ObjectivesTo develop a contrastive learning model for lung disease classification using discriminative CT imaging embeddings.MethodsA total of 1,187 subjects were included: asthma (n = 315), COPD (n = 355), post-COVID-19 (n = 375), and healthy controls (n = 142). Of these, 1,003 subjects had a single visit with similarly protocoled CT scans acquired at total lung capacity (TLC) and residual volume (RV), and 92 (33 asthma and 59 post-COVID-19) completed a follow-up visit, with two scans per visit. We developed a modified contrastive learning model incorporating an expert-conditioned routing network and adaptive temperature scaling to learn discriminative embeddings. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The embeddings were further validated via k-means clustering, and quantitative CT (qCT) metrics were compared across the derived clusters. The embedding space was used to track disease progression or improvement in the follow-up disease subgroup and to evaluate the model's ability to predict qCT metrics, quantified by the coefficient of determination (R2).ResultsThe model achieved a macro-AUC of 89.3% (95% CI: 86.5, 91.8; P < 0.001) in differentiating the four classes. Post-COVID-19 emerged as a distinct class from asthma and COPD in the t-SNE embedding space, and its embeddings across two visits captured disease improvement. Additionally, the learned embeddings showed predictive power for several qCT metrics, particularly the Jacobian (R2=0.61).ConclusionsThe proposed model effectively differentiated these three lung diseases and provided meaningful embeddings for phenotype characterization, longitudinal assessment, and qCT metric prediction.

Paediatric respirology presents distinctive challenges relative to adult care: patients have difficulties performing pulmonary function tests, more frequently have trouble complying, have distinct pathophysiology relative to adults and have diseases that occur alongside normal or abnormal lung development. Understanding and disentangling the effects of disease and growth are important to identifying the pathophysiology that drives paediatric disease and affects the quality of life of this vulnerable group. Magnetic resonance imaging (MRI) can provide detailed images of lung structure and function using either standard 1H (proton) MRI or hyperpolarised 129Xe (xenon) gas MRI. Radiation exposure in this vulnerable group is problematic, but MRI is ionising radiation-free and safe to perform in children and neonates. Exam acquisition speed has improved with some scans taking as little as 4 s. Structural imaging using 1H MRI can assess the airways and locate consolidation, mucus plugging and bronchiectasis. Dynamic structural imaging can extract information about lung motion and airway collapse, and can even be used to extract ventilation and perfusion information. In the past decade, pulmonary MRI has been examined in a wide variety of diseases, including asthma, bronchopulmonary dysplasia, bronchiolitis obliterans, childhood interstitial lung diseases, cystic fibrosis and multiple rare lung diseases. This multitechnique approach using MRI provides a holistic view that elucidates underlying disease mechanisms and connects them to patient outcomes and treatment response. This review will examine developments in pulmonary MRI over the past decade, with the aim of illustrating recent advances in research and how these discoveries are beginning to be applied to clinical settings.

Accurate airway segmentation is essential for quantitative assessment of pulmonary diseases but remains challenging in expiratory computed tomography (CT) because airways become thinner and less distinguishable from adjacent vessels, resulting in severe class imbalance and increased peripheral leakages. Conventional manual or rule-based methods are time-consuming and limited reproducibility, while most deep learning studies have focused exclusively on inspiratory scans. To address these limitations, we propose a three-dimensional (3D) segmentation framework with an attention gate, termed Averaged Multi-Gaussian Response (AMGR), integrated into a U-Net architecture and tailored for expiratory airways. The AMGR gate stabilizes feature fusion by averaging multiple Gaussian responses per channel, suppressing peripheral noise and improving continuity in distal airway. The model is trained using a composite loss that combines cross-entropy, intersection over union, and centerline, jointly enhancing overlap accuracy and structural completeness. A total of 120 subjects from four cohorts (asthma, COPD, post-COVID-19, and healthy subjects) were used for training, validation, and independent testing. Quantitative evaluation demonstrated a high Dice score of 0.9592 relative to existing approaches, while maintaining balanced Precision (0.9416) and Recall (0.9780). Qualitatively, the model effectively suppresses false-positive leakages and noise. These results confirm that the AMGR framework provides a robust and generalizable solution for airway segmentation in expiratory CT, enabling accurate geometric analysis for future phenotype- and biomarker-based pulmonary studies.

PURPOSE:This study investigated asthma phenotypes and their associations with ventilation heterogeneity and particle deposition by utilizing Single-Photon Emission Computed Tomography (SPECT) imaging, quantitative Computed Tomography (qCT) imaging-based subgrouping, and a whole-lung computational model. MATERIALS AND METHODS:Two datasets were analyzed: one from a combined SPECT and CT (SPECT/CT) study with six asthmatic subjects, and another from the Severe Asthma Research Program (SARP) with 209 asthmatic subjects. Data from 35 previously acquired healthy subjects served as a control group. Each subject underwent CT scans at full inspiration and expiration, along with pulmonary function testing (PFT). The SPECT/CT study included ventilation SPECT imaging. Key qCT variables such as airway diameter, wall thickness, percentage of air trapping (AirT%), and percentage of small airway disease (fSAD%) were assessed. A subject-specific whole-lung computational fluid and particle dynamics (CFPD) model predicted airway resistance, particle deposition fraction, and the coefficient of variation (CV) for ventilation heterogeneity. Subjects were categorized into four predefined asthma imaging subgroups/clusters with increasing severity (C1-C4). CFPD-predicted CVs were validated against SPECT measurements. We compared PFT, qCT, and CFPD variables across SARP clusters and analyzed particle deposition fractions in large conducting, small conducting, and respiratory airways. RESULTS:Cluster C4 exhibited a significantly distinct ventilation profile compared to other clusters and health controls. This distinction contrasted with the insignificant differences between ventilation profiles in severity subgroups defined by conventional spirometry-based guidelines. Airway resistance varied significantly across the asthma clusters. Although both C3 and C4 clusters represented severe asthma, only C4 showed a significant increase in AirT%, primarily due to fSAD%. Since inflammatory phenotypes differ - C3 with wall thickening in large and small conducting airways, and C4 with elevated fSAD% and Emph% in small conducting and respiratory airways - fine particles (∼5 μm) and extrafine particles (∼1 μm) are more effective at reaching the respective regions in C3 and C4. Given that C2 and C4 have hyper-responsive phenotypes with narrowed conducting airways, fine particles are more effective in reaching these areas. Airway enlargement in targeted segments of the left lower lobe resulted in improved particle deposition. CONCLUSION:Our cluster-informed CFPD-based approach enhances the understanding of ventilation heterogeneity in asthma and holds potential for refining strategies for inhalational therapies.

Body composition (BC) is emerging as a prognostic factor in cancer, with metformin showing potential anti-tumor effects in overweight/obese patients with non-small cell lung cancer (NSCLC). However, the sex-specific impact of BC on overall survival (OS), its underlying mechanisms, and relationship to metformin's beneficial effects remain unclear. A total of 1, 275 patients with NSCLC were included. Six AI-derived BC measurements were extracted from chest CT scans: muscle density and volume, visceral (VAT) and subcutaneous adipose tissue (SAT), VAT/SAT ratio, and intra-muscular adipose tissue (IMAT). Clinical data included age, sex, histology, stage, smoking status, diabetes, metformin use, and BMI. Multivariable Cox proportional hazard model was used to assess the associations with OS. Correlations between BC and eight circulating inflammatory cytokines were evaluated in a subset of 93 patients and BMI-related mutation profiles were compared in an independent cohort of 5, 363 patients with NSCLC. Sex-specific distributions showed significantly higher muscle volume, density, VAT, and VAT/SAT ratio in males, while females had higher SAT and IMAT. Higher muscle volume is associated with prolonged OS (HR = 0.71, T3 vs. T1 and HR = 0.78, T2 vs. T1, P < 0.01). In females, higher muscle density was associated with prolonged OS (HR = 0.76, T3 vs. T1, P = 0.04), while higher IMAT correlated with shortened OS (HR = 1.39, T3 vs. T1, P = 0.01). In males, increased VAT and SAT tended to associate with prolonged OS. Higher SAT (HR = 0.48, 0.42 for T3 and T2 vs. T1, P = 0.02 and 0.01) and IMAT (HR = 0.52, 0.42 for T3 and T2 vs. T1, P = 0.04 and 0.01) predicted prolonged OS specifically in metformin users, but not in diabetic non-metformin users. Cytokine analysis showed positive correlations between VAT, SAT, IMAT and leptin in both sexes, while IMAT positively correlated with IL-8 and MCP-1, and muscle density negatively correlated with leptin and IL-8 in females. EGFR mutation was significantly less frequent and KRAS mutations more frequent in patients with BMI > 25, particularly in females. Mutation analysis revealed lower EGFR and higher KRAS mutation frequencies in patients with BMI > 25, particularly in females. Mutations in metabolism and immune response genes (STK11, NF1, ATM, and SMARCA4) showed reversed sex-specific trends related to BMI, suggesting obesity may drive distinct mutation selection and immune responses between sexes. AI-derived BC measurements revealed sex-specific associations with OS in NSCLC, supported by distinct patterns in circulating cytokines and somatic mutations. Metformin's beneficial effects were specifically associated with adipose tissue measurements, suggesting its anti-tumor activity may be mediated through metabolic and immunomodulatory pathways. Xinan Wang, Akinori Hata, Noriaki Wada, Yi Li, Mark Schiebler, Hiroto Hatabu, David Christiani. Artificial intelligence-derived body composition measurements reveal sex- and metformin-specific predictors of overall survival in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3584.

Lung MRI provides both structural and functional information across a spectrum of parenchymal and airway pathologies. MRI, using current widely available conventional sequences, provides high-quality diagnostic images that allow tissue characterization and delineation of lung lesions; dynamic evaluation of expiratory central airway collapse, diaphragmatic or chest wall motion, and the relation of lung masses to the chest wall; oncologic staging; surveillance of chronic lung pathologies; and differentiation of inflammation and fibrosis in interstitial lung disease. Ongoing technologic advances, including deep learning acceleration methods, may enable future applications in longitudinal lung cancer screening without ionizing radiation exposure and in the regional quantification of ventilation and perfusion without hyperpolarized gas or IV contrast media. Although society statements highlight appropriate indications for lung MRI and the modality has performed favorably relative to CT or FDG PET/CT in various indications, the examination's clinical utilization remains extremely low. Ongoing barriers to adoption include limited awareness by referring physicians, as well as insufficient proficiency and experience by radiologists and technologists. In this AJR Expert Panel Narrative Review, we review the clinical indications for lung MRI, describe the examination's current capabilities, provide guidance on protocols comprising widely available pulse sequences, introduce emerging techniques, and issue consensus recommendations.

Abstract Bronchoscopy is not conventionally guided by prior knowledge of segmental airway obstruction. Hyperpolarized gas magnetic resonance imaging (MRI) ventilation abnormalities and computed tomography (CT) air trapping are related to lung function and asthma severity but have not been used to target segmental inflammation and remodeling. We evaluate the feasibility of using bronchoscopy guided by 3He MRI and CT to reveal differences in inflammatory response, morphology, and cellular activity in poorly‐ (defect) versus well‐ventilated (control) lung regions. Eleven participants (5 female; age, 22.8 ± 3.4 years; 9 asthma) who experienced a cold with increased lower airway symptoms underwent 3He MRI and/or CT at least 6 weeks after recovery. Differences between defect and control regions were compared. In defect as compared to control sites, bronchoalveolar lavage neutrophils (p = 0.06) and granulocytes (p = 0.08) trended towards an increase; inflammatory mediators (i.e., 15‐epi‐LXA4, LXA4) were also significantly different (p < 0.05) between sites. Correlations were observed between macrophages, neutrophils, and eosinophils with inflammatory mediators (i.e., 15‐epi‐LXA4, LXA4, LTB4). Correlations were observed for macrophages and neutrophils with 15‐epi‐LXA4, and eosinophils with LXA4 and leukotriene B4. Basement membrane wall thickness was similar for defect versus control sites (p = 0.9). These results support the feasibility of image‐guided methods to identify airway obstruction phenotypes.

Radiologists are witnessing astonishing innovation and advancement of CT technologies and their clinical applications. This review highlights how photon-counting CT (PCCT), upright CT, and artificial intelligence (AI) may impact cardiothoracic CT applications for imaging and diagnosis. PCCT relies on new detectors that can bin the separate photon energies and allow for lower radiation dose and better spatial resolution. The clinical applications of PCCT in the coronary arteries are becoming the new standard for cardiac CT imaging. New upright CT has shown the benefits of imaging in the upright position and offers new insight into how the upright position affects biomechanics and physiology. Four-dimensional CT, which can be used to directly image perfusion, is challenging MRI and MR angiography for primacy in this area. The burgeoning role of AI and informatics is changing the way radiologists interpret and report many imaging examinations. The future is bright and promises lower radiation and intravenous contrast agent doses and higher spatial resolution, and will further incorporate deep learning to improve the effectiveness of CT.

BACKGROUND:Body composition is emerging as a prognostic factor in cancer, with metformin showing potential antitumour effects in patients with obesity and non-small cell lung cancer (NSCLC). We aimed to determine which specific body composition components drive survival differences in different patient subgroups and explore underlying molecular mechanisms. METHODS:In this retrospective cohort study, we analysed 1275 patients with confirmed NSCLC diagnosis and available chest CT imaging for body composition analysis at the Massachusetts General Hospital. Six body composition parameters (muscle density and volume, visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], VAT/SAT ratio, and intramuscular adipose tissue [IMAT]) were quantified using validated CNN-based segmentation algorithms from baseline CT scans. The primary outcome was overall survival (OS), assessed from diagnosis until death or last follow-up. Associations with OS were analysed using multivariable Cox proportional hazards model, followed by subgroup analyses by sex, BMI, diabetes, and metformin use. Circulating cytokine levels (n = 93) and oncogenic driver mutations (n = 646) from the imaging cohort and comprehensive tumour mutation profiles from an independent Memorial Sloan Kettering Cancer Center cohort (n = 5363) were analysed for mechanistic insights. FINDINGS:Muscle volume demonstrated a dose-response relationship with OS (T2 vs. T1: HR = 0.78, 95% CI: 0.65-0.94; T3 vs. T1: HR = 0.71, 95% CI: 0.57-0.88) and the effect was intensified in patients with a BMI ≥25 kg/m2 (T2 vs. T1: HR = 0.64, 95% CI: 0.49-0.85; T3 vs. T1: HR = 0.56, 95% CI: 0.41-0.77) and with diabetes (T2 vs. T1: HR = 0.61, 95% CI: 0.39-0.97; T3 vs. T1: HR = 0.50, 95% CI: 0.30-0.84). Higher SAT volume (T2 vs. T1: HR = 0.48, 95% CI: 0.26-0.88; T3 vs. T1: HR = 0.42, 95% CI: 0.21-0.83) and IMAT (T2 vs. T1: HR = 0.52, 95% CI: 0.28-0.97; T3 vs. T1: HR = 0.42, 95% CI: 0.22-0.82) predicted prolonged OS specifically in patients who have used metformin. Genomic analysis revealed that KRAS mutation was significantly enriched in patients with higher SAT, VAT and BMI ≥25 kg/m2 while EGFR mutation was enriched in patients with lower SAT, IMAT and BMI <25 kg/m2. INTERPRETATION:Higher muscle volume consistently predicts improved survival in patients with NSCLC, with enhanced effects in patients with elevated BMI or diabetes. Increased adipose tissue components benefit only metformin users, suggesting drug-specific metabolic interactions. Our work demonstrates the clinical utility of detailed body composition and suggests opportunities for personalised treatment approaches based on comprehensive body composition profiles rather than BMI alone. FUNDING:This work was supported by grants from the National Cancer Institute of the National Institutes of Health: U01CA209414 to X.W. and D.C.C., 1K99CA297010 to X.W.

This retrospective study developed an automated algorithm for 3D segmentation of adipose tissue and paravertebral muscle on chest CT using artificial intelligence (AI) and assessed its feasibility. The study included patients from the Boston Lung Cancer Study (2000-2011). For adipose tissue quantification, 77 patients were included, while 245 were used for muscle quantification. The data were split into training and test sets, with manual segmentation as the ground truth. Subcutaneous and visceral adipose tissues (SAT and VAT) were segmented separately. Muscle area, mean attenuation value, and intermuscular adipose tissue percentage (IMAT%) were calculated in the paravertebral muscle segmentation. The AI algorithm was trained on the training sets, and its performance was evaluated on the test sets. The AI achieved Dice scores above 0.87 and showed excellent correlations for VAT/SAT ratios, muscle attenuation value, and IMAT% (correlation coefficients > 0.98, p < 0.001). The mean differences between the AI and ground truth were minimal (VAT/SAT ratio: 0.7%; muscle attenuation value: 1 HU; IMAT%: <1%). In conclusion, we developed a feasible AI algorithm for automated 3D segmentation of adipose tissue and paravertebral muscle on chest CT.

By incompletely understood mechanisms, type 2 (T2) inflammation present in the airways of severe asthmatics drives the formation of pathologic mucus which leads to airway mucus plugging. Here we investigate the molecular role and clinical significance of intelectin-1 (ITLN-1) in the development of pathologic airway mucus in asthma. Through analyses of human airway epithelial cells we find that ITLN1 gene expression is highly induced by interleukin-13 (IL-13) in a subset of metaplastic MUC5AC+ mucus secretory cells, and that ITLN-1 protein is a secreted component of IL-13-induced mucus. Additionally, we find ITLN-1 protein binds the C-terminus of the MUC5AC mucin and that its deletion in airway epithelial cells partially reverses IL-13-induced mucostasis. Through analysis of nasal airway epithelial brushings, we find that ITLN1 is highly expressed in T2-high asthmatics, when compared to T2-low children. Furthermore, we demonstrate that both ITLN-1 gene expression and protein levels are significantly reduced by a common genetic variant that is associated with protection from the formation of mucus plugs in T2-high asthma. This work identifies an important biomarker and targetable pathways for the treatment of mucus obstruction in asthma. Type 2 inflammation drives the formation of pathologic mucus in patients with asthma. Here, authors reveal a role for intelectin-1 in IL-13-induced mucus properties, and that an ITLN1 eQTL is associated with protection from the formation of mucus plugs in T2-high asthma.

BACKGROUND. Closure of a GE Healthcare facility in Shanghai, China, in 2022 disrupted the iodinated contrast media supply. Technologic advances have addressed limitations associated with the use of pulmonary MRA for diagnosis of pulmonary embolism (PE). OBJECTIVE. The purpose of this study was to describe a single institution's experience in the use of pulmonary MRA as an alternative to CTA for the diagnosis of PE in the general population during the iodinated contrast media shortage in 2022. METHODS. This retrospective single-center study included all CTA and MRA examinations performed to exclude PE from April 1 through July 31 (18 weekly periods) in 2019 (before the COVID-19 pandemic and contrast media shortage), 2021 (during the pandemic but before the shortage), and 2022 (during both the pandemic and the shortage). From early May through mid-July of 2022, MRA served as the preferred test for PE diagnosis, to preserve iodinated contrast media. CTA and MRA reports were reviewed. The total savings in iodinated contrast media volume resulting from preferred use of MRA was estimated. RESULTS. The study included 4491 examinations of 4006 patients (mean age, 57 ± 18 [SD] years; 1715 men, 2291 women): 1245 examinations (1111 CTA, 134 MRA) in 2019, 1547 examinations (1403 CTA, 144 MRA) in 2021, and 1699 examinations (1282 CTA, 417 MRA) in 2022. In 2022, the number of MRA examinations was four (nine when normalized to a 7-day period) in week 1, and this number increased to a maximum of 63 in week 10 and then decreased to 10 in week 18. During weeks 8-11, more MRA examinations (range, 45-63 examinations) than CTA examinations (range, 27-46 examinations) were performed. In 2022, seven patients with negative MRA underwent subsequent CTA within 2 weeks; CTA was negative in all cases. In 2022, 13.9% of CTA examinations (vs 10.3% of MRA examinations) were reported as having limited image quality. The estimated 4-month savings resulting from preferred use of MRA in 2022, under the assumption of uniform simple linear growth in CTA utilization annually and a CTA dose of 1 mL/kg, was 27 L of iohexol (350 mg I/mL). CONCLUSION. Preferred use of pulmonary MRA for PE diagnosis in the general population helped to conserve iodinated contrast media during the 2022 shortage. CLINICAL IMPACT. This single-center experience shows pulmonary MRA to be a practical substitute for pulmonary CTA in emergency settings.

HomeRadiologyVol. 307, No. 4 PreviousNext Reviews and CommentaryEditorialUsing Functional Lung MRI to Predict Chronic Lung Allograft DysfunctionSean B. Fain , Mark L. SchieblerSean B. Fain , Mark L. SchieblerAuthor AffiliationsFrom the Department of Radiology, Carver College of Medicine, University of Iowa, 200 Hawkins Dr, Iowa City, IA 52242 (S.B.F.); and Department of Radiology, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, Wis (M.L.S.).Address correspondence to S.B.F. (email: [email protected]).Sean B. Fain Mark L. SchieblerPublished Online:Apr 18 2023https://doi.org/10.1148/radiol.230636MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Glanville AR, Verleden GM, Todd JL, et al. Chronic lung allograft dysfunction: definition and update of restrictive allograft syndrome—a consensus report from the Pulmonary Council of the ISHLT. J Heart Lung Transplant 2019;38(5):483–492. Crossref, Medline, Google Scholar2. Bos S, Vos R, Van Raemdonck DE, Verleden GM. Survival in adult lung transplantation: where are we in 2020? Curr Opin Organ Transplant 2020;25(3):268–273. Crossref, Medline, Google Scholar3. Belloli EA, Gu T, Wang Y, et al. Radiographic graft surveillance in lung transplantation: prognostic role of parametric response mapping. Am J Respir Crit Care Med 2021;204(8):967–976. Crossref, Medline, Google Scholar4. Vogel-Claussen C, Kaireit TF, Voskrebenzev A, et al. Phase-resolved functional lung (PREFUL) MRI–derived ventilation and perfusion parameters predict future lung transplant loss. Radiology 2023;307(4):e221958. Link, Google Scholar5. Verleden SE, Vos R, Vandermeulen E, et al. Parametric response mapping of bronchiolitis obliterans syndrome progression after lung transplantation. Am J Transplant 2016;16(11):3262–3269. Crossref, Medline, Google Scholar6. Ohno Y, Seo JB, Parraga G, et al. Pulmonary functional imaging: part 1—state-of-the-art technical and physiologic underpinnings. Radiology 2021;299(3):508–523. Link, Google Scholar7. Renne J, Lauermann P, Hinrichs JB, et al. Chronic lung allograft dysfunction: oxygen-enhanced T1-mapping MR imaging of the lung. Radiology 2015;276(1):266–273. Link, Google Scholar8. Walkup LL, Myers K, El-Bietar J, et al. Xenon-129 MRI detects ventilation deficits in paediatric stem cell transplant patients unable to perform spirometry. Eur Respir J 2019;53(5):1801779. Crossref, Medline, Google Scholar9. Quantitative Imaging Biomarker Alliance (QIBA). https://qibawiki.rsna.org/index.php/Main_Page. Posted June 17, 2022. Accessed March 12, 2023. Google Scholar10. 129Xe MRI Clinical Trials Consortium. https://www.129xectc.org. Accessed March 12, 2023. Google ScholarArticle HistoryReceived: Mar 13 2023Revision requested: Mar 17 2023Revision received: Mar 19 2023Accepted: Mar 21 2023Published online: Apr 18 2023 FiguresReferencesRelatedDetailsAccompanying This ArticlePhase-resolved Functional Lung (PREFUL) MRI–derived Ventilation and Perfusion Parameters Predict Future Lung Transplant LossApr 18 2023RadiologyRecommended Articles Phase-resolved Functional Lung (PREFUL) MRI–derived Ventilation and Perfusion Parameters Predict Future Lung Transplant LossRadiology2023Volume: 307Issue: 4Chronic Lung Allograft Dysfunction: Review of CT and Pathologic FindingsRadiology: Cardiothoracic Imaging2021Volume: 3Issue: 1Functional Lung MRI: Deep Learning Turns Proton into Helium Ventilation Maps—The Battle Is On!Radiology2020Volume: 298Issue: 2pp. 439-440Hyperpolarized 129Xe MR Spectroscopy in the Lung Shows 1-year Reduced Function in Idiopathic Pulmonary FibrosisRadiology2022Volume: 305Issue: 3pp. 688-696Chronic Obstructive Pulmonary Disease: Lobar Analysis with Hyperpolarized 129Xe MR ImagingRadiology2016Volume: 282Issue: 3pp. 857-868See More RSNA Education Exhibits Lung Transplantation : CT Assessment of Chronic Lung Allograft Dysfunction (CLAD)Digital Posters2020Emerging Phenotypes Of Chronic Lung Allograft Dysfunction (CLAD) In Long-term Lung Transplant SurvivorsDigital Posters2021Light as Air: Imaging Course of Lung Transplantation from Patient Selection to Postoperative ComplicationsDigital Posters2019 RSNA Case Collection Diffuse idiopathic pulmonary neuroendocrine cell hyperplasia with carcinoid tumorRSNA Case Collection2020Granulomatous lymphocytic interstitial lung disease RSNA Case Collection2021Mounier-Kuhn syndromeRSNA Case Collection2020 Vol. 307, No. 4 Metrics Altmetric Score PDF download

HomeRadiologyVol. 306, No. 2 PreviousNext Reviews and CommentaryEditorial–Centennial ContentAdvances in Thoracic Imaging: Key Developments in the Past Decade and Future DirectionsMizuki Nishino , Mark L. SchieblerMizuki Nishino , Mark L. SchieblerAuthor AffiliationsFrom the Department of Radiology, Brigham and Women’s Hospital and Dana-Farber Cancer Institute, 450 Brookline Ave, Boston MA (M.N.); and Department of Radiology, University of Wisconsin–Madison School of Medicine and Public Health, Madison, Wis (M.L.S.).Address correspondence to M.N. (email: [email protected]).Mizuki Nishino Mark L. SchieblerPublished Online:Jan 10 2023https://doi.org/10.1148/radiol.222536MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. National Lung Screening Trial Research Team; Aberle DR, Berg CD, et al. The National Lung Screening Trial: overview and study design. Radiology 2011;258(1):243–253. Link, Google Scholar2. Liang M, Tang W, Xu DM, et al. Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers. Radiology 2016;281(1):279–288. Link, Google Scholar3. Beig N, Khorrami M, Alilou M, et al. Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology 2019;290(3):783–792. Link, Google Scholar4. Wang GX, Baggett TP, Pandharipande PV, et al. Barriers to Lung Cancer Screening Engagement from the Patient and Provider Perspective. Radiology 2019;290(2):278–287. Link, Google Scholar5. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology 2017;284(1):228–243. Link, Google Scholar6. Zhou M, Leung A, Echegaray S, et al. Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications. Radiology 2018;286(1):307–315. Link, Google Scholar7. Huang Y, Liu Z, He L, et al. Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. Radiology 2016;281(3):947–957. Link, Google Scholar8. Nishino M, Hatabu H, Johnson BE, McLoud TC. State of the art: Response assessment in lung cancer in the era of genomic medicine. Radiology 2014;271(1):6–27. Link, Google Scholar9. Park H, Sholl LM, Hatabu H, Awad MM, Nishino M. Imaging of Precision Therapy for Lung Cancer: Current State of the Art. Radiology 2019;293(1):15–29. Link, Google Scholar10. Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology 2017;284(2):574–582. Link, Google Scholar11. Annarumma M, Withey SJ, Bakewell RJ, Pesce E, Goh V, Montana G. Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks. Radiology 2019;291(1):196–202. Link, Google Scholar12. Nam JG, Park S, Hwang EJ, et al. Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology 2019;290(1):218–228. Link, Google Scholar13. Chung M, Bernheim A, Mei X, et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology 2020;295(1):202–207. Link, Google Scholar14. Lei J, Li J, Li X, Qi X. CT Imaging of the 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology 2020;295(1):18. Link, Google Scholar15. Song F, Shi N, Shan F, et al. Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology 2020;295(1):210–217. Link, Google Scholar16. Wong HYF, Lam HYS, Fong AH, et al. Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19. Radiology 2020;296(2):E72–E78. Link, Google Scholar17. Pan F, Ye T, Sun P, et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology 2020;295(3):715–721. Link, Google Scholar18. Caruso D, Zerunian M, Polici M, et al. Chest CT Features of COVID-19 in Rome, Italy. Radiology 2020;296(2):E79–E85. Link, Google Scholar19. Rubin GD, Ryerson CJ, Haramati LB, et al. The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society. Radiology 2020;296(1):172–180. fi Link, Google Scholar20. Bai HX, Hsieh B, Xiong Z, et al. Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT. Radiology 2020;296(2):E46–E54. Link, Google Scholar21. Li L, Qin L, Xu Z, et al. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology 2020;296(2):E65–E71. Link, Google Scholar22. Ai T, Yang Z, Hou H, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology 2020;296(2):E32–E40. Link, Google Scholar23. Fang Y, Zhang H, Xie J, et al. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology 2020;296(2):E115–E117. Link, Google Scholar24. Prokop M, van Everdingen W, van Rees Vellinga T, et al; COVID-19 Standardized Reporting Working Group of the Dutch Radiological Society. CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation. Radiology 2020;296(2):E97–E104. Link, Google Scholar25. Suh YJ, Hong H, Ohana M, et al. Pulmonary Embolism and Deep Vein Thrombosis in COVID-19: A Systematic Review and Meta-Analysis. Radiology 2021;298(2):E70–E80. Link, Google Scholar26. Han X, Fan Y, Alwalid O, et al. Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia. Radiology 2021;299(1):E177–E186. Link, Google Scholar27. Pan F, Yang L, Liang B, et al. Chest CT Patterns from Diagnosis to 1 Year of Follow-up in Patients with COVID-19. Radiology 2022;302(3):709–719. Link, Google Scholar28. Franquet T. Imaging of pulmonary viral pneumonia. Radiology 2011;260(1):18–39. Link, Google Scholar29. Ohno Y, Seo JB, Parraga G, et al. Pulmonary Functional Imaging: Part 1-State-of-the-Art Technical and Physiologic Underpinnings. Radiology 2021;299(3):508–523. Link, Google Scholar30. Gefter WB, Lee KS, Schiebler ML, et al. Pulmonary Functional Imaging: Part 2-State-of-the-Art Clinical Applications and Opportunities for Improved Patient Care. Radiology 2021;299(3):524–538. Link, Google Scholar31. Hatabu H, Ohno Y, Gefter WB, et alFleischner Society. Expanding Applications of Pulmonary MRI in the Clinical Evaluation of Lung Disorders: Fleischner Society Position Paper. Radiology 2020;297(2):286–301. Link, Google Scholar32. Matheson AM, McIntosh MJ, Kooner HK, et al. Persistent 129Xe MRI Pulmonary and CT Vascular Abnormalities in Symptomatic Individuals with Post-acute COVID-19 Syndrome. Radiology 2022;305(2):466–476. Link, Google Scholar33. Mistretta CA, Crummy AB, Strother CM. Digital angiography: a perspective. Radiology 1981;139(2):273–276. Link, Google Scholar34. Gu T, Korosec FR, Block WF, et al. PC VIPR: a high-speed 3D phase-contrast method for flow quantification and high-resolution angiography. AJNR Am J Neuroradiol 2005;26(4):743–749. Medline, Google Scholar35. Mistretta CA. Breaking Angiographic Speed Limits: Accelerated 4D MRA and 4D DSA Using Undersampled Acquisition and Constrained Reconstruction. Lecture presented at: 2012 RSNA Annual Meeting; November 29, 2012; Chicago, IL. Google Scholar36. Pickhardt PJ. Value-added Opportunistic CT Screening: State of the Art. Radiology 2022;303(2):241–254. Link, Google Scholar37. McLoud T. Thoracic Radiology: Recent Developments and Future Trends. Radiology 2023. https://pubs.rsna.org/doi/10.1148/radiol.223121. Published online January 17, 2023. Google Scholar38. Yoon SH, Lee JH, Koh J, et al. An Integrated Radiologic–Pathologic Understanding of COVID-19 Pneumonia. Radiology 2023. https://pubs.rsna.org/doi/10.1148/radiol.222600. Published online January 17, 2023. Google Scholar39. Lee KS, Jeong YJ, Wi YM, et al. Current and Emerging Knowledge in COVID-19. Radiology 2023. https://pubs.rsna.org/doi/10.1148/radiol.222462. Published online January 10, 2023. Google Scholar40. Hansell DM, Bankier AA, MacMahon H, et al. Fleischner Society: glossary of terms for thoracic imaging. Radiology 2008;246(3):697–722. Link, Google Scholar41. Bernheim A, Mei X, Huang M, et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology 2020;295(3):685–691. Link, Google Scholar42. MacMahon H, Austin JH, Gamsu G, et al. Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. Radiology 2005;237(2):395–400. Link, Google Scholar43. Xie X, Zhong Z, Zhao W, et al. Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR testing. Radiology 2020;296(2):E41–E45. Link, Google ScholarArticle HistoryReceived: Oct 3 2022Revision requested: Oct 12 2022Revision received: Oct 13 2022Accepted: Oct 20 2022Published online: Jan 10 2023 FiguresReferencesRelatedDetailsCited ByThoracic Radiology: Recent Developments and Future TrendsTheresa C. McLoud, Brent P. Little, 17 January 2023 | Radiology, Vol. 306, No. 2Recommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 306, No. 2 Metrics Altmetric Score PDF download

The imaging of asthma using chest computed tomography (CT) is well-established (Jarjour et al., Am J Respir Crit Care Med 185(4):356-62, 2012; Castro et al., J Allergy Clin Immunol 128:467-78, 2011). Moreover, recent advances in functional imaging of the lungs with advanced computer analysis of both CT and magnetic resonance images (MRI) of the lungs have begun to play a role in quantifying regional obstruction. Specifically, quantitative measurements of the airways for bronchial wall thickening, luminal narrowing and distortion, the amount of mucus plugging, parenchymal density, and ventilation defects that could contribute to the patient's disease course are instructive for the entire care team. In this chapter, we will review common imaging methods and findings that relate to the heterogeneity of asthma. This information can help to guide treatment decisions. We will discuss mucous plugging, quantitative assessment of bronchial wall thickening, delta lumen phenomenon, parenchymal low-density lung on CT, and ventilation defect percentage on MRI as metrics for assessing regional ventilatory dysfunction.

Rationale: Extrapulmonary manifestations of asthma, including fatty infiltration in tissues, may reflect systemic inflammation and influence lung function and disease severity. Objectives: To determine if skeletal muscle adiposity predicts lung function trajectory in asthma. Methods: Adult SARP III (Severe Asthma Research Program III) participants with baseline computed tomography imaging and longitudinal postbronchodilator FEV1% predicted (median follow-up 5 years [1,132 person-years]) were evaluated. The mean of left and right paraspinous muscle density (PSMD) at the 12th thoracic vertebral body was calculated (Hounsfield units [HU]). Lower PSMD reflects higher muscle adiposity. We derived PSMD reference ranges from healthy control subjects without asthma. A linear multivariable mixed-effects model was constructed to evaluate associations of baseline PSMD and lung function trajectory stratified by sex. Measurements and Main Results: Participants included 219 with asthma (67% women; mean [SD] body mass index, 32.3 [8.8] kg/m(2)) and 37 control subjects (51% women; mean [SD] body mass index, 26.3 [4.7] kg/m(2)). Participants with asthma had lower adjusted PSMD than control subjects (42.2 vs. 55.8 HU; P < 0.001). In adjusted models, PSMD predicted lung function trajectory in women with asthma (beta = -0.47 Delta slope per 10-HU decrease; P = 0.03) but not men (beta = 0.11 Delta slope per 10-HU decrease; P = 0.77). The highest PSMD tertile predicted a 2.9% improvement whereas the lowest tertile predicted a 1.8% decline in FEV1% predicted among women with asthma over 5 years. Conclusions: Participants with asthma have lower PSMD, reflecting greater muscle fat infiltration. Baseline PSMD predicted lung function decline among women with asthma but not men. These data support an important role of metabolic dysfunction in lung function decline.

BACKGROUND Although lung volumes are usually normal in individuals with chronic thromboembolic pulmonary hypertension（CTEPH）, approximately 20%-29% of patients exhibit a restrictive pattern on pulmonary function testing.AIM To quantify longitudinal changes in lung volume and cardiac cross-sectional area（CSA） in patients with CTEPH.METHODS In a retrospective cohort study of patients seen in our hospital between January 2012 and December 2019, we evaluated 15 patients with CTEPH who had chest computed tomography（CT） performed at baseline and after at least 6 mo of therapy. We matched the CTEPH cohort with 45 control patients by age, sex, and observation period. CT-based lung volumes and maximum cardiac CSAs were measured and compared using the Wilcoxon signed-rank test and the Mann-Whitney u test.RESULTS Total, right lung, and right lower lobe volumes were significantly reduced in the CTEPH cohort at follow-up vs baseline（total, P = 0.004; right lung, P = 0.003; right lower lobe; P = 0.01）. In the CTEPH group, the reduction in lung volume and cardiac CSA was significantly greater than the corresponding changes in the control group（total, P = 0.01; right lung, P = 0.007; right lower lobe,P = 0.01; CSA, P = 0.0002）. There was a negative correlation between lung volume change and cardiac CSA change in the control group but not in the CTEPH cohort.CONCLUSION After at least 6 mo of treatment, CT showed an unexpected loss of total lung volume in patients with CTEPH that may reflect continued parenchymal remodeling.