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PURPOSE Resectable non–small-cell lung cancer (NSCLC) with a high probability of mediastinal nodal involvement requires mediastinal staging by endosonography and, in the absence of nodal metastases, confirmatory mediastinoscopy according to current guidelines. However, randomized data regarding immediate lung tumor resection after systematic endosonography versus additional confirmatory mediastinoscopy before resection are lacking. METHODS Patients with (suspected) resectable NSCLC and an indication for mediastinal staging after negative systematic endosonography were randomly assigned to immediate lung tumor resection or confirmatory mediastinoscopy followed by tumor resection. The primary outcome in this noninferiority trial (noninferiority margin of 8% that previously showed to not compromise survival, Pnoninferior < .0250) was the presence of unforeseen N2 disease after tumor resection with lymph node dissection. Secondary outcomes were 30-day major morbidity and mortality. RESULTS Between July 17, 2017, and October 5, 2020, 360 patients were randomly assigned, 178 to immediate lung tumor resection (seven dropouts) and 182 to confirmatory mediastinoscopy first (seven dropouts before and six after mediastinoscopy). Mediastinoscopy detected metastases in 8.0% (14/175; 95% CI, 4.8 to 13.0) of patients. Unforeseen N2 rate after immediate resection (8.8%) was noninferior compared with mediastinoscopy first (7.7%) in both intention-to-treat (Δ, 1.03%; UL 95% CIΔ, 7.2%; Pnoninferior = .0144) and per-protocol analyses (Δ, 0.83%; UL 95% CIΔ, 7.3%; Pnoninferior = .0157). Major morbidity and 30-day mortality was 12.9% after immediate resection versus 15.4% after mediastinoscopy first ( P = .4940). CONCLUSION On the basis of our chosen noninferiority margin in the rate of unforeseen N2, confirmatory mediastinoscopy after negative systematic endosonography can be omitted in patients with resectable NSCLC and an indication for mediastinal staging.

Although fibrinogen is a FDA qualified prognostic biomarker in COPD, it still lacks sufficient resolution to be clinically useful. Next to replication of findings in different cohorts also the combination with other validated biomarkers should be investigated. Therefore, the aim of this study was to confirm in a large well-defined population of COPD patients whether fibrinogen can predict mortality and whether a combination with the biomarker MR-proADM can increase prognostic accuracy. From the COMIC cohort study we included COPD patients with a blood sample obtained in stable state (n = 640) and/or at hospitalization for an acute exacerbation of COPD (n = 262). Risk of death during 3 years of follow up for the separate and combined biomarker models was analyzed with Cox regression. Furthermore, logistic regression models for death after one year were constructed. When both fibrinogen and MR-proADM were included in the survival model, a doubling in fibrinogen and MR-proADM levels gave a 2.2 (95% CI 1.3-3.7) and 2.1 (95% CI 1.5-3.0) fold increased risk of dying, respectively. The prediction model for death after 1 year improved significantly when MR-proADM was added to the model with fibrinogen (AUC increased from 0.78 to 0.83; p = 0.02). However, the combined model was not significantly more adequate than the model with solely MR-proADM (AUC 0.83 vs 0.82; p = 0.34). The study suggests that MR-proADM is more promising than fibrinogen in prediciting mortality. Adding fibrinogen to a model containing MR-proADM does not significantly increase the predictive capacity of the model.

Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies.This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer?In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data.A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86.Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer.The Netherlands Trial Register; No.: NL7025; URL.

Introduction: Exhaled-breath analysis of volatile organic compounds (VOCs) has shown the potential to detect lung cancer. Reproducibility of prediction models, especially based on artificial intelligence (AI) algorithms is essential. However, external validation is often lacking as this is time-consuming and meanwhile improved AI models often outperform the “older” model, based on a training set. We aim to simultaneously validate and improve a training model to distinguish non-small cell lung cancer (NSCLC) patients from healthy controls based on AI algorithms. Methods: We obtained exhaled-breath data of > 800 subjects. This new cohort will be used to externally validate our original prediction model to distinguish between NSCLC patients and healthy controls (N=290, AUC-ROC 0.76). In a step-wise design, a set of 50 subjects will be first predicted by the original model, whereupon these data are added to the unblinded data, and a new prediction model will be created based on an increased sample size. This will be repeated 6 times. The remaining 500 subjects will be used to validate the final extended model. Performance will be assessed by Area under the Curve. Results: Despite finishing the inclusions, due to the COVID pandemic, we have not yet been able to validate data of all included subjects in all 7 centres. This will be done before September 2020. Conclusion: We propose a design to simultaneously externally validate an original prediction model based on exhaled-breath data to distinguish NSCLC patients from healthy controls and develop new prediction models based on improved AI techniques.

Objective: Simulators allow trainees to acquire basic skills for flexible bronchoscopy (FB) before practice on a patient. The aim of the current study was to gain insight into the required skills for FB, their underlying cognitive aspects and to what extent they can be trained on a virtual-reality (VR) simulator. Method: Four established FB guidelines and a simulation-based FB curriculum were analyzed to identify essential steps in FB and used as input for a semi-structured interview. A retrospective think-aloud study was performed combined with a semi-structured interview. Six experts performed a diagnostic FB on a VR simulator which was video-recorded. Participants engaged in retrospective think-aloud while watching their performance on video. A semi-structured interview helped clarify concepts and opinions about simulation-based training. Results: Experts agreed that two types of skills can be trained on the VR simulator: handling of the bronchoscope and inspecting the airway. For other FB related skills, such as taking biopsies or dealing with complications, the VR simulator was too inaccurate for either training or assessment. The results from this study provide detailed descriptions of relevant cues, training goals, and experienced difficulties. Discussion: Simulation-based training for flexible bronchoscopy should combine different types of simulators to effectively train all relevant cognitive and psychomotor skills. Technical limitations and disagreements among experts about the level of competency required in FB need to be resolved before implementing the VR simulator in training and assessment.

Background: Previous studies showed that microRNAs (miR) in tissue biopsies and blood samples are potential biomarkers for diverse types of cancer. MiRs are actively secreted to end up in the blood and pass the kidneys to be excreted through urine. Aims of this study were to investigate whether miRs in urine can be used to diagnose non-small cell lung cancer (NSCLC) and to compare diagnostic miR panels of blood and urine. Methods: We used next generation sequencing (NGS) and differential expression analyses to identify diagnostic miRs in cell-free urine and plasma of 15 stage III and IV NSCLC patients and 13 healthy individuals. An independent urine dataset derived from a previous study with 13 NSCLC patients and 15 healthy controls was used to confirm reliability. Results: Urine of NSCLC patients contained a significant increased expression of 5 miRs and this panel identified lung cancer with a sensitivity of 76%, a specificity of 92% and an AUC of 85%. In the independent urine dataset these results persisted with a sensitivity and specificity of 63% and 100% and an AUC of 73%. No differentiation could be made between plasma samples using the same miR panel, in fact a completely different miR panel was identified by differential expression analyses with only limited differentiation power. Conclusion: A 5-miR panel in urine of patients showed good diagnostic accuracy in the differentiation between NSCLC patients and healthy controls. The diagnostic potential was confirmed in an independent dataset. Remarkably there was no differentiation power of this panel in plasma samples, nor could we identify a different diagnostic miR panel.

Introduction Exhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis. Methods Based on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis. Results NSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69–0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81–0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79–0.89). Conclusions Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer., Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer http://bit.ly/38ps6fH

Background: Elevated levels of midrange proadrenomedullin (MR-proADM) are associated with worse outcome in different diseases, including COPD. The association of stable-state MR-proADM with severe acute exacerbations of COPD (AECOPDs) requiring hospitalization, or with community-acquired pneumonia (CAP) in patients with COPD, has not been studied yet. The aim of this study was to evaluate the association of stable-state MR-proADM with severe AECOPD and CAP in patients with COPD. Methods: This study pooled data of 1,285 patients from the Cohort of Mortality and Inflammation in COPD (COMIC) and PRedicting Outcome using systemic Markers In Severe Exacerbations of Chronic Obstructive Pulmonary Disease (PROMISE-COPD) cohort studies. Time until first severe AECOPD was compared between patients with high (≥ 0.87 nmol/L) or low (< 0.87 nmol/L) levels of plasma MR-proADM in stable state as previously defined. For time until first CAP, only COMIC data (n = 795) were available. Results: Patients with COPD with high-level stable-state MR-proADM have a significantly higher risk for severe AECOPD compared with those with low-level MR-proADM with a corrected hazard ratio (HR) of 1.30 (95% CI, 1.01-1.68). Patients with high-level stable-state MR-proADM had a significantly higher risk for CAP compared with patients with COPD with low-level MR-proADM in univariate analysis (HR, 1.93; 95% CI, 1.24-3.01), but after correction for age, lung function, and previous AECOPD, the association was no longer significant (corrected HR, 1.10; 95% CI, 0.68-1.80). Conclusions: Stable-state high-level MR-proADM in patients with COPD is associated with severe AECOPD but not with CAP.

Introduction: Lung cancer is a leading cause of cancer mortality. Exhaled-breath analysis of volatile organic compounds (VOC’s) may detect lung cancer at an early stage, possibly leading to better outcomes. Artificial neural networks (ANN) have proven to be accurate tools to diagnose lung cancer by distinguishing between breath profiles of healthy and sick individuals. Adding readily available clinical information to the ANN may improve the diagnostic accuracy. Methods: Subjects with non-small cell lung cancer (NSCLC) and healthy controls breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). Diagnostic accuracy, presented as Area under the Curve (AUC) was studied in a prospective, multi-center study in 282 individuals of whom 140 had confirmed NSCLC. We compared a 6-element breath-vector (Aeonose-only) with a 10-element vector (Aeonose plus age, pack years, COPD-presence, and gender). Results: Confirmed NSCLC patients (67.1 (9.0) years; 57.6% male) were compared with non-NSCLC controls (62.1 (7.1) years; 40.4% male). The AUC based on the Aeonose-only classification was 0.75 (95% CI: 0.69-0.81). Adding age, number of pack years, presence of COPD, and gender to the ANN resulted in an improved performance with an AUC of 0.84 (95% CI: 0.79-0.88). By choosing an appropriate threshold value in the ROC-diagram of the multivariate model, we observed a sensitivity of 92.9%, a specificity of 53.3%, and a positive and negative predictive value of 66.3% and 88.4%, respectively. Conclusion: The diagnostic accuracy to predict presence or absence of lung cancer can be improved significantly by adding readily available clinical information to the ANN.

Introduction: Lung cancer remains a leading cause of cancer mortality. Exhaled-breath analysis of volatile organic compounds (VOC’s), reflecting pathological processes, might detect lung cancer at an early stage, possibly leading to improved outcomes. Combining breath patterns with clinical parameters may improve the accuracy to diagnose lung cancer. Methods: In a multi-center study 144 subjects diagnosed with non-small cell lung cancer (NSCLC) and 146 healthy subjects breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). The diagnostic accuracy, presented as Area under the Curve (AUC) of the Aeonose™ sec was compared with the diagnostic accuracy when combined with clinical parameters in a multivariate logistic regression analysis. Results: Confirmed NSCLC patients (67.1 (9.0) years; 57.6% male) were compared with controls without NSCLC (62.1 (7.1) years; 40.4% male). The AUC of the absolute Aeonose™ value obtained by a trained neural network was 0.76 (95% CI: 0.71-0.82). Adding age, number of pack years, and presence of COPD to this absolute value of the Aeonose™ from the neural network resulted in an improved performance with an AUC of 0.86 (95% CI: 0.81-0.90). By choosing an appropriate threshold value in the ROC-diagram of the multivariate model, we observed a sensitivity of 95.7%, a specificity of 59.7%, and a positive and negative predictive value of 69.5% and 92.5%, respectively. Conclusion: Adding readily available clinical information to the absolute obtained value of exhaled-breath analysis with the Aeonose™ improves the diagnostic accuracy to detect the presence or absence of lung cancer.

Objectives Lung cancer is a leading cause of mortality. Exhaled-breath analysis of volatile organic compounds (VOC’s) might detect lung cancer early in the course of the disease, which may improve outcomes. Subtyping lung cancers could be helpful in further clinical decisions. Materials and methods In a prospective, multi-centre study, using 10 electronic nose devices, 144 subjects diagnosed with NSCLC and 146 healthy subjects, including subjects considered negative for NSCLC after investigation, breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). Also, analyses into subtypes of NSCLC, such as adenocarcinoma (AC) and squamous cell carcinoma (SCC), and analyses of patients with small cell lung cancer (SCLC) were performed. Results Choosing a cut-off point to predominantly rule out cancer resulted for NSCLC in a sensitivity of 94.4%, a specificity of 32.9%, a positive predictive value of 58.1%, a negative predictive value (NPV) of 85.7%, and an area under the curve (AUC) of 0.76. For AC sensitivity, PPV, NPV, and AUC were 81.5%, 56.4%, 79.5%, and 0.74, respectively, while for SCC these numbers were 80.8%, 45.7%, 93.0%, and 0.77, respectively. SCLC could be ruled out with a sensitivity of 88.9% and an NPV of 96.8% with an AUC of 0.86. Conclusion Electronic nose technology with the Aeonose™ can play an important role in rapidly excluding lung cancer due to the high negative predictive value for various, but not all types of lung cancer. Patients showing positive breath tests should still be subjected to further diagnostic testing.

Introduction: Small cell lung cancer (SCLC) is an aggressive type of lung cancer and accounts for 15% of all lung cancer cases. Progress in the survival rate of SCLC is poor with a 5-year survival rate of only 5-10%, depending on tumour stage at presentation. Tumour detection earlier in the course of this disease is a potential tool to improve outcome. Exhaled-breath analysis of volatile organic compounds (VOC’s), reflecting pathological processes, have the potential to diagnose lung cancer in a sensitive, non-invasive way. We aimed to determine the diagnostic accuracy of breath analysis in SCLC using an electronic nose (Aeonose™). Methods: Subjects with confirmed SCLC and healthy subjects breathed into the Aeonose™ (The eNose Company, Zutphen, The Netherlands) for 5 minutes. Diagnostic performance was studied in a prospective multicentre study conducted in 4 hospitals in 93 subjects of which 18 had pathologically confirmed SCLC. The breath prints of the two groups were compared, using data compression and artificial neural networks for the statistical analysis of VOC data. Results: SCLC patients and healthy subjects had a mean age of 63.2±8.2 and 65.9±8.8 years, respectively. Choosing a proper point at the ROC curve to rule out SCLC resulted in a sensitivity of 88.9%, a specificity of 80.0%, a positive predictive value of 51.6%, a negative predictive value of 96.8%, and an area under the curve of 0.86. Conclusion: The data show that electronic nose technology with the Aeonose™ can play an important role in rapidly excluding SCLC due to the high negative predictive value that was obtained in this study. Patients showing positive breath tests should still be subjected to further diagnostic testing.

Background: In patients hospitalised for an exacerbation of COPD (severe AECOPD), elevated blood eosinophil count was associated with a shortened length of stay. Less is known about its association with survival. Aims and objectives: The aim of this study was to evaluate the association of elevated blood eosinophil count at admission for a severe AECOPD with all-cause mortality. Methods: 450 patients of the COMIC cohort study who were admitted for a severe AECOPD were divided in normal or elevated blood eosinophil count, defined as eosinophils ≥2% of the total leukocyte count on day of admission. Outcome parameter was all-cause mortality. Minimum follow-up was 3 years. Results: Elevated blood eosinophil count (N=97) was associated with a significantly lower risk for all-cause mortality in univariate analysis (Figure 1; logrank p=0.004) (HR 1.53; 95%CI 1.15-2.04). [figure1] Patients with elevated blood eosinophil count were younger than patients with normal blood eosinophil count (66.3 (SD 10.4) versus 70.6 (SD 9.6) years, respectively (p Conclusion: The strong association of elevated blood eosinophils on admission for a severe AECOPD with lower risk for mortality in univariate analysis disappeared after correction for age. The association of age with eosinophil count needs further investigation.

Background: Both chronic inflammation and cardiovascular comorbidity play an important role in the morbidity and mortality in patients with chronic obstructive pulmonary disease (COPD). Statins could be a potential adjunct therapy. The additional effects of statins in COPD are however still under discussion. Aims and objectives: The aim of this study is to further investigate the effects of statin use on mortality in a well-described COPD cohort. Methods: 795 patients of the Cohort of Mortality and Inflammation in COPD (COMIC) study were divided into statin users or not. Statin use was defined as having a statin for at least 90 consecutive days after inclusion. Outcome parameter was 3-year survival, based on all-cause mortality. A sensitivity analysis was performed without patients who started a statin 3 months or more after inclusion to exclude immortal time bias. Results: Statin use, corrected for confounders, resulted in a better overall survival (corrected hazard ratio 0.70 (95% CI, 0.51 - 0.96)), but in the sensitivity analysis this effect disappeared. Conclusions: In the COMIC study statin use is not associated with a reduced risk of all-cause mortality in patients with COPD.

Background: Midrange-proadrenomedullin(MR-proADM), assessed in patients with clinically stable COPD, has been shown to be an independent predictor of all-cause mortality. In patients with community acquired pneumonia(CAP), pro-adrenomedullin has been associated with mortality. However, stable state MR-proADM as a predictor for severe exacerbations of COPD with hospitalisation(AECOPD), or for CAP in COPD patients has not been studied yet. Aims and objectives: The aim of this study was to evaluate stable state MR-proADM as an independent predictor for an AECOPD and CAP in COPD patients. Methods: This study pooled data of 1285 patients from the COMIC and PROMISE-COPD cohort studies. Time till first AECOPD was compared between patients with high or low levels of MR-proADM based on the cut-off level of 0.87 nmol/l in stable state defined in earlier studies. For time till first CAP only COMIC data could be used. Results: COPD patients with high level stable state MR-proADM had a significantly higher risk for an AECOPD compared with those with low level MR-proADM with an adjusted Hazard Ratio (HR) of 1.33 (95% CI, 1.03 - 1.71) in multivariate Cox regression analysis. In the COMIC study COPD patients with high level stable state MR-proADM had a significantly higher risk for a CAP compared with COPD patients with low level MR-proADM (unadjusted HR 1.93; p=0.003). However, corrected for age and lung function in multivariate Cox regression this effect disappeared (corrected HR 1.17; p=0.532). Conclusions: Stable state high level MR-proADM in COPD patients is an independent predictor for a severe AECOPD.

Background Both chronic inflammation and cardiovascular comorbidity play an important role in the morbidity and mortality of patients with chronic obstructive pulmonary disease (COPD). Statins could be a potential adjunct therapy. The additional effects of statins in COPD are, however, still under discussion. The aim of this study is to further investigate the association of statin use with clinical outcomes in a well-described COPD cohort. Methods 795 patients of the Cohort of Mortality and Inflammation in COPD (COMIC) study were divided into statin users or not. Statin use was defined as having a statin for at least 90 consecutive days after inclusion. Outcome parameters were 3-year survival, based on all-cause mortality, time until first hospitalisation for an acute exacerbation of COPD (AECOPD) and time until first community-acquired pneumonia (CAP). A sensitivity analysis was performed without patients who started a statin 3 months or more after inclusion to exclude immortal time bias. Results Statin use resulted in a better overall survival (corrected HR 0.70 (95% CI 0.51 to 0.96) in multivariate analysis), but in the sensitivity analysis this association disappeared. Statin use was not associated with time until first hospitalisation for an AECOPD (cHR 0.95, 95% CI 0.74 to 1.22) or time until first CAP (cHR 1.1, 95% CI 0.83 to 1.47). Conclusions In the COMIC study, statin use is not associated with a reduced risk of all-cause mortality, time until first hospitalisation for an AECOPD or time until first CAP in patients with COPD.

Purpose: The prognosis of malignant pleural mesothelioma (MPM) is poor and most prognostic factors are inconsistent. PET/CT provides a measure of the metabolic activity of the tumor. Therefore, we evaluated the prognostic value of parameters derived from PET/CT, produced by our new developed tool. Methods: PET/CT images of 38 patients, from three institutions, with MPM who underwent PET/CT prior to treatment were retrospectively semi-automatically analyzed. The measured parameters included; mean, maximum and peak standardized uptake values (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG, the tumor volume multiplied by its mean SUV). Results: Median survival was 9.9 months after diagnosis. Univariate Cox regression showed that SUVmax (HR=1.05), SUVpeak (HR=1.05), TLG (HR=1.15) and MTV (HR=1.13) are significant predictors for overall survival. Kaplan-Meier analyses showed significant difference in survival when MTV and TLG were grouped based on their median value. Conclusion: A new developed software tool showed that SUVmax, SUVpeak, TLG and MTV are all significant PET/CT parameters to predict survival in patients with malignant pleural mesothelioma. A prospective study should be performed to asses the possibility for chemotherapy response monitoring using this tool.