Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath.

BACKGROUND: Pathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity. METHODS: Exhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe). RESULTS: The proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity. CONCLUSIONS: Exhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.

Evaluation of Donor Lungs for Transplantation: The Efficacy of Screening Bronchoscopy for Detecting Donor Aspiration and Its Relationship to the Resulting Allograft Function in Corresponding Recipients.

BACKGROUND: Potential organ donors often have suffered anoxic and/or traumatic brain injury during which they may have experienced aspiration of gastric material (AGM). Evaluation of such donors typically includes a screening bronchoscopic examination during which determinations of aspiration are made. The efficacy of this visual screening and its relationship to post-transplant allograft function are unknown. METHODS: Before procurement, bronchoscopy was performed on donors in which both bronchoalveolar lavage fluid (BALF) was collected and a visual inspection made. As a marker of AGM, BALF specimens were analyzed for the presence of bile salts. Data collected on the corresponding recipients included primary graft dysfunction (PGD) score, post-transplant spirometry, acute rejection scores (ARS), and overall survival. RESULTS: Of 31 donors evaluated, bronchoscopies revealed only 2 with visual evidence of AGM, whereas BALF analysis for bile salts indicated AGM in 14. As such, screening bronchoscopy had a sensitivity of only 7.1%. Visual detection of AGM via bronchoscopy was not associated with any resulting grade of PGD (χ2 = 2.96, P = .23); however, AGM defined by detection of bile salts was associated (χ2 = 7.56, P = .02). Over the first post-transplant year, the corresponding recipients experienced a similar improvement in allograft function (χ2 = 1.63, P = .69), ARS (P = .69), and survival (P = .24). CONCLUSION: Visual inspection during a single bronchoscopic examination of lung donors underestimates the prevalence of AGM. The detection of bile salts in donor BALF is associated with early allograft dysfunction in the corresponding recipients but not with later allograft proficiency, acute rejection responses, or 1-year post-transplant survival.

Veno-venous extracorporeal membrane oxygenation for the treatment of respiratory compromise.

Extracorporeal membrane oxygenation for the purpose of intervening upon profound cardiovascular or pulmonary compromise has proven to be a worthy intervention. Technological advancements have allowed this mode of therapy to become more effective and widespread. Veno-venous extracorporeal membrane oxygenation (VV-ECMO) is a commonly used strategy to help manage patients with pulmonary dysfunction refractory to traditional management methods. This review intends to focus upon common indications and the clinical considerations for the institution of VV-ECMO as well as some of its known complications.

Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling.

The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.

Minimally Invasive Esophagectomy Is Associated with Superior Survival Compared to Open Surgery.

INTRODUCTION: Minimally invasive esophagectomy (MIE) has not been associated with a long-term survival advantage compared to open esophagectomy (OE). We investigated survival differences between MIE, including laparoscopic and robotic, and OE. METHODS: Patients undergoing esophagectomy from 2010 to 2014 with T1-4N0-3M0, adenocarcinoma or squamous cell histology, in middle or lower esophagus were queried from the National Cancer Database and stratified into groups based on their surgical procedure: robotic, laparoscopic, or OE. Propensity matching (1:1) was done between robotic and laparoscopic to produce an MIE group. The MIE group was matched to OE yielding a 1:1:2 matching of robotic:laparoscopic:OE. Postoperative outcomes and survival (Kaplan-Meier) were compared between groups. RESULTS: Prior to matching, 7,163 patients met inclusion criteria and a greater portion underwent OE (67.7%) than MIE (laparoscopic 24.9% and robotic 7.4%). Matching yielded similar groups (robotic = 527, laparoscopic = 527, and OE =1054). Compared to OE, MIE patients had a significantly greater number of nodes sampled and trended toward increased R0 resections (96.1% vs 94.3%, P = .053). OE was associated with a longer median postoperative stay (10 vs 9 days, P = .001). Mortality at 30 and 90 days was similar. However, postoperative survival for MIE was significantly greater than OE (P < .001). No survival difference existed between robotic and laparoscopic (P = .723). CONCLUSIONS: MIE is associated with increased number of nodes examined and a shorter postoperative length of stay. After propensity matching, patients undergoing MIE had better long but not short-term survival than OE. This benefit seems to be independent of the use of robotic technology.

The American Association for Thoracic Surgery (AATS) 2022 Expert Consensus Document: The use of mechanical circulatory support in lung transplantation.

OBJECTIVE: The use of mechanical circulatory support (MCS) in lung transplantation has been steadily increasing over the prior decade, with evolving strategies for incorporating support in the preoperative, intraoperative, and postoperative settings. There is significant practice variability in the use of these techniques, however, and relatively limited data to help establish institutional protocols. The objective of the AATS Clinical Practice Standards Committee (CPSC) expert panel was to review the existing literature and establish recommendations about the use of MCS before, during, and after lung transplantation. METHODS: The AATS CPSC assembled an expert panel of 16 lung transplantation physicians who developed a consensus document of recommendations. The panel was broken into subgroups focused on preoperative, intraoperative, and postoperative support, and each subgroup performed a focused literature review. These subgroups formulated recommendation statements for each subtopic, which were evaluated by the entire group. The statements were then developed via discussion among the panel and refined until consensus was achieved on each statement. RESULTS: The expert panel achieved consensus on 36 recommendations for how and when to use MCS in lung transplantation. These recommendations included the use of veno-venous extracorporeal membrane oxygenation (ECMO) as a bridging strategy in the preoperative setting, a preference for central veno-arterial ECMO over traditional cardiopulmonary bypass during the transplantation procedure, and the benefit of supporting selected patients with MCS postoperatively. CONCLUSIONS: Achieving optimal results in lung transplantation requires the use of a wide range of strategies. MCS provides an important mechanism for helping these critically ill patients through the peritransplantation period. Despite the complex nature of the decision making process in the treatment of these patients, the expert panel was able to achieve consensus on 36 recommendations. These recommendations should provide guidance for professionals involved in the care of end-stage lung disease patients considered for transplantation.

Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data.

INTRODUCTION: While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value. OBJECTIVES: Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies. METHODS: Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility. RESULTS: Overall survival (OS) is predicted in external validation cohort with AUROCTEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROCTEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased. CONCLUSION: This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.

Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival.

INTRODUCTION: Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive. OBJECTIVES: This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS. METHODS: Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events. RESULTS: Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates). CONCLUSION: Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.

Computer-Assisted Image Processing System for Early Assessment of Lung Nodule Malignancy.

Lung cancer is one of the most dreadful cancers, and its detection in the early stage is very important and challenging. This manuscript proposes a new computer-aided diagnosis system for lung cancer diagnosis from chest computed tomography scans. The proposed system extracts two different kinds of features, namely, appearance features and shape features. For the appearance features, a Histogram of oriented gradients, a Multi-view analytical Local Binary Pattern, and a Markov Gibbs Random Field are developed to give a good description of the lung nodule texture, which is one of the main distinguishing characteristics between benign and malignant nodules. For the shape features, Multi-view Peripheral Sum Curvature Scale Space, Spherical Harmonics Expansion, and a group of some fundamental morphological features are implemented to describe the outer contour complexity of the nodules, which is main factor in lung nodule diagnosis. Each feature is fed into a stacked auto-encoder followed by a soft-max classifier to generate the initial malignancy probability. Finally, all these probabilities are combined together and fed to the last network to give the final diagnosis. The system is validated using 727 nodules which are subset from the Lung Image Database Consortium (LIDC) dataset. The system shows very high performance measures and achieves 92.55%, 91.70%, and 93.40% for the accuracy, sensitivity, and specificity, respectively. This high performance shows the ability of the system to distinguish between the malignant and benign nodules precisely.

The profibrotic and senescence phenotype of old lung fibroblasts is reversed or ameliorated by genetic and pharmacological manipulation of Slc7a11 expression.

Increased senescence and expression of profibrotic genes in old lung fibroblasts contribute to disrepair responses. We reported that primary lung fibroblasts from old mice have lower expression and activity of the cystine transporter Slc7a11/xCT than cells from young mice, resulting in changes in both the intracellular and extracellular redox environments. This study examines the hypothesis that low Slc7a11 expression in old lung fibroblasts promotes senescence and profibrotic gene expression. The levels of mRNA and protein of Slc7a11, senescence markers, and profibrotic genes were measured in primary fibroblasts from the lungs of old (24 mo) and young (3 mo) mice. In addition, the effects of genetic and pharmacological manipulation of Slc7a11 were investigated. We found that decreased expression of Slc7a11 in old cells was associated with elevated markers of senescence (p21, p16, p53, and ß-galactosidase) and increased expression of profibrotic genes (Tgfb1, Smad3, Acta2, Fn1, Col1a1, and Col5a1). Silencing of Slc7a11 in young cells replicated the aging phenotype, whereas overexpression of Slc7a11 in old cells decreased expression of senescence and profibrotic genes. Young cells were induced to express the senescence and profibrotic phenotype by sulfasalazine, a Slc7a11 inhibitor, whereas treatment of old cells with sulforaphane, a Slc7a11 inducer, decreased senescence without affecting profibrotic genes. Like aging cells, idiopathic pulmonary fibrosis fibroblasts show decreased Slc7a11 expression and increased profibrotic markers. In short, old lung fibroblasts manifest a profibrotic and senescence phenotype that is modulated by genetic or pharmacological manipulation of Slc7a11.

COVID-19 and low-dose radiation therapy.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of the coronavirus disease 2019 (COVID-19), has caused more than 179 million infections and 3.8 million deaths worldwide. Global health authorities working on the COVID-19 outbreak continue to explore methods to reduce the rate of its transmission to healthy individuals. Treatment protocols thus far have focused on social distancing and masking, treatment with antivirals early in infection, and steroids to reduce the inflammatory response. An alternative approach is therapy with low dose radiation (LDR), which has several advantages compared to the current drugs and medicines. To date more than 10 case reports and pilot clinical trial preliminary outcome are available from different countries. These reports cover a wide range of patient conditions and LDR treatment strategies. Although one report showed the failure to observe the improvement of COVID-19 patients after LDR therapy, the majority showed some clinical improvement, and demonstrated the safety of LDR for COVID-19 patients, particularly with 0.5 â€‹Gy. This review aims to summarize the potential rationales and mechanisms of LDR therapy for COVID-19 patients, and its current clinical status and potential use.

Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data.

OBJECTIVES: Despite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results. This proof-of-concept study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies. MATERIALS AND METHODS: Samples (n = 36) were evaluated with high-resolution 2DLC-MS/MS and 13C-glucose enrichment, and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) in terms of first-line chemotherapy. Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests (RF)) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets. RESULTS: The models predicted patient classifications in the validation subsets with AUC (95 % CI): DC vs. PD (SVM), 0.970(0.961-0.979); CR/PR vs. SD/PD (PLS-DA), 0.880(0.865-0.895); stage I/II/III vs. IV (SVM), 0.902(0.880-0.924). Highest performing model was SVM for DC vs. PD (balanced accuracy = 0.92; kappa = 0.74). CONCLUSION: This study illustrates a comprehensive evaluation of patient tumor-specific metabolic profiles, with the potential to identify disease stage and predict response to first-line chemotherapy.

A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer.

This study presents a non-invasive, automated, clinical diagnostic system for early diagnosis of lung cancer that integrates imaging data from a single computed tomography scan and breath bio-markers obtained from a single exhaled breath to quickly and accurately classify lung nodules. CT imaging and breath volatile organic compounds data were collected from 47 patients. Spherical Harmonics-based shape features to quantify the shape complexity of the pulmonary nodules, 7th-Order Markov Gibbs Random Field based appearance model to describe the spatial non-homogeneities in the pulmonary nodule, and volumetric features (size) of pulmonary nodules were calculated from CT images. 27 VOCs in exhaled breath were captured by a micro-reactor approach and quantied using mass spectrometry. CT and breath markers were input into a deep-learning autoencoder classifier with a leave-one-subject-out cross validation for nodule classification. To mitigate the limitation of a small sample size and validate the methodology for individual markers, retrospective CT scans from 467 patients with 727 pulmonary nodules, and breath samples from 504 patients were analyzed. The CAD system achieved 97.8% accuracy, 97.3% sensitivity, 100% specificity, and 99.1% area under curve in classifying pulmonary nodules.

Intraoperative Esophagogastroduodenoscopy During Heller Myotomy: Evaluating Guidelines.

BACKGROUND: National guidelines suggest routine intraoperative esophagogastroduodenoscopy (EGD) during laparoscopic Heller myotomy (LHM) to assess for mucosal perforation and myotomy adequacy, but the utility of this is unknown. This study aimed to evaluate the effect of intraoperative EGD on outcomes after LHM. METHODS: Patients who underwent LHM in a single center were retrospectively identified. Outcomes were compared between patients who did and did not undergo intraoperative EGD. RESULTS: Sixty-one patients were reviewed: 46 (75%) underwent intraoperative EGD and 15 (25%) did not. Mucosal perforations occurred in 2 (4%) of the EGD group and 3 (20%) of the non-EGD group (p = 0.06). All perforations, regardless of EGD use, were recognized laparoscopically. There were no postoperative leaks. Failed myotomy occurred in 5 (11%) who underwent EGD and 1 (7%) who did not (p = 0.64). CONCLUSIONS: Because EGD does not appear to improve outcomes after LHM, we emphasize its selective, rather than routine, use.