lung classification

BACKGROUND: Acute respiratory distress syndrome (ARDS) is responsible for 400,000 deaths annually worldwide. Few improvements have been made despite five decades of research, partially because ARDS is a highly heterogeneous syndrome including various types of aetiologies. Lower airway microbiota is involved in chronic inflammatory diseases and recent data suggest that it could also play a role in ARDS. Nevertheless, whether the lower airway microbiota composition varies between the aetiologies of ARDS remain unknown...

BACKGROUND: The immune status of a patient's tumor microenvironment (TME) may guide therapeutic interventions with cancer immunotherapy and help identify potential resistance mechanisms. Currently, patients' immune status is mostly classified based on CD8+tumor-infiltrating lymphocytes. An unmet need exists for comparable and reliable precision immunophenotyping tools that would facilitate clinical treatment-relevant decision-making and the understanding of how to overcome resistance mechanisms...

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung...

PURPOSES: This study aimed to develop and validate algorithms for automating intensity modulated radiation therapy (IMRT) planning in breast cancer patients, with a focus on patient anatomical characteristics. MATERIAL AND METHODS: We retrospectively selected 400 breast cancer patients without lymph node involvement for automated treatment planning. Automation was achieved using the Eclipse Scripting Application Programming Interface (ESAPI) integrated into the Eclipse Treatment Planning System...

Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas...

BACKGROUND: Race is a potent influencer of healthcare access. Geography and income may exert equal or greater influence on patient outcomes. We sought to define the intersection of race, rurality, and income and their influence on access to minimally invasive lung surgery in Medicare beneficiaries. METHODS: Medicare and Medicaid Services data were used to evaluate patients with lung cancer who underwent right upper lobectomy, via open, robotic-assisted (RATS), or video-assisted thoracic surgery (VATS) between 2018 and 2020...

BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise...

PURPOSE: To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT). MATERIALS AND METHODS: We collected a data set of 650 nodules (316 acute and 334 chronic) from the CT scans of 110 patients with NTM-LD. The data set was divided into training, validation, and test sets in a ratio of 4:1:1. Bounding boxes were used to crop the 2D CT images down to the area of interest...

Trilaciclib is a first-in-class, intravenous cyclin-dependent kinase 4 and 6 inhibitor approved for reducing the incidence of chemotherapy-induced myelosuppression in adult patients with extensive-stage small cell lung cancer receiving a platinum/etoposide-containing or topotecan-containing regimen. No dose adjustment is recommended for participants with mild hepatic impairment (HI) based on previous population pharmacokinetic (PK) analysis. This open-label, parallel-group study examined the impact of moderate and severe HI on the PK of trilaciclib...

INTRODUCTION: Major histocompatibility complex class II molecule (MHC-II) is pivotal in anti-tumor immunity, and targeting MHC-II in tumors may help improve patient survival. But function of MHC-II in the immunotherapy and prognosis of lung adenocarcinoma (LUAD) patients has not been thoroughly studied and reported. METHODS: We selected LUAD-related MHC-II genes from public databases based on previous literature reports. We identified different subtypes according to expression differences of these genes in different LUAD samples through cluster analysis...

Surfactant protein A (SP-A) and Surfactant protein D (SP-D) glycoproteins play a crucial role in maintaining lung homeostasis and lung host defense. Interestingly, these proteins are also expressed in extra-pulmonary tissues, including the female genital tract. The ovarian tissue, where SP-A and SP-D expression increases with follicular development, may serve as the primary site of defense for this tissue. However, their functions in these tissues are not well understood and are currently an active area of research...

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care...

BACKGROUND: Interstitial lung disease (ILD) is a common manifestation of idiopathic inflammatory myopathies (IIM) and a substantial contributor to hospitalisation, increased morbidity, and mortality. In-vivo evidence of ongoing tissue remodelling in IIM-ILD is scarce. We aimed to evaluate fibroblast activation in lungs of IIM-patients and control individuals using ⁶⁸Ga-labelled inhibitor of Fibroblast-Activation-Protein (FAPi) based positronic emission tomography and computed tomography imaging (PET/CT)...

BACKGROUND: Transcriptomic subtyping holds promise for personalized therapy in extensive-stage small-cell lung cancer (ES-SCLC). In this study, we aimed to assess intratumoral transcriptomic subtype diversity and to identify biomarkers of long-term chemoimmunotherapy benefit in human ES-SCLC. PATIENTS AND METHODS: We analyzed tumor samples from 58 ES-SCLC patients enrolled in two multicenter single-arm phase IIIb studies evaluating front-line chemoimmunotherapy in Spain: n=32 from the IMfirst trial, and n=26 from the CANTABRICO trial...

BACKGROUND: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research. PURPOSE: The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms...

UNLABELLED: OObjective: Prognostic models aid clinical practice with decision-making on treatment and hospitalization in exacerbation of chronic obstructive lung disease (ECOPD). Although there are many studies with prognostic models, diagnostic accuracy is variable within and between models. SUBJECTS AND METHODS: We compared prognostic performance of the BAP65 score, DECAF score, PEARL score and modified early warning score (MEWS) in hospitalized patients with ECOPD, to estimate ventilatory support need...

BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma...

BACKGROUND: This study aims to investigate the application value of serum cytokeratin 19 fragment (CYFRA21-1) combined with nerve-specific enolase (NSE), carcinoembryonic antigen (CEA), and squamous cell carcinoma antigen (SCC-Ag) in the diagnosis of lung cancer (LC). METHODS: A total of 831 cases of LC, 360 cases of benign lung disease (BLD) and 102 healthy controls, were enrolled. The data were processed using SPSS, GraphPad Prism, and MedCalc software. RESULTS: The tumor marker (TM) levels in the LC and BLD groups were significantly higher than those in the control group; the CYFRA21-1, NSE, and CEA levels in the patients with LC were higher than in those with BLD...

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts...

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model...