Silent Airway Mucus Plugs in COPD and Clinical Implications.

BACKGROUND: Airway mucus plugs are frequently identified on CT scans of patients with COPD with a smoking history without mucus-related symptoms (ie, cough, phlegm [silent mucus plugs]). RESEARCH QUESTION: In patients with COPD, what are the risk and protective factors associated with silent airway mucus plugs? Are silent mucus plugs associated with functional, structural, and clinical measures of disease? STUDY DESIGN AND METHODS: We identified mucus plugs on chest CT scans of participants with COPD from the COPDGene study. The mucus plug score was defined as the number of pulmonary segments with mucus plugs, ranging from 0 to 18, and categorized into three groups (0, 1-2, and ≥ 3). We determined risk and protective factors for silent mucus plugs and the associations of silent mucus plugs with measures of disease severity using multivariable linear and logistic regression models. RESULTS: Of 4,363 participants with COPD, 1,739 had no cough or phlegm. Among the 1,739 participants, 627 (36%) had airway mucus plugs identified on CT scan. Risk factors of silent mucus plugs (compared with symptomatic mucus plugs) were older age (OR, 1.02), female sex (OR, 1.40), and Black race (OR, 1.93) (all P values < .01). Among those without cough or phlegm, silent mucus plugs (vs absence of mucus plugs) were associated with worse 6-min walk distance, worse resting arterial oxygen saturation, worse FEV1 % predicted, greater emphysema, thicker airway walls, and higher odds of severe exacerbation in the past year in adjusted models. INTERPRETATION: Mucus plugs are common in patients with COPD without mucus-related symptoms. Silent mucus plugs are associated with worse functional, structural, and clinical measures of disease. CT scan-identified mucus plugs can complement the evaluation of patients with COPD.

Black race and lower age at surgery are associated with smoking relapse in a safety-net setting after surgery for stage I non-small cell lung cancer.

Background: Smoking relapse after surgical resection for lung cancer (LC) remains a health concern. This study aims to determine various factors associated with postoperative smoking relapse in patients undergoing surgical resection for stage I non-small cell lung cancer (NSCLC) at an urban safety net hospital. Methods: We analyzed the demographic and clinical variables of all patients who underwent surgical resection for stage I NSCLC from 2002 to 2016 at our institution. Based on the post-operative smoking history, we segregated the cohort into two groups: relapse and abstinent. Chi-squared and analysis of variance tests were used to identify the variables that registered a significant difference between the two groups. Further, we used univariable and multivariable logistic regression to determine association between variables and smoking relapse. Results: We analyzed data from 168 patients, excluding those with inadequate smoking history and never smokers. In total, 64 (38.1%) patients experienced smoking relapse, and 104 (61.9%) remained abstinent. The age, annual income, and race showed significant differences between the two groups. Multivariable logistic regression reflected that black patients had higher odds of relapse than white patients [odds ratio (OR) =3.26, confidence interval (CI): 1.54-6.89, P=0.002] and the chances of relapse decreased as the age increased (5-year age gap, OR =0.70, CI: 0.58-0.85, P<0.001). Conclusions: Black race and younger age at the time of surgery are associated with smoking relapse after surgery for stage I NSCLC. Targeted smoking cessation programs catered towards these patient groups may help reduce the prevalence of post-operative smoking.

Bronchial epithelial gene expression and interstitial lung abnormalities.

INTRODUCTION: Interstitial lung abnormalities (ILA) often represent early fibrotic changes that can portend a progressive fibrotic phenotype. In particular, the fibrotic subtype of ILA is associated with increased mortality and rapid decline in lung function. Understanding the differential gene expression that occurs in the lungs of participants with fibrotic ILA may provide insight into development of a useful biomarker for early detection and therapeutic targets for progressive pulmonary fibrosis. METHODS: Measures of ILA and gene expression data were available in 213 participants in the Detection of Early Lung Cancer Among Military Personnel (DECAMP1 and DECAMP2) cohorts. ILA was defined using Fleischner Society guidelines and determined by sequential reading of computed tomography (CT) scans. Primary analysis focused on comparing gene expression in ILA with usual interstitial pneumonia (UIP) pattern with those with no ILA. RESULTS: ILA was present in 51 (24%) participants, of which 16 (7%) were subtyped as ILA with a UIP pattern. One gene, pro platelet basic protein (PPBP) and seventeen pathways (e.g. TNF-α signalling) were significantly differentially expressed between those with a probable or definite UIP pattern of ILA compared to those without ILA. 16 of these 17 pathways, but no individual gene, met significance when comparing those with ILA to those without ILA. CONCLUSION: Our study demonstrates that abnormal inflammatory processes are apparent in the bronchial airway gene expression profiles of smokers with and without lung cancer with ILA. Future studies with larger and more diverse populations will be needed to confirm these findings.

A Millimeter-Scale Soft Robot for Tissue Biopsy Procedures.

While interest in soft robotics as surgical tools has grown due to their inherently safe interactions with the body, their feasibility is limited in the amount of force that can be transmitted during procedures. This is especially apparent in minimally invasive procedures where millimeter-scale devices are necessary for reaching the desired surgical site, such as in interventional bronchoscopy. To leverage the benefits of soft robotics in minimally invasive surgery, a soft robot with integrated tip steering, stabilization, and needle deployment capabilities is proposed for lung tissue biopsy procedures. Design, fabrication, and modeling of the force transmission of this soft robotic platform allows for integration into a system with a diameter of 3.5 mm. Characterizations of the soft robot are performed to analyze bending angle, force transmission, and expansion during needle deployment. In-vitro experiments of both the needle deployment mechanism and fully integrated soft robot validate the proposed workflow and capabilities in a simulated surgical setting.

Single-molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer.

Somatic mutations are a hallmark of tumorigenesis and may be useful for non-invasive diagnosis of cancer. We analyzed whole-genome sequencing data from 2,511 individuals in the Pan-Cancer Analysis of Whole Genomes (PCAWG) study as well as 489 individuals from four prospective cohorts and found distinct regional mutation type-specific frequencies in tissue and cell-free DNA from patients with cancer that were associated with replication timing and other chromatin features. A machine-learning model using genome-wide mutational profiles combined with other features and followed by CT imaging detected >90% of patients with lung cancer, including those with stage I and II disease. The fixed model was validated in an independent cohort, detected patients with cancer earlier than standard approaches and could be used to monitor response to therapy. This approach lays the groundwork for non-invasive cancer detection using genome-wide mutation features that may facilitate cancer screening and monitoring.

Improving lung cancer diagnosis with cancer, fungal, and imaging biomarkers.

OBJECTIVE: Indeterminate pulmonary nodules (IPNs) represent a significant diagnostic burden in health care. We aimed to compare a combination clinical prediction model (Mayo Clinic model), fungal (histoplasmosis serology), imaging (computed tomography [CT] radiomics), and cancer (high-sensitivity cytokeratin fraction 21; hsCYFRA 21-1) biomarker approach to a validated prediction model in diagnosing lung cancer. METHODS: A prospective specimen collection, retrospective blinded evaluation study was performed in 3 independent cohorts with 6- to 30-mm IPNs (n = 281). Serum histoplasmosis immunoglobulin G and immunoglobulin M antibodies and hsCYFRA 21-1 levels were measured and a validated CT radiomic score was calculated. Multivariable logistic regression models were estimated with Mayo Clinic model variables, histoplasmosis antibody levels, CT radiomic score, and hsCYFRA 21-1. Diagnostic performance of the combination model was compared with that of the Mayo Clinic model. Bias-corrected clinical net reclassification index (cNRI) was used to estimate the clinical utility of a combination biomarker approach. RESULTS: A total of 281 patients were included (111 from a histoplasmosis-endemic region). The combination biomarker model including the Mayo Clinic model score, histoplasmosis antibody levels, radiomics, and hsCYFRA 21-1 level showed improved diagnostic accuracy for IPNs compared with the Mayo Clinic model alone with an area under the receiver operating characteristics curve of 0.80 (95% CI, 0.76-0.84) versus 0.72 (95% CI, 0.66-0.78). Use of this combination model correctly reclassified intermediate risk IPNs into low- or high-risk category (cNRI benign = 0.11 and cNRI malignant = 0.16). CONCLUSIONS: The addition of cancer, fungal, and imaging biomarkers improves the diagnostic accuracy for IPNs. Integrating a combination biomarker approach into the diagnostic algorithm of IPNs might decrease unnecessary invasive testing of benign nodules and reduce time to diagnosis for cancer.

Improving Lung Cancer Diagnosis with CT Radiomics and Serum Histoplasmosis Testing.

BACKGROUND: Indeterminate pulmonary nodules (IPN) are a diagnostic challenge in regions where pulmonary fungal disease and smoking prevalence are high. We aimed to determine the impact of a combined fungal and imaging biomarker approach compared with a validated prediction model (Mayo) to rule out benign disease and diagnose lung cancer. METHODS: Adults ages 40 to 90 years with 6-30 mm IPNs were included from four sites. Serum samples were tested for histoplasmosis IgG and IgM antibodies by enzyme immunoassay and a CT-based risk score was estimated from a validated radiomic model. Multivariable logistic regression models including Mayo score, radiomics score, and IgG and IgM histoplasmosis antibody levels were estimated. The areas under the ROC curves (AUC) of the models were compared among themselves and to Mayo. Bias-corrected clinical net reclassification index (cNRI) was estimated to assess clinical reclassification using a combined biomarker model. RESULTS: We included 327 patients; 157 from histoplasmosis-endemic regions. The combined biomarker model including radiomics, histoplasmosis serology, and Mayo score demonstrated improved diagnostic accuracy when endemic histoplasmosis was accounted for [AUC, 0.84; 95% confidence interval (CI), 0.79-0.88; P < 0.0001 compared with 0.73; 95% CI, 0.67-0.78 for Mayo]. The combined model demonstrated improved reclassification with cNRI of 0.18 among malignant nodules. CONCLUSIONS: Fungal and imaging biomarkers may improve diagnostic accuracy and meaningfully reclassify IPNs. The endemic prevalence of histoplasmosis and cancer impact model performance when using disease related biomarkers. IMPACT: Integrating a combined biomarker approach into the diagnostic algorithm of IPNs could decrease time to diagnosis.

Bronchial gene expression alterations associated with radiological bronchiectasis.

OBJECTIVES: Discovering airway gene expression alterations associated with radiological bronchiectasis may improve the understanding of the pathobiology of early-stage bronchiectasis. METHODS: Presence of radiological bronchiectasis in 173 individuals without a clinical diagnosis of bronchiectasis was evaluated. Bronchial brushings from these individuals were transcriptomically profiled and analysed. Single-cell deconvolution was performed to estimate changes in cellular landscape that may be associated with early disease progression. RESULTS: 20 participants have widespread radiological bronchiectasis (three or more lobes). Transcriptomic analysis reflects biological processes associated with bronchiectasis including decreased expression of genes involved in cell adhesion and increased expression of genes involved in inflammatory pathways (655 genes, false discovery rate <0.1, log2 fold-change >0.25). Deconvolution analysis suggests that radiological bronchiectasis is associated with an increased proportion of ciliated and deuterosomal cells, and a decreased proportion of basal cells. Gene expression patterns separated participants into three clusters: normal, intermediate and bronchiectatic. The bronchiectatic cluster was enriched by participants with more lobes of radiological bronchiectasis (p<0.0001), more symptoms (p=0.002), higher SERPINA1 mutation rates (p=0.03) and higher computed tomography derived bronchiectasis scores (p<0.0001). CONCLUSIONS: Genes involved in cell adhesion, Wnt signalling, ciliogenesis and interferon-γ pathways had altered expression in the bronchus of participants with widespread radiological bronchiectasis, possibly associated with decreased basal and increased ciliated cells. This gene expression pattern is not only highly enriched among individuals with radiological bronchiectasis, but also associated with airway-related symptoms in those without discernible radiological bronchiectasis, suggesting that it reflects a bronchiectasis-associated, but non-bronchiectasis-specific lung pathophysiological process.

Smoking modulates different secretory subpopulations expressing SARS-CoV-2 entry genes in the nasal and bronchial airways.

SARS-CoV-2 infection and disease severity are influenced by viral entry (VE) gene expression patterns in the airway epithelium. The similarities and differences of VE gene expression (ACE2, TMPRSS2, and CTSL) across nasal and bronchial compartments have not been fully characterized using matched samples from large cohorts. Gene expression data from 793 nasal and 1673 bronchial brushes obtained from individuals participating in lung cancer screening or diagnostic workup revealed that smoking status (current versus former) was the only clinical factor significantly and reproducibly associated with VE gene expression. The expression of ACE2 and TMPRSS2 was higher in smokers in the bronchus but not in the nose. scRNA-seq of nasal brushings indicated that ACE2 co-expressed genes were highly expressed in club and C15orf48+ secretory cells while TMPRSS2 co-expressed genes were highly expressed in keratinizing epithelial cells. In contrast, these ACE2 and TMPRSS2 modules were highly expressed in goblet cells in scRNA-seq from bronchial brushings. Cell-type deconvolution of the gene expression data confirmed that smoking increased the abundance of several secretory cell populations in the bronchus, but only goblet cells in the nose. The association of ACE2 and TMPRSS2 with smoking in the bronchus is due to their high expression in goblet cells which increase in abundance in current smoker airways. In contrast, in the nose, these genes are not predominantly expressed in cell populations modulated by smoking. In individuals with elevated lung cancer risk, smoking-induced VE gene expression changes in the nose likely have minimal impact on SARS-CoV-2 infection, but in the bronchus, smoking may lead to higher viral loads and more severe disease.

Transfer Learning for Automated COVID-19 B-Line Classification in Lung Ultrasound.

Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective was to develop automated methods to classify B-lines as pathologic vs. normal. We developed transfer learning models based on ResNet networks to classify B-lines as pathologic (at least 3 B-lines per lung field) vs. normal using COVID-19 LUS data. Assessment of B-line severity on a 0-4 multi-class scale was also explored. For binary B-line classification, at the frame-level, all ResNet models pretrained with ImageNet yielded higher performance than the baseline nonpretrained ResNet-18. Pretrained ResNet-18 has the best Equal Error Rate (EER) of 9.1% vs the baseline of 11.9%. At the clip-level, all pretrained network models resulted in better Cohen's kappa agreement (linear-weighted) and clip score accuracy, with the pretrained ResNet-18 having the best Cohen's kappa of 0.815 [95% CI: 0.804-0.826], and ResNet-101 the best clip scoring accuracy of 93.6%. Similar results were shown for multi-class scoring, where pretrained network models outperformed the baseline model. A class activation map is also presented to guide clinicians in interpreting LUS findings. Future work aims to further improve the multi-class assessment for severity of B-lines with a more diverse LUS dataset.

A Soft Robot for Peripheral Lung Cancer Diagnosis and Therapy.

Lung cancer is one of the deadliest forms of cancers and is often diagnosed by performing biopsies with the use of a bronchoscope. However, this diagnostic procedure is limited in ability to explore deep into the periphery of the lung where cancer can remain undetected. In this study, we present design, modeling, fabrication, and testing of a one degree of freedom soft robot with integrated diagnostic and interventional capabilities as well as vision sensing. The robot can be deployed through the working channel of commercial bronchoscopes or used as a stand-alone system as it integrates a micro camera to provide vision sensing and controls to the periphery of the lung. The small diameter (2.4 mm) of the device allows navigation in branches deeper in the lung, where current devices have limited reachability. We have performed mechanical characterizations of the robotic platform, including blocked force, maximum bending angle, maximum angular velocity, and workspace, and assessed its performance in in vitro and ex vivo experiments. We have developed a computer vision algorithm, and validated it in in vitro conditions, to autonomously align the robot to a selected branch of the lung and aid the clinician (by means of a graphical user interface) during navigation tasks and to perform robot-assisted stabilization in front of a lesion, with automated tracking and alignment.

Smoking Modulates Different Secretory Subpopulations Expressing SARS-CoV-2 Entry Genes in the Nasal and Bronchial Airways.

Background : SARS-CoV-2 infection and disease severity are influenced by viral entry (VE) gene expression patterns in airway epithelium. The similarities and differences of VE gene expression (ACE2, TMPRSS2, and CTSL) across nasal and bronchial compartments has not been fully characterized using matched samples from large cohorts. Results : Gene expression data from 793 nasal and 1,673 bronchial brushes obtained from individuals participating in lung cancer screening or diagnostic workup revealed that smoking was the only clinical factor significantly and reproducibly associated with VE gene expression. ACE2 and TMPRSS2 expression were higher in smokers in the bronchus but not in the nose. scRNA-seq of nasal brushings indicated that ACE2 co-expressed genes were highly expressed in club and C15orf48 + secretory cells while TMPRSS2 co-expressed genes were highly expressed in keratinizing epithelial cells. In contrast, these ACE2 and TMPRSS2 modules were highly expressed in goblet cells in scRNA-seq from bronchial brushings. Cell-type deconvolution of the RNA-seq confirmed that smoking increased the abundance of several secretory cell populations in the bronchus, but only goblet cells in the nose. Conclusions : The association of ACE2 and TMPRSS2 with smoking in the bronchus is due to their high expression in goblet cells which increase in abundance in current smoker airways. In contrast, in the nose these genes are not predominantly expressed in cell populations modulated by smoking. Smoking-induced VE gene expression changes in the nose likely has minimal impact on SARS-CoV-2 infection, but in the bronchus, smoking may lead to higher viral loads and more severe disease.

A simple assessment of lung nodule location for reduction in unnecessary invasive procedures.

BACKGROUND: CT screening for lung cancer results in a significant mortality reduction but is complicated by invasive procedures performed for evaluation of the many detected benign nodules. The purpose of this study was to evaluate measures of nodule location within the lung as predictors of malignancy. METHODS: We analyzed images and data from 3,483 participants in the National Lung Screening Trial (NLST). All nodules (4-20 mm) were characterized by 3D geospatial location using a Cartesian coordinate system and evaluated in logistic regression analysis. Model development and probability cutpoint selection was performed in the NLST testing set. The Geospatial test was then validated in the NLST testing set, and subsequently replicated in a new cohort of 147 participants from The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium. RESULTS: The Geospatial Test, consisting of the superior-inferior distance (Z distance), nodule diameter, and radial distance (carina to nodule) performed well in both the NLST validation set (AUC 0.85) and the DECAMP replication cohort (AUC 0.75). A negative Geospatial Test resulted in a less than 2% risk of cancer across all nodule diameters. The Geospatial Test correctly reclassified 19.7% of indeterminate nodules with a diameter over 6mm as benign, while only incorrectly classifying 1% of cancerous nodules as benign. In contrast, the parsimonious Brock Model applied to the same group of nodules correctly reclassified 64.5% of indeterminate nodules as benign but resulted in misclassification of a cancer as benign in 18.2% of the cases. Applying the Geospatial test would result in reducing invasive procedures performed for benign lesions by 11.3% with a low rate of misclassification (1.3%). In contrast, the Brock model applied to the same group of patients results in decreasing invasive procedures for benign lesion by 39.0% but misclassifying 21.1% of cancers as benign. CONCLUSIONS: Utilizing information about geospatial location within the lung improves risk assessment for indeterminate lung nodules and may reduce unnecessary procedures. TRIAL REGISTRATION: NCT00047385, NCT01785342.

Evaluation and management of pleural sepsis.

Pleural sepsis stems from an infection within the pleural space typically from an underlying bacterial pneumonia leading to development of a parapneumonic effusion. This effusion is traditionally divided into uncomplicated, complicated, and empyema. Poor clinical outcomes and increased mortality can be associated with the development of parapneumonic effusions, reinforcing the importance of early recognition and diagnosis. Management necessitates a multimodal therapeutic strategy consisting of antimicrobials, catheter/tube thoracostomy, and at times, video-assisted thoracoscopic surgery.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Polymicrobial empyema; a novel case of Actinomyces turicensis.

Empyema or infection of the pleural space is a well described complication of pneumonia, however knowledge of culprit pathogens is still evolving. We report a novel case of empyema due to Actinomyces turicensis, a commensal of the oropharynx and female urogenital tract but previously undescribed cause of empyema. We additionally review general pathogenesis of Actinomyces species within the pleural space. Familiarity with this unique pleural infection pathogen is important for selection of adequate antimicrobial therapy given the propensity of anaerobes such as Actinomyces species to disobey anatomic boundaries and recrudescence of infection in the absence of appropriate therapy.

Distinguishing Smoking-Related Lung Disease Phenotypes Via Imaging and Molecular Features.

BACKGROUND: Chronic tobacco smoke exposure results in a broad range of lung pathologies including emphysema, airway disease and parenchymal fibrosis as well as a multitude of extra-pulmonary comorbidities. Prior work using CT imaging has identified several clinically relevant subgroups of smoking related lung disease, but these investigations have generally lacked organ specific molecular correlates. RESEARCH QUESTION: Can CT imaging be used to identify clinical phenotypes of smoking related lung disease that have specific bronchial epithelial gene expression patterns to better understand disease pathogenesis? STUDY DESIGN AND METHODS: Using K-means clustering, we clustered participants from the COPDGene study (n = 5,273) based on CT imaging characteristics and then evaluated their clinical phenotypes. These clusters were replicated in the Detection of Early Lung Cancer Among Military Personnel (DECAMP) cohort (n = 360), and were further characterized using bronchial epithelial gene expression. RESULTS: Three clusters (preserved, interstitial predominant and emphysema predominant) were identified. Compared to the preserved cluster, the interstitial and emphysema clusters had worse lung function, exercise capacity and quality of life. In longitudinal follow-up, individuals from the emphysema group had greater declines in exercise capacity and lung function, more emphysema, more exacerbations, and higher mortality. Similarly, genes involved in inflammatory pathways (tumor necrosis factor-α, interferon-ß) are more highly expressed in bronchial epithelial cells from individuals in the emphysema cluster, while genes associated with T-cell related biology are decreased in these samples. Samples from individuals in the interstitial cluster generally had intermediate levels of expression of these genes. INTERPRETATION: Using quantitative CT imaging, we identified three groups of individuals in older ever-smokers that replicate in two cohorts. Airway gene expression differences between the three groups suggests increased levels of inflammation in the most severe clinical phenotype, possibly mediated by the tumor necrosis factor-α and interferon-ß pathways. CLINICAL TRIAL REGISTRATION: COPDGene (NCT00608764), DECAMP-1 (NCT01785342), DECAMP-2 (NCT02504697).

A chocolate effusion - An unusual cause of elevated adenosine deaminase in the pleural fluid.

BACKGROUND: Thoracic Endometriosis Syndrome (TES) is a rare diagnosis characterized by ectopic endometrial tissue in the chest. Pleural fluid adenosine deaminase (ADA) is thought to be highly specific for tuberculous pleural effusions, particularly when >40 IU/L (international units/liter). RESULTS: A 36-year-old woman from Cameroon (immigrated 10 years ago) with no past medical history presented to the emergency department with increasing abdominal swelling over months found to have on imaging ascites, a left adnexal lesion, a large right-sided pleural effusion and peritoneal studding. Sampling of the pleural fluid revealed dark brown fluid which on analysis was a non-specific exudate with an adenosine deaminase >100. Exploratory laparotomy by gynecology-oncology revealed a large amount of hemorrhagic ascites, multiple endometriotic implants, and a right ovarian endometrioma. Ultimately the patient was taken for video-assisted thoracoscopy (VATS) and decortication. The VATS revealed a diaphragmatic tear was seen suggesting the etiology of the pleural fluid was trans-diaphragmatic passage of blood through the defect. There was no evidence of malignancy or granulomas. Stains and subsequent cultures were negative on all specimens for Mycobacterium tuberculosis. DISCUSSION: Our case demonstrates a rarity of an ADA >100 IU/L due to TES rather than tuberculosis. In conclusion, ADA analysis, as with any lab test, should be interpreted within clinical context as false positives may occur. Several weeks following presentation the patient was discharged without any intrapleural catheter and near complete expansion of the lung. She was started on leuprolide and medroxyprogesterone and has no recurrent effusion or ascites in over two years since initial presentation.

Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts.

BACKGROUND: Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. METHODS: In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates - age, pack-years, inhaled medication use, and specimen collection timing. RESULTS: In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37-53%) and a sensitivity of 91% (95%CI 81-97%), resulting in a negative predictive value of 95% (95% CI 89-98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44-82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78-97%). CONCLUSIONS: The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.

Detection of early lung cancer among military personnel (DECAMP) consortium: study protocols.

BACKGROUND: Lung cancer is the leading cause of cancer-related death due in large part to our inability to diagnose it at an early and potentially curable stage. Screening for lung cancer via low dose computed tomographic (LDCT) imaging has been demonstrated to improve mortality but also results in a high rate of false positive tests. The identification and application of non-invasive molecular biomarkers that improve the performance of CT imaging for the detection of lung cancer in high risk individuals would aid in clinical decision-making, eliminate the need for unnecessary LDCT follow-up, and further refine the screening criteria for an already large high-risk population. METHODS: The Detection of Early Lung Cancer Among Military Personnel (DECAMP) consortium is conducting two multicenter prospective studies with the goals of developing an integrated panel of both airway and blood-based molecular biomarkers that discriminate benign and malignant indeterminate nodules detected on CT scan as well as predict the future development of lung cancer in high-risk individuals. To achieve these goals, DECAMP is compiling an extensive array of biospecimens including nasal brushings, serum, plasma and intrathoracic airway samples (bronchial brushings and bronchial biopsies) from normal-appearing airway epithelium. DISCUSSION: This bank of samples is the foundation for multiple DECAMP efforts focused on the identification of those at greatest risk of developing lung cancer as well as the discrimination of benign and malignant pulmonary nodules. The clinical, imaging and biospecimen repositories will serve as a resource for the biomedical community and their investigation of the molecular basis of chronic respiratory disease. TRIAL REGISTRATION: Retrospectively registered as NCT01785342 - DECAMP-1: Diagnosis and Surveillance of Indeterminate Pulmonary Nodules (DECAMP-1). Date of Registration: February 7, 2013. Retrospectively registered as NCT02504697 - DECAMP-2: Screening of Patients With Early Stage Lung Cancer or at High Risk for Developing Lung Cancer (DECAMP-2). Date of Registration: July 22, 2015.