Introduction: The Global Initiative for Obstructive Lung Disease (GOLD) recommends lung cancer screening for patients with Chronic Obstructive Pulmonary Disease (COPD), but data is lacking regarding results of screening in this high-risk population. The main goal of the present work is to explore if lung cancer screening with Low Dose Chest Tomography (LDCT) in people with COPD, allows lung cancer (LC) diagnosis in early stages with survival compatible with curative state. Methods: This is a post hoc exploratory analysis. Pamplona International Early Lung Cancer Action Program (P-IELCAP) participants with a GOLD defined obstructive pattern (post bronchodilator FEV1/FVC<0.70) were selected for analysis. The characteristics of those who developed LC and their survival are described. A Cox proportional analysis explored the factors associated with LC diagnosis. Results: Eight hundred and sixty-five patients (77% male, 93% in spirometric GOLD stage 1+2) were followed for 102±63 months. LC prevalence was 2.6% at baseline, with an annual LC diagnosis rate of 0.68%. Early-stage tumors predominated (74%) with a median survival (25-75th percentiles) of 139 (76-185) months. Cumulative tobacco exposure, FEV1%, and emphysema were the main predictors of an LC diagnosis. Eight (11%) patients with COPD had a second LC, most of them in early stage (92%), and 6 (8%) had recurrence. Median survival (25-75th percentiles) in these patients was 168 (108-191) months.

BackgroundIdentifying predictive non-invasive biomarkers of immunotherapy response is crucial to avoid premature treatment interruptions or ineffective prolongation. Our aim was to develop a non-invasive biomarker for predicting immunotherapy clinical durable benefit, based on the integration of radiomics and clinical data monitored through early anti-PD-1/PD-L1 monoclonal antibodies treatment in patients with advanced non-small cell lung cancer (NSCLC).MethodsIn this study, 264 patients with pathologically confirmed stage IV NSCLC treated with immunotherapy were retrospectively collected from two institutions. The cohort was randomly divided into a training (n = 221) and an independent test set (n = 43), ensuring the balanced availability of baseline and follow-up data for each patient. Clinical data corresponding to the start of treatment was retrieved from electronic patient records, and blood test variables after the first and third cycles of immunotherapy were also collected. Additionally, traditional radiomics and deep-radiomics features were extracted from the primary tumors of the computed tomography (CT) scans before treatment and during patient follow-up. Random Forest was used to implementing baseline and longitudinal models using clinical and radiomics data separately, and then an ensemble model was built integrating both sources of information.ResultsThe integration of longitudinal clinical and deep-radiomics data significantly improved clinical durable benefit prediction at 6 and 9 months after treatment in the independent test set, achieving an area under the receiver operating characteristic curve of 0.824 (95% CI: [0.658,0.953]) and 0.753 (95% CI: [0.549,0.931]). The Kaplan-Meier survival analysis showed that, for both endpoints, the signatures significantly stratified high- and low-risk patients (p-value< 0.05) and were significantly correlated with progression-free survival (PFS6 model: C-index 0.723, p-value = 0.004; PFS9 model: C-index 0.685, p-value = 0.030) and overall survival (PFS6 models: C-index 0.768, p-value = 0.002; PFS9 model: C-index 0.736, p-value = 0.023).ConclusionsIntegrating multidimensional and longitudinal data improved clinical durable benefit prediction to immunotherapy treatment of advanced non-small cell lung cancer patients. The selection of effective treatment and the appropriate evaluation of clinical benefit are important for better managing cancer patients with prolonged survival and preserving quality of life.

To address the feasibility of implementing a lung cancer screening program in liver transplant recipients (LTR) targeted to detect early-stage lung cancer one hundred twenty-four LTR (89% male, 59.8+/-8.8 y old), who entered the lung cancer screening program at our hospital were reviewed. The results of the diagnostic algorithm using low-dose CT and F-18-fluorodeoxyglycose positron emission tomography (FDG-PET) were analyzed. Lung cancer was detected in 12 LTR (9.7%), most of which corresponded to the non-small cell subtype. Two of the 12 lung cancers were detected in the baseline study (prevalence of 1.6%), whereas 10 patients were diagnosed with lung cancer in the follow-up (incidence of 8.1%). Considering all cancers, 10 of 12 (83.3%) were diagnosed at stage I, one cancer was diagnosed at stage IIIA, and another one at stage IV. The sensitivity, specificity, diagnostic accuracy, and positive and negative predictive values of F-18-fluorodeoxyglycose positron emission tomography to detect malignancy in our cohort were 81.8%,100%, 99.3%, 100%, and 99.3%, respectively. A carefully followed multidisciplinary lung cancer screening algorithm in LTR that includes F-18-fluorodeoxyglycose positron emission tomography and low-dose CT allows lung cancer to be diagnosed at an early stage while reducing unnecessary invasive procedures.

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.

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.

Objectives We aimed to assess the learning curves and the influence of the pathologist's performance on the endobronchial ultrasound transbronchial needle aspiration's (EBUS-TBNA's) diagnostic accuracy in a real-world study. Design/setting Cohort study conducted in a tertiary care university hospital (single centre) with patients referred for EBUS-TBNA. Participants/intervention We initially evaluated 376 patients (673 lymph nodes), 368 (660 lymph nodes) of whom were recruited. The inclusion criterion was EBUS-TBNA indicated for the study of mediastinal or hilar lesions. The exclusion criteria were the absence of mediastinal and hilar lesions during EBUS confirmed by a normal mediastinum and hilum on chest CT (except in cases of mediastinal staging of cancer) and lost to follow-up. Primary and secondary outcome measures Diagnostic accuracy and related outcomes. Methods We included patients from a prospectively constructed database. We performed a logistic regression multivariate analysis to adjust for potential confounders of the association between pathologist performance and EBUS-TBNA accuracy. The Cumulative Summation (CUSUM) analysis was used to assess pathologists' performance and learning curves. Results Most indications for EBUS were suspicion of malignancy, including intrathoracic tumours (68.3%), extrathoracic tumours (9.8%) and cancer staging (7.0%). The patients' mean age was 63.7 years, and 71.5% were male. Overall EBUS-TBNA accuracy was 80.8%. In the multivariate logistic regression model, the factors independently associated with EBUS-TBNA accuracy included certain pathologists (ORs ranging from 0.16 to 0.41; p<0.017), a lymph node short-axis diameter <1 cm (OR: 0.36; 95% CI 0.21 to 0.62; p<0.001), and the aetiology of lymph node enlargement (ORs ranging from 7 to 37; p<0.001). CUSUM analysis revealed four different learning curve patterns, ranging from almost immediate learning to a prolonged learning phase, as well as a pattern consistent with performance attrition. Conclusions Pathologists' proficiency conditioned EBUS-TBNA accuracy. This human factor is a potential source of error independent of factors conditioning tissue sample adequacy.

Simple Summary The increasing number of data supporting use of a personalized approach in cancer treatment, is changing the path of patient's management. In the same time, the availability of technologies should allow patients to receive the best test for the specific individual condition. This is theoretically true, when a specific test is designed for the specific disease condition, while it is difficult to implement in the setting of agnostic therapies. Financial sources availability related to the non homogeneous health systems working in the different countries do not allow for an immediate implementation of the technologies and test commercially available. Future perspectives for targeted oncology include tumor-agnostic drugs, which target a given mutation and could be used in treating cancers from multiple organ types. Therefore, the present paper is aimed to both underline a how much important is this new view and also to sensitize the international bodies that supervise health policies at the decision-making level, with the aim of harmonizing cancer treatment pathways in at least all European countries. Rapid and continuing advances in biomarker testing are not being matched by uptake in health systems, and this is hampering both patient care and innovation. It also risks costing health systems the opportunity to make their services more efficient and, over time, more economical. The potential that genomics has brought to biomarker testing in diagnosis, prediction and research is being realised, pre-eminently in many cancers, but also in an ever-wider range of conditions-notably BRCA1/2 testing in ovarian, breast, pancreatic and prostate cancers. Nevertheless, the implementation of genetic testing in clinical routine setting is still challenging. Development is impeded by country-related heterogeneity, data deficiencies, and lack of policy alignment on standards, approval-and the role of real-world evidence in the process-and reimbursement. The acute nature of the problem is compellingly illustrated by the particular challenges facing the development and use of tumour agnostic therapies, where the gaps in preparedness for taking advantage of this innovative approach to cancer therapy are sharply exposed. Europe should already have in place a guarantee of universal access to a minimum suite of biomarker tests and should be planning for an optimum testing scenario with a wider range of biomarker tests integrated into a more sophisticated health system articulated around personalised medicine. Improving healthcare and winning advantages for Europe's industrial competitiveness and innovation require an appropriate policy framework-starting with an update to outdated recommendations. We show herein the main issues and proposals that emerged during the previous advisory boards organised by the European Alliance for Personalized Medicine which mainly focus on possible scenarios of harmonisation of both oncogenetic testing and management of cancer patients.

BACKGROUND: SARS-CoV-2, the COVID-19 causative agent, has infected millions of people and killed over 1.6 million worldwide. A small percentage of cases persist with prolonged positive RT-PCR on nasopharyngeal swabs. The aim of this study was to determine risk factors for prolonged viral shedding among patient's basal clinical conditions. METHODS: We have evaluated all 513 patients attended in our hospital between March 1 and July 1. We have selected all 18 patients with prolonged viral shedding, and compared them with 36 sex-matched randomly selected controls. Demographic, treatment and clinical data were systematically collected. RESULTS: Global median duration of viral clearance was 25.5 days (n=54; IQR, 22-39.3 days), 48.5 days in cases (IQR 38.7-54.9 days) and 23 days in controls (IQR 20.2-25.7), respectively. There were not observed differences in demographic, symptoms or treatment data between groups. Chronic rhino-sinusitis and atopy were more common in patients with prolonged viral shedding (67%) compared with controls (11% and 25% respectively) (p<0.001 and p=0,003). The use of inhaled corticosteroids was also more frequent in case group (p=0.007). Multivariate analysis indicated that CRS (odds ratio [OR], 18.78; 95% confidence interval [95%CI],3.89 - 90.59; p<0.001) was independently associated with prolonged SARS-CoV-2 RNA shedding in URT samples, after adjusting for initial PCR Ct values. CONCLUSION: We found that chronic rhino-sinusitis and atopy might be ass