data more limited.The TF recommends that adult and adolescent patients with mild asthma use as-needed ICS/formoterol instead of as-needed SABA (Strong Recommendation; Low Certainty of Evidence). This recommendation is based on the benefit of as-needed ICS/formoterol in mild asthma on several outcomes and the risks related to as-needed SABA in the absence of anti-inflammatory treatment.The implementation of this recommendation is hampered in countries (including European Union countries) where as-needed ICS/formoterol is not approved for mild asthma

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

Metallomic signatures of lung cancer and chronic obstructive pulmonary disease

Lung cancer (LC) is the leading cause of cancer deaths, and chronic obstructive pulmonary disease (COPD) can increase LC risk. Metallomics may provide insights into both of these tobacco-related diseases and their shared etiology. We conducted an observational study of 191 human serum samples, including those of healthy controls, LC patients, COPD patients, and patients with both COPD and LC. We found 18 elements (V, Al, As, Mn, Co, Cu, Zn, Cd, Se, W, Mo, Sb, Pb, Tl, Cr, Mg, Ni, and U) in these samples. In addition, we evaluated the elemental profiles of COPD cases of varying severity. The ratios and associations between the elements were also studied as possible signatures of the diseases. COPD severity and LC have a significant impact on the elemental composition of human serum. The severity of COPD was found to reduce the serum concentrations of As, Cd, and Tl and increased the serum concentrations of Mn and Sb compared with healthy control samples, while LC was found to increase Al, As, Mn, and Pb concentrations. This study provides new insights into the effects of LC and COPD on the human serum elemental profile that will pave the way for the potential use of elements as biomarkers for diagnosis and prognosis. It also sheds light on the potential link between the two diseases, i.e., the evolution of COPD to LC.Funding: This work has been supported by the project “Heteroatom-tagged proteomics and metabolomics to study lung cancer. Influence of gut microbiota” (Ref.: PY20_00366) (Project of Excellence, Regional Ministry of Economy, Knowledge, Business and University, Andalusia, Spain). The authors are also grateful for grants 651/2018 and 115/2020 from the Spanish Society of Pneumology and Surgery (SEPAR) and grant 08/2018 from the Association of Pneumology and Thoracic Surgery (Neumosur), which were used to facilitate recruitment at the hospitals and biobank registration. The authors also thank Instituto de Salud Carlos III (AES16/01783) and wish to express their gratitude for the unrestricted funding from the Menarini Group and AstraZeneca. Acknowledgments: We thank all the patients who volunteered and donated their biomaterials for the study

Untargeted Metabolomic Study of Lung Cancer Patients after Surgery with Curative Intent

Lung cancer (LC) is a leading cause of mortality, claiming more than 1.8 million deaths per year worldwide. Surgery is one of the most effective treatments when the disease is in its early stages. The study of metabolic alterations after surgical intervention with curative intent could be used to assess the response to treatment or the detection of cancer recurrence. In this study, we have evaluated the metabolomic profile of serum samples (n = 110) from preoperative (PRE) and postoperative (POST) LC patients collected at two different time points (1 month, A; 3–6 months, B) with respect to healthy people. An untargeted metabolomic platform based on reversed phase (RP) and hydrophilic interaction chromatography (HILIC), using ultra-high performance liquid chromatography (UHPLC) and mass spectrometry (MS), was applied (MassIVE ID MSV000092213). Twenty-two altered metabolites were annotated by comparing all the different studied groups. DG(14,0/22:1), stearamide, proline, and E,e-carotene-3,3′-dione were found altered in PRE, and their levels returned to those of a baseline control group 3–6 months after surgery. Furthermore, 3-galactosyllactose levels remained altered after intervention in some patients. This study provides unique insights into the metabolic profiles of LC patients after surgery at two different time points by combining complementary analytical methods

Impact of applying the global lung initiative criteria for airway obstruction in GOLD defined COPD cohorts: the BODE and CHAIN experience

Introduction: The Global Lung Function Initiative (GLI) has proposed new criteria for airflow limitation (AL) and recommends using these to interpret spirometry. The objective of this study was to explore the impact of the application of the AL GLI criteria in two well characterized GOLD-defined COPD cohorts. Methods: COPD patients from the BODE (n=360) and the COPD History Assessment In SpaiN (CHAIN) cohorts (n=722) were enrolled and followed. Age, gender, pack-years history, BMI, dyspnea, lung function measurements, exercise capacity, BODE index, history of exacerbations and survival were recorded. CT-detected comorbidities were registered in the BODE cohort. The proportion of subjects without AL by GLI criteria was determined in each cohort. The clinical, CT-detected comorbidity, and overall survival of these patients were evaluated. Results: In total, 18% of the BODE and 15% of the CHAIN cohort did not meet GLI AL criteria. In the BODE and CHAIN cohorts respectively, these patients had a high clinical burden (BODE≥3: 9% and 20%; mMRC≥2: 16% and 45%; exacerbations in the previous year: 31% and 9%; 6MWD<350m: 15% and 19%, respectively), and a similar prevalence of CT-diagnosed comorbidities compared with those with GLI AL. They also had a higher rate of long-term mortality - 33% and 22% respectively. Conclusions: An important proportion of patients from 2 GOLD-defined COPD cohorts did not meet GLI AL criteria at enrolment, although they had a significant burden of disease. Caution must be taken when applying the GLI AL criteria in clinical practice

Metallomic Signatures of Lung Cancer and Chronic Obstructive Pulmonary Disease

Lung cancer (LC) is the leading cause of cancer deaths, and chronic obstructive pulmonary disease (COPD) can increase LC risk. Metallomics may provide insights into both of these tobacco-related diseases and their shared etiology. We conducted an observational study of 191 human serum samples, including those of healthy controls, LC patients, COPD patients, and patients with both COPD and LC. We found 18 elements (V, Al, As, Mn, Co, Cu, Zn, Cd, Se, W, Mo, Sb, Pb, Tl, Cr, Mg, Ni, and U) in these samples. In addition, we evaluated the elemental profiles of COPD cases of varying severity. The ratios and associations between the elements were also studied as possible signatures of the diseases. COPD severity and LC have a significant impact on the elemental composition of human serum. The severity of COPD was found to reduce the serum concentrations of As, Cd, and Tl and increased the serum concentrations of Mn and Sb compared with healthy control samples, while LC was found to increase Al, As, Mn, and Pb concentrations. This study provides new insights into the effects of LC and COPD on the human serum elemental profile that will pave the way for the potential use of elements as biomarkers for diagnosis and prognosis. It also sheds light on the potential link between the two diseases, i.e., the evolution of COPD to LC