Prevalence of Airflow Limitation and Emphysema in Lung Cancer Screening Populations: A Systematic Review and Meta-analysis.

Background: Early detection and treatment of chronic obstructive pulmonary disease (COPD) are becoming important for improving the prognosis of individuals who smoke heavily. Despite the higher risk of COPD among individuals participating in lung cancer screening, many of these patients remain underdiagnosed.

Research Questions: How many participants in lung cancer screening have emphysema or airflow limitation? If spirometry is incorporated into the screening, how many additional patients with airflow limitation could be newly identified?

Study Design And Methods: The Ovid-MEDLINE and Embase databases were searched from inception to November 30, 2023.

AI Applications for Thoracic Imaging: Considerations for Best Practice.

Artificial intelligence (AI) technology is rapidly being introduced into thoracic radiology practice. Current representative use cases for AI in thoracic imaging show cumulative evidence of effectiveness. These include AI assistance for reading chest radiographs and low-dose (1.

Diagnostic Performance of the Modified Lung CT Screening Reporting and Data System in a TB-Endemic Country: The Korean National Lung Cancer Screening Program.

Background: In 2019, Korea initiated the world's first national low-dose CT imaging lung cancer screening (LCS) program, adapting the Lung CT Screening Reporting and Data System (Lung-RADS) to counteract the high false-positive rates driven by prevalent TB.

Research Question: Does the modified Lung-RADS enhance screening specificity while maintaining sensitivity?

Study Design And Methods: This nationwide, retrospective cohort study included high-risk individuals 54 to 74 years of age with active tobacco use of at least 30 pack-years participating in the national LCS program from 2019 through 2020. The modified Lung-RADS 1.

Optimizing prone CT use for suspected interstitial lung abnormalities.

Objectives: We investigated whether supine chest CT alone suffices for diagnosing ILAs, thereby reducing the need for prone chest CT.

Materials And Methods: Patients who underwent prone chest CT for suspected ILAs from January 2021 to July 2023, with matching supine CT within 1 year, were retrospectively evaluated. Five multinational thoracic radiologists independently rated ILA suspicion and fibrosis scores (1 to 5-point) and ILA extent (1-100%) using supine CT first, then combined supine-prone CT after a 1-month washout.

Low-Dose CT Screening in East Asian Women Who Have Never Smoked: Association between Family History of Lung Cancer and Ground-Glass Nodule Prevalence and Growth.

Background Characteristics of ground-glass nodules (GGNs) in Asian women who have never smoked with family history of lung cancer (FHLC) remain unexamined. Purpose To investigate the differences in GGN progression to lung cancer at low-dose CT (LDCT) screening between Asian women who have never smoked with and without FHLC, and to examine associations between FHLC and GGN prevalence and growth. Materials and Methods This single-center retrospective study included East Asian women who had never smoked and had no personal history of lung cancer who underwent baseline LDCT for a health checkup between January 2011 and December 2015.

Clinical Utility of a CT-based AI Prognostic Model for Segmentectomy in Non-Small Cell Lung Cancer.

Background Currently, no tool exists for risk stratification in patients undergoing segmentectomy for non-small cell lung cancer (NSCLC). Purpose To develop and validate a deep learning (DL) prognostic model using preoperative CT scans and clinical and radiologic information for risk stratification in patients with clinical stage IA NSCLC undergoing segmentectomy. Materials and Methods In this single-center retrospective study, transfer learning of a pretrained model was performed for survival prediction in patients with clinical stage IA NSCLC who underwent lobectomy from January 2008 to March 2017.

Measurement Variability of Same-Day CT Quantification of Interstitial Lung Disease: A Multicenter Prospective Study.

Purpose To investigate quantitative CT (QCT) measurement variability in interstitial lung disease (ILD) on the basis of two same-day CT scans. Materials and Methods Participants with ILD were enrolled in this multicenter prospective study between March and October 2022. Participants underwent two same-day CT scans at an interval of a few minutes.

Association between body fat decrease during the first year after diagnosis and the prognosis of idiopathic pulmonary fibrosis: CT-based body composition analysis.

Background: The prognostic role of changes in body fat in patients with idiopathic pulmonary fibrosis (IPF) remains underexplored. We investigated the association between changes in body fat during the first year post-diagnosis and outcomes in patients with IPF.

Methods: This single-center, retrospective study included IPF patients with chest CT scan and pulmonary function test (PFT) at diagnosis and a one-year follow-up between January 2010 and December 2020.

Fleischner Society: Glossary of Terms for Thoracic Imaging.

Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition.

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

Purpose: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR).

Methods: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women).

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

Objectives: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data.

Methods: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected.

Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach.

Objectives: To evaluate the relationship of changes in the deep learning-based CT quantification of interstitial lung disease (ILD) with changes in forced vital capacity (FVC) and visual assessments of ILD progression, and to investigate their prognostic implications.

Methods: This study included ILD patients with CT scans at intervals of over 2 years between January 2015 and June 2021. Deep learning-based texture analysis software was used to segment ILD findings on CT images (fibrosis: reticular opacity + honeycombing cysts; total ILD extent: ground-glass opacity + fibrosis).

Usual Interstitial Pneumonia: Associations With Complications After Percutaneous Transthoracic Needle Lung Biopsy.

Changes in lung parenchyma elasticity in usual interstitial pneumonia (UIP) may increase the risk for complications after percutaneous transthoracic needle biopsy (PTNB) of the lung. The purpose of this article was to investigate the association of UIP findings on CT with complications after PTNB, including pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis. This retrospective single-center study included 4187 patients (mean age, 63.

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.

Objectives: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model.

Methods: DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes.

Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.

Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. The purpose of our study was to evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) system for detecting ETT presence and position on chest radiographs in three patient samples from two different institutions. This retrospective study included 539 chest radiographs obtained immediately after ETT insertion from January 1 to March 31, 2020, in 505 patients (293 men, 212 women; mean age, 63 years) from institution A (sample A); 637 chest radiographs obtained from January 1 to January 3, 2020, in 302 patients (157 men, 145 women; mean age, 66 years) in the ICU (with or without an ETT) from institution A (sample B); and 546 chest radiographs obtained from January 1 to January 20, 2020, in 83 patients (54 men, 29 women; mean age, 70 years) in the ICU (with or without an ETT) from institution B (sample C).

75% radiation dose reduction using deep learning reconstruction on low-dose chest CT.

Objective: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR).

Materials And Methods: We retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio.

Prognostic value of CT-defined ground-glass opacity in early-stage lung adenocarcinomas: a single-center study and meta-analysis.

Objectives: The prognostic value of ground-glass opacity at preoperative chest CT scans in early-stage lung adenocarcinomas is a matter of debate. We aimed to clarify the existing evidence through a single-center, retrospective cohort study and to quantitatively summarize the body of literature by conducting a meta-analysis.

Methods: In a retrospective cohort study, patients with clinical stage I lung adenocarcinoma were identified, and the prognostic value of ground-glass opacity was analyzed using multivariable Cox regression.

Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic.

Objective: The clinical impact of artificial intelligence-based computer-aided detection (AI-CAD) beyond diagnostic accuracy remains uncertain. We aimed to investigate the influence of the clinical implementation of AI-CAD for chest radiograph (CR) interpretation in daily practice on the rate of referral for chest computed tomography (CT).

Materials And Methods: AI-CAD was implemented in clinical practice at the Seoul National University Hospital.

Correction: Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/42717.].

Strategies to Improve Smoking Cessation for Participants in Lung Cancer Screening Program: Analysis of Factors Associated with Smoking Cessation in Korean Lung Cancer Screening Project (K-LUCAS).

Purpose: Smoking cessation intervention is one of the key components of successful lung cancer screening program. We investigated the effectiveness and related factors of smoking cessation services provided to the participants in a population-based lung cancer screening trial.

Materials And Methods: The Korean Lung Cancer Screening Project (K-LUCAS) is a nationwide, multi-center lung cancer screening trial that evaluates the feasibility of implementing population-based lung cancer screening.