Evaluating ChatGPT's performance across radiology subspecialties: A meta-analysis of board-style examination accuracy and variability.

Introduction: Large language models (LLMs) like ChatGPT are increasingly used in medicine due to their ability to synthesize information and support clinical decision-making. While prior research has evaluated ChatGPT's performance on medical board exams, limited data exist on radiology-specific exams especially considering prompt strategies and input modalities. This meta-analysis reviews ChatGPT's performance on radiology board-style questions, assessing accuracy across radiology subspecialties, prompt engineering methods, GPT model versions, and input modalities.

Application of a Growth-Rate Model to Enhance Subgroup Identification in Heterogeneous Clinical Courses of the Idiopathic Inflammatory Myopathy-Associated Interstitial Lung Disease and Its Prognostic Implication.

Background: Analyzing longitudinal real-world data with nonuniform study-time intervals is challenging. This study aimed to identify subgroups in heterogeneous clinical courses of idiopathic inflammatory myopathies-associated interstitial lung disease (IIM-ILD) using a growth rate model and to assess their prognostic significance.

Methods: In this retrospective cohort study, 243 chest high-resolution computed tomography (HRCT) scans from 80 patients with IIM-ILD were analyzed using a computer-aided quantification system to estimate quantitative lung fibrosis (QLF) scores.

Impact of quantitative radiological features of interstitial lung disease on immunomodulatory treatment response in three autoimmune interstitial lung disease cohorts.

Background: The defining radiological features of autoimmune interstitial lung disease (ILD) are ground glass opacification (GGO) and fibrosis. The associations between these features and physiological response to immunomodulation remain unclear.

Methods: This study leveraged three autoimmune ILD cohorts: two with systemic sclerosis (SSc) and one with rheumatoid arthritis (RA) which were selected for inherent differences in fibrotic extents/patterns.

Evaluating the robustness of deep learning models trained to diagnose idiopathic pulmonary fibrosis using a retrospective study.

Background: Deep learning (DL)-based systems have not yet been broadly implemented in clinical practice, in part due to unknown robustness across multiple imaging protocols.

Purpose: To this end, we aim to evaluate the performance of several previously developed DL-based models, which were trained to distinguish idiopathic pulmonary fibrosis (IPF) from non-IPF among interstitial lung disease (ILD) patients, under standardized reference CT imaging protocols. In this study, we utilized CT scans from non-IPF ILD subjects, acquired using various imaging protocols, to assess the model performance.

Extent of lung fibrosis is of greater prognostic importance than HRCT pattern in patients with progressive pulmonary fibrosis: data from the ILD-PRO registry.

Background: The prognostic value of patterns and quantitative measures of lung fibrosis on high-resolution computed tomography (HRCT) in patients identified as having progressive pulmonary fibrosis (PPF) has not been established. We investigated whether HRCT patterns and quantitative scores were associated with risk of progression in patients with PPF.

Methods: Patients enrolled in the ILD-PRO Registry had an interstitial lung disease (ILD) other than idiopathic pulmonary fibrosis, reticular abnormality and traction bronchiectasis, and met criteria for ILD progression.

Biological correlates of radiological features of systemic sclerosis interstitial lung disease.

Background And Objectives: The extent and pattern of radiological features ( fibrosis and ground glass) can influence treatment approaches for systemic sclerosis-related interstitial lung disease (SSc-ILD). However, the pathobiology underlying these radiological features is poorly understood and warrants further investigation.

Methods: 68 proteins were measured in bronchoalveolar lavage (BAL) fluid from 103 SSc-ILD participants in Scleroderma Lung Study I.

A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction.

. The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations..

Treatment Response Biomarkers for Systemic Sclerosis-Associated Interstitial Lung Disease.

Objective: This study investigated whether changes in circulating biomarkers predict progressive pulmonary fibrosis (PPF) in patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) receiving treatment.

Methods: Participants of the Scleroderma Lung Study II, which compared receiving mycophenolate mofetil (MMF) versus cyclophosphamide (CYC) for treating SSc-ILD, who had blood samples at baseline and 12 months were included. Levels for C-reactive protein (CRP), interleukin-6, C-X-C motif chemokine ligand (CXCL) 4, CCL18, and Krebs von den Lungen (KL)-6 were measured, and a logistic regression model evaluated relationships between changes in these biomarkers and the development of PPF by 24 months.

Bexotegrast in people with idiopathic pulmonary fibrosis (IPF): a plain language summary of publication of the INTEGRIS-IPF study.

This plain language summary shares results from a clinical study called INTEGRIS-IPF that was published in the in 2024. This study looked at a medicine called (beck-so-teh-grast) as a possible treatment for (i-dee-uh-pa-thick pul-muh-ner-ee fie-bro-sis; IPF). is an investigational medicine, which means that it is being studied and has not yet been approved by the US Food and Drug Administration (FDA), for people with IPF to take as a treatment.

Predicting Invasiveness in Lepidic Pattern Adenocarcinoma of Lung: Analysis of Visual Semantic and Radiomic Features.

Objectives: To differentiate invasive lepidic predominant adenocarcinoma (iLPA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) of lung utilizing visual semantic and computer-aided detection (CAD)-based texture features on subjects initially diagnosed as AIS or MIA with CT-guided biopsy.

Materials And Methods: From 2011 to 2017, all patients with CT-guided biopsy results of AIS or MIA who subsequently underwent resection were identified. CT scan before the biopsy was used to assess visual semantic and CAD texture features, totaling 23 semantic and 95 CAD-based quantitative texture variables.

Correlation of FAPI PET Uptake with Immunohistochemistry in Explanted Lungs from Patients with Advanced Interstitial Lung Disease.

Recent studies have demonstrated promising results of fibroblast activation protein (FAP) inhibitor (FAPI) PET in prognosticating and monitoring interstitial lung diseases (ILDs). As a first step toward successful translation, our primary aim was to validate the FAPI PET uptake through immunohistochemistry in patients with advanced ILD who underwent lung transplantation after a FAPI PET scan. This is a preliminary analysis of a single-center, open-label, single-arm, prospective exploratory biodistribution study of Ga-FAPI-46 PET imaging in patients with ILD (NCT05365802).

Associations between 6-minute walk distance and physiologic measures and clinical outcomes in myositis-associated interstitial lung disease.

Objective: The 6-min walk test (6MWT) is a simple test widely used to assess sub-maximal exercise capacity in chronic respiratory diseases. We explored the relationship of 6-min walk distance (6MWD) with measurements of physiological, clinical, radiographic measures in patients with myositis-associated interstitial lung disease (MA-ILD).

Method: We analyzed data from the Abatacept in Myositis Associated Interstitial lung disease (Attack My-ILD) study, a 48-week multicentre randomized trial of patients with anti-synthetase antibodies and active MA-ILD.

Bexotegrast in Patients with Idiopathic Pulmonary Fibrosis: The INTEGRIS-IPF Clinical Trial.

Idiopathic pulmonary fibrosis (IPF) is a rare and progressive disease that causes progressive cough, exertional dyspnea, impaired quality of life, and death. Bexotegrast (PLN-74809) is an oral, once-daily, investigational drug in development for the treatment of IPF. This Phase-2a multicenter, clinical trial randomized participants with IPF to receive, orally and once daily, bexotegrast at 40 mg, 80 mg, 160 mg, or 320 mg, or placebo, with or without background IPF therapy (pirfenidone or nintedanib), in an approximately 3:1 ratio in each bexotegrast dose cohort, for at least 12 weeks.

Quantifying lung fissure integrity using a three-dimensional patch-based convolutional neural network on CT images for emphysema treatment planning.

Purpose: Evaluation of lung fissure integrity is required to determine whether emphysema patients have complete fissures and are candidates for endobronchial valve (EBV) therapy. We propose a deep learning (DL) approach to segment fissures using a three-dimensional patch-based convolutional neural network (CNN) and quantitatively assess fissure integrity on CT to evaluate it in subjects with severe emphysema.

Approach: From an anonymized image database of patients with severe emphysema, 129 CT scans were used.

ChatGPT in radiology: A systematic review of performance, pitfalls, and future perspectives.

Purpose: The purpose of this study was to systematically review the reported performances of ChatGPT, identify potential limitations, and explore future directions for its integration, optimization, and ethical considerations in radiology applications.

Materials And Methods: After a comprehensive review of PubMed, Web of Science, Embase, and Google Scholar databases, a cohort of published studies was identified up to January 1, 2024, utilizing ChatGPT for clinical radiology applications.

Results: Out of 861 studies derived, 44 studies evaluated the performance of ChatGPT; among these, 37 (37/44; 84.

Assessing variability in non-contrast CT for the evaluation of stroke: The effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume.

Purpose: To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume.

Interstitial lung abnormality evaluated by an automated quantification system: prevalence and progression rate.

Background: Despite the importance of recognizing interstitial lung abnormalities, screening methods using computer-based quantitative analysis are not well developed, and studies on the subject with an Asian population are rare. We aimed to identify the prevalence and progression rate of interstitial lung abnormality evaluated by an automated quantification system in the Korean population.

Methods: A total of 2,890 healthy participants in a health screening program (mean age: 49 years, men: 79.

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.

Objective: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs.

Materials And Methods: 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions.

Pulmonary Fibrosis Stakeholder Summit: A Joint NHLBI, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report.

Despite progress in elucidation of disease mechanisms, identification of risk factors, biomarker discovery, and the approval of two medications to slow lung function decline in idiopathic pulmonary fibrosis and one medication to slow lung function decline in progressive pulmonary fibrosis, pulmonary fibrosis remains a disease with a high morbidity and mortality. In recognition of the need to catalyze ongoing advances and collaboration in the field of pulmonary fibrosis, the NHLBI, the Three Lakes Foundation, and the Pulmonary Fibrosis Foundation hosted the Pulmonary Fibrosis Stakeholder Summit on November 8-9, 2022. This workshop was held virtually and was organized into three topic areas: ) novel models and research tools to better study pulmonary fibrosis and uncover new therapies, ) early disease risk factors and methods to improve diagnosis, and ) innovative approaches toward clinical trial design for pulmonary fibrosis.

Combining Clinical and Biological Data to Predict Progressive Pulmonary Fibrosis in Patients With Systemic Sclerosis Despite Immunomodulatory Therapy.

Objective: Progressive pulmonary fibrosis (PPF) is the leading cause of death in systemic sclerosis (SSc). This study aimed to develop a clinical prediction nomogram using clinical and biological data to assess risk of PPF among patients receiving treatment of SSc-related interstitial lung disease (SSc-ILD).

Methods: Patients with SSc-ILD who participated in the Scleroderma Lung Study II (SLS II) were randomized to treatment with either mycophenolate mofetil (MMF) or cyclophosphamide (CYC).

How AI Responds to Common Lung Cancer Questions: ChatGPT vs Google Bard.

Background The recent release of large language models (LLMs) for public use, such as ChatGPT and Google Bard, has opened up a multitude of potential benefits as well as challenges. Purpose To evaluate and compare the accuracy and consistency of responses generated by publicly available ChatGPT-3.5 and Google Bard to non-expert questions related to lung cancer prevention, screening, and terminology commonly used in radiology reports based on the recommendation of Lung Imaging Reporting and Data System (Lung-RADS) v2022 from American College of Radiology and Fleischner society.

Quantitative image analysis in COVID-19 acute respiratory distress syndrome: a cohort observational study.

Acute respiratory distress syndrome (ARDS) is a severe form of acute lung injury commonly associated with pneumonia, including coronavirus disease-19 (COVID-19). The resultant effect can be persistent lung damage, but its extent is not known. We used quantitative high resolution computed tomography (QHR-CT) lung scans to radiographically characterize the lung damage in COVID-19 ARDS (CARDS) survivors.