Abstract The expression subtypes of lung adenocarcinoma (LUAD) capture tumors with distinct pathway activity, mutations and histopathology and also differentiate clinical outcomes. The microenvironments of these subtypes, proximal-inflammatory (PI), proximal-proliferative (PP), and terminal respiratory unit (TRU), have been described generally as immune hot, immune moderate and immune cold, respectively, but otherwise have not been analyzed at high resolution. Here, we aimed to characterize and compare tumor microenvironments between LUAD subtypes. Using spatially barcoded arrays and cDNA libraries (10x Genomics), we sequenced the spatial transcriptomes of a 6.5mm2 plane of 14 LUAD tumors from the Applied Proteogenomics and Organizational Learning Outcomes (APOLLO) program. Spatial transcriptomes had a median of 3,560 spots and a median of 4,026 genes detected per spot. First, collapsing the spatial array to a bulk measurement per sample, we applied our published expression subtype predictor classifying 4 PI, 5 PP, and 5 TRU cases. We then decomposed each tumor’s spatial expression profile by unsupervised clustering, followed by signature scoring and collapsing into tumor, immune, and stroma tumor microenvironment (TME) components. Twelve of the fourteen tumors harbored multiple components while two tumors had one component. The region areas of TME components showed trends among the subtypes, with PI having the greatest immune area and PP having the greatest tumor area. Within each tumor, we calculated differentially-expressed genes between each TME component. Comparing TME genes to the subtype predictor genes, we found significant overlap (chi-square p << 0.001). This indicates that genes that are variable among bulk tumors also have variability within tumors. We then predicted expression subtype for decomposed compartments. Five tumors had the same expression subtype across their TME components, which we refer to as single subtype tumors. However, six tumors had more than one expression subtype prediction among the tumor’s TME components, which we call ‘multi-subtype tumors’. Multi-subtype tumors had lower bulk subtype prediction scores than single-subtype tumors (p < 0.01), indicating that the TME diversity among tumors affects the bulk expression subtype. Interestingly, the six multi-subtype tumors were in the PP and PI subtypes, suggesting greater TME component diversity than TRU. Calculating the spatial compactness of the tumors through continuity indices, we found that PI subtype trended with greater intermixing of TME components. In summary, the bulk LUAD expression subtypes capture differences between tumors and within tumors related to the tumor microenvironment. The views expressed in this abstract are solely of the authors and do not reflect the official policy of the Departments of Army/Navy/Air Force, Department of Defense, USUHS, HJF, or U.S. Government. Citation Format: Shaoqiu He, Camille Alba, Savannah Kounelis-Wuillaume, Teri J. Franks, Martin L. Doughty, Robert F. Browning, Craig D. Shriver, Clifton L. Dalgard, APOLLO Research Network, Matthew D. Wilkerson. Spatial decomposition of lung adenocarcinoma expression subtypes reveals tumor microenvironment characteristics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1142.

The role that inhaled particulate matter plays in the development of post-deployment lung disease among US service members deployed to Southwest Asia during the Global War on Terrorism has been difficult to define. There is a persistent gap in data addressing the relationship between relatively short-term (months to a few years) exposures to high levels of particulate matter during deployment and the subsequent development of adverse pulmonary outcomes. Surgical lung biopsies from deployed service members and veterans (DSMs) and non-deployed service members and veterans (NDSMs) who develop lung diseases can be analyzed to potentially identify residual deployment-specific particles and develop associations with pulmonary pathological diagnoses. We examined 52 surgical lung biopsies from 25 DSMs and 27 NDSMs using field emission scanning electron microscopy (FE-SEM) with energy dispersive x-ray spectroscopy (EDS) to identify any between-group differences in the number and composition of retained inorganic particles, then compared the particle analysis results with the original histopathologic diagnoses. We recorded a higher number of total particles in biopsies from DSMs than from NDSMs, and this difference was mainly accounted for by geologic clays (illite, kaolinite), feldspars, quartz/silica, and titanium-rich silicate mixtures. Biopsies from DSMs deployed to other Southwest Asia regions (SWA-Other) had higher particle counts than those from DSMs primarily deployed to Iraq or Afghanistan, due mainly to illite. Distinct deployment-specific particles were not identified. Particles did not qualitatively associate with country of deployment. The individual diagnoses of the DSMs and NDSMs were not associated with elevated levels of total particles, metals, cerium oxide, or titanium dioxide particles. These results support the examination of particle-related lung disease in DSMs in the context of comparison groups, such as NDSMs, to assist in determining the strength of associations between specific pulmonary pathology diagnoses and deployment-specific inorganic particulate matter exposure.

HomeRadioGraphicsVol. 43, No. 7 PreviousNext Chest ImagingInvited Commentary: Patterns of Lung Injury and the Challenging Role of the RadiologistSeth Kligerman , Teri Franks, Jeff GalvinSeth Kligerman , Teri Franks, Jeff GalvinAuthor AffiliationsFrom the Department of Radiology, National Jewish Health, 1400 S Jackson St, Denver, CO 80209 (S.K.); Division of Pulmonary and Mediastinal Pathology, The Joint Pathology Center, Joint Task Force, Defense Health Agency, Silver Spring, Md (T.F.); and Departments of Diagnostic Radiology and Nuclear Medicine and Internal Medicine (Pulmonary/Critical Care), University of Maryland School of Medicine, Baltimore, Md (J.G.).Address correspondence to S.K. (email: [email protected]).Seth Kligerman Teri FranksJeff GalvinPublished Online:Jun 8 2023https://doi.org/10.1148/rg.230013MoreSectionsFull textPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookXLinked In References1. Hogan J, Smith P, Heath D, Harris P. The thickness of the alveolar capillary wall in the human lung at high and low altitude. Br J Dis Chest 1986;80(1):13–18. Crossref, Medline, Google Scholar2. Marquis KM, Hammer MM, Steinbrecher K, et al. CT approach to lung injury. RadioGraphics 2023;43(7):e220176. https://doi.org/10.1148/rg.220176. Google Scholar3. Kligerman SJ, Franks TJ, Galvin JR. From the Radiologic Pathology Archives: organization and fibrosis as a response to lung injury in diffuse alveolar damage, organizing pneumonia, and acute fibrinous and organizing pneumonia. RadioGraphics 2013;33(7):1951–1975. Link, Google Scholar4. Myers JL, Katzenstein AL. Ultrastructural evidence of alveolar epithelial injury in idiopathic bronchiolitis obliterans-organizing pneumonia. Am J Pathol 1988;132(1):102–109. Medline, Google Scholar5. Matsubara O, Tamura A, Ohdama S, Mark EJ. Alveolar basement membrane breaks down in diffuse alveolar damage: an immunohistochemical study. Pathol Int 1995;45(7):473–482. Crossref, Medline, Google Scholar6. Yazicioglu T, Mühlfeld C, Autilio C, et al. Aging impairs alveolar epithelial type II cell function in acute lung injury. Am J Physiol Lung Cell Mol Physiol 2020;319(5):L755–L769. Crossref, Medline, Google Scholar7. Borczuk AC, Salvatore SP, Seshan SV, et al. COVID-19 pulmonary pathology: a multi-institutional autopsy cohort from Italy and New York City. Mod Pathol 2020;33(11):2156–2168. Crossref, Medline, Google Scholar8. Faverio P, Luppi F, Rebora P, et al. One-year pulmonary impairment after severe COVID-19: a prospective, multicenter follow-up study. Respir Res 2022;23(1):65. Crossref, Medline, Google Scholar9. Travis WD, Hunninghake G, King TE Jr, et al. Idiopathic nonspecific interstitial pneumonia: report of an American Thoracic Society project. Am J Respir Crit Care Med 2008;177(12):1338–1347. Crossref, Medline, Google Scholar10. Konopka KE, Perry W, Huang T, Farver CF, Myers JL. Usual Interstitial Pneumonia is the Most Common Finding in Surgical Lung Biopsies from Patients with Persistent Interstitial Lung Disease following Infection with SARS-CoV-2. EClinicalMedicine 2021;42:101209. Crossref, Medline, Google ScholarArticle HistoryReceived: Feb 1 2023Accepted: Feb 6 2023Published online: June 08 2023 FiguresReferencesRelatedDetailsRecommended Articles CT Approach to Lung InjuryRadioGraphics2023Volume: 43Issue: 7Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CTRadiology2021Volume: 302Issue: 1pp. 187-197Practical Imaging Interpretation in Patients Suspected of Having Idiopathic Pulmonary Fibrosis: Official Recommendations from the Radiology Working Group of the Pulmonary Fibrosis FoundationRadiology: Cardiothoracic Imaging2021Volume: 3Issue: 1Thin-Section CT in the Categorization and Management of Pulmonary Fibrosis including Recently Defined Progressive Pulmonary FibrosisRadiology: Cardiothoracic Imaging2024Volume: 6Issue: 1An Integrated Radiologic-Pathologic Understanding of COVID-19 PneumoniaRadiology2023Volume: 306Issue: 2See More RSNA Education Exhibits Reviewing Idiopathic Interstitial Pneumonia's Radiographic Features According to the Latest American Thoracic Society / European Respiratory Society UpdatesDigital Posters2020Organizing Your Approach To Lung InjuryDigital Posters2021Breathe Sparingly: Diffuse Lung Diseases that Spare the Subpleural RegionsDigital Posters2020 RSNA Case Collection Bleomycin induced lung toxicityRSNA Case Collection2021 Cryptogenic Organizing PneumoniaRSNA Case Collection2020Organizing pneumonia associated with ulcerative colitisRSNA Case Collection2020 Vol. 43, No. 7 Metrics Altmetric Score PDF download

OBJECTIVES:Present-day pathologists may be unfamiliar with the histopathologic features of measles, which is a reemerging disease. Awareness of these features may enable early diagnosis of measles in unsuspected cases, including those with an atypical presentation. Using archived tissue samples from historic patients, a unique source of histopathologic information about measles and other reemerging infectious diseases, we performed a comprehensive analysis of the histopathologic features of measles seen in commonly infected tissues during prodrome, active, and late phases of the disease.METHODS:Subspecialty pathologists analyzed H&E-stained slides of specimens from 89 patients accessioned from 1919 to 1998 and correlated the histopathologic findings with clinical data.RESULTS:Measles caused acute and chronic histopathologic changes, especially in the respiratory, lymphoid (including appendix and tonsils), and central nervous systems. Bacterial infections in lung and other organs contributed significantly to adverse outcomes, especially in immunocompromised patients.CONCLUSIONS:Certain histopathologic features, especially Warthin-Finkeldey cells and multinucleated giant cells without inclusions, allow pathologists to diagnose or suggest the diagnosis of measles in unsuspected cases.

Lung cancer is a leading cause of cancer deaths worldwide and has complex underlying genetic drivers, subtypes and immune cell types. Molecular analysis of bulk tumors has repeatedly identified key somatic driver genes and subtypes in lung cancer. However, these key molecular strata of bulk lung tumors still contain significant heterogeneity which if characterized in finer detail may reveal new tumor microenvironment factors and lead to improved patient prognostication and therapy options. Here, we sought to compare the tumor microenvironments of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) through spatial transcriptomics. Using four frozen lung tumors and three with replicated sections (n = 7), we sequenced spatial transcriptomes using the 10X Visium platform and illumina sequencing to measure up to 5,000 latticed-spots throughout 6.5 mm2 of the tumor surface area. Data analysis revealed a large number of latticed-spots per tumor (median 3,486) with a large number of genes detected per median spot (tumor median 4,427). Through unsupervised clustering of spots in each tumor, we found between 8 and 10 clusters per tumor with distinct pathway activities, including multiple immune-enriched clusters per tumor (range: 3-5). Immune-enriched clusters in one LUAD tumor displayed a spatial shape consistent with tertiary lymphoid structures (TLS). Concordantly, this cluster overexpressed both B cell and T cell pathways and as well as a TLS signature from liver cancer. Interestingly by bulk tumor RNA analysis, this TLS+ tumor was classified to be in the terminal respiratory unit expression subtype, which is an immune-mild bulk subtype. This supports that the TLS signal can be a unique property of spatial expression in lung cancer that may be unobservable by bulk tumor RNA sequencing. Then to compare global spatial heterogeneity among tumors, we calculated an index of expression spatial continuity and found LUSC tumors to have more contiguous expression patterns than LUAD tumors (mean 0.60 vs 0.54). We also quantified expression diversity across all tumor latticed-spots and found that LUSC tumors had greater values compared to LUAD tumors (mean 0.37 vs 0.27). Together, our results suggest that LUSC has a more contiguous and heterogeneous tumor expression microenvironment than LUAD. TLS are predictive of immune checkpoint inhibitor response in many other tumor types, and our results suggest that spatial transcriptomics may also identify this responsiveness in lung cancer. Future, larger cohorts of lung tumors are needed to determine recurrent spatial properties associated with patient outcome and treatment response. The views expressed in this abstract are solely of the authors and do not reflect the official policy of the Departments of Army/Navy/Air Force, Department of Defense, USUHS, HJF, or U.S. Government. Citation Format: Matthew D. Wilkerson, Savannah Kounelis-Wuillaume, Camille Alba, Teri J. Franks, Martin L. Doughty, Robert L. Kortum, Robert F. Browning, Clifton L. Dalgard, Craig D. Shriver. Tumor microenvironment differences between lung cancer subtypes revealed by spatial transcriptomics. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4622.

Approximately 1.4 million virus-induced cancers occur annually, representing roughly 10% of the cancer burden worldwide. Seven oncogenic DNA and RNA viruses (ie, oncoviruses) are implicated in approximately 12%-25% of all human cancers owing to a variety of mechanisms as uncommon consequences of the normal viral life cycle. These seven well-recognized human oncoviruses are Epstein-Barr virus (EBV), human T-lymphotropic virus 1, hepatitis B virus, hepatitis C virus, HIV, human papilloma virus (HPV), and human herpesvirus 8 (HHV-8). Several viruses-namely, EBV, HPV, and Kaposi sarcoma herpesvirus or HHV-8-are increasingly being recognized as being related to HIV and/or AIDS, the growing number of transplant cases, and the use of immunosuppressive therapies. Infectious and inflammatory processes, and the accompanying lymphadenopathy, are great mimickers of human oncovirus-related tumors. Although it is often difficult to differentiate these entities, the associated clinical setting and radiologic findings may provide clues for an accurate diagnosis and appropriate management. Malignant lymphoid lesions are best evaluated with multidetector chest CT. The radiologic findings of these lesions are often nonspecific and are best interpreted in correlation with clinical data and histopathologic findings. ©RSNA, 2022.

Connective tissue diseases (CTDs) demonstrating features of interstitial lung disease (ILD) include systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), systemic sclerosis (SSc), dermatomyositis (DM) and polymyositis (PM), ankylosing spondylitis (AS), Sjogren syndrome (SS), and mixed connective tissue disease (MCTD). On histopathology of lung biopsy in CTD-related ILDs (CTD-ILDs), multi-compartment involvement is an important clue, and when present, should bring CTD to the top of the list of etiologic differential diagnoses. Diverse histologic patterns including nonspecific interstitial pneumonia (NSIP), usual interstitial pneumonia (UIP), organizing pneumonia, apical fibrosis, diffuse alveolar damage, and lymphoid interstitial pneumonia can be seen on histology in patients with CTD-ILDs. Although proportions of ILDs vary, the NSIP pattern accounts for a large proportion, especially in SSc, DM and/or PM and MCTD, followed by the UIP pattern. In RA patients, interstitial lung abnormality (ILA) is reported to occur in approximately 20-60% of individuals of which 35-45% will have progression of the CT abnormality. Subpleural distribution and greater baseline ILA involvement are risk factors associated with disease progression. Asymptomatic CTD-ILDs or ILA patients with normal lung function and without evidence of disease progression can be followed without treatment. Immunosuppressive or antifibrotic agents for symptomatic and/or fibrosing CTD-ILDs can be used in patients who require treatment.

[This retracts the article DOI: 10.1016/j.ejro.2020.100311.].

HomeRadiologyVol. 307, No. 1 PreviousNext Reviews and CommentaryEditorialMarijuana and the Vulnerable Lung: The Role of Imaging and the Need to Move QuicklyJeffrey Galvin , Teri FranksJeffrey Galvin , Teri FranksAuthor AffiliationsFrom the Department of Radiology, University of Maryland School of Medicine, 22 S Wayne St, Baltimore, MD 21201-1595 (J.G.); and Department of Pulmonary and Mediastinal Pathology, The Joint Pathology Center, Silver Spring, Md (T.F.).Address correspondence to J.G. (email: [email protected]).Jeffrey Galvin Teri FranksPublished Online:Nov 15 2022https://doi.org/10.1148/radiol.222745MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. 2020 National Survey on Drug Use and Health: Office of the Assistant Secretary (OAS) National Presentation. U.S. Department of Health & Human Services. https://www.samhsa.gov/data/report/2020-nsduh-national-oas. Published July 27, 2022. Accessed October 20, 2022. Google Scholar2. Gardner MN, Brandt AM. "The doctors’ choice is America’s choice": the physician in US cigarette advertisements, 1930-1953. Am J Public Health 2006;96(2):222–232. Crossref, Medline, Google Scholar3. Doll R, Hill AB. Smoking and carcinoma of the lung; preliminary report. BMJ 1950;2(4682):739–748. Crossref, Medline, Google Scholar4. Kligerman S, Franks TJ, Galvin JR. Clinical-Radiologic-Pathologic Correlation of Smoking-Related Diffuse Parenchymal Lung Disease. Radiol Clin North Am 2016;54(6):1047–1063. Crossref, Medline, Google Scholar5. Murtha L, Sathiadoss P, Salameh JP, Mcinnes MDF, Revah G. Chest CT Findings in Marijuana Smokers. Radiology 2023;307(1):e212611. Google Scholar6. Aldington S, Williams M, Nowitz M, et al. Effects of cannabis on pulmonary structure, function and symptoms. Thorax 2007;62(12):1058–1063. Crossref, Medline, Google Scholar7. Wu TC, Tashkin DP, Djahed B, Rose JE. Pulmonary hazards of smoking marijuana as compared with tobacco. N Engl J Med 1988;318(6):347–351. Crossref, Medline, Google Scholar8. Gill A. Bong lung: regular smokers of cannabis show relatively distinctive histologic changes that predispose to pneumothorax. Am J Surg Pathol 2005;29(7):980–982. Crossref, Medline, Google Scholar9. Bisconti M, Marulli G, Pacifici R, et al. Cannabinoids Identification in Lung Tissues of Young Cannabis Smokers Operated for Primary Spontaneous Pneumothorax and Correlation with Pathologic Findings. Respiration 2019;98(6):503–511. Crossref, Medline, Google Scholar10. E-cigarette, or vaping, products visual dictionary. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. https://stacks.cdc.gov/view/cdc/103783. Published December 13, 2019. Accessed July 15, 2022. Google ScholarArticle HistoryReceived: Oct 25 2022Revision requested: Oct 26 2022Revision received: Oct 28 2022Accepted: Oct 31 2022Published online: Nov 15 2022 FiguresReferencesRelatedDetailsAccompanying This ArticleChest CT Findings in Marijuana SmokersNov 15 2022RadiologyRecommended Articles Chest CT Findings in Marijuana SmokersRadiology2022Volume: 307Issue: 1Fleischner Society Visual Emphysema CT Patterns Help Predict Progression of Emphysema in Current and Former Smokers: Results from the COPDGene StudyRadiology2020Volume: 298Issue: 2pp. 441-449Deep Learning Assessment of Emphysema Progression at CT Predicts OutcomesRadiology2022Volume: 304Issue: 3pp. 680-682Relationship between Interstitial Lung Abnormalities and Emphysema in Smokers with and Those without COPDRadiology2018Volume: 288Issue: 2pp. 610-611Practical Imaging Interpretation in Patients Suspected of Having Idiopathic Pulmonary Fibrosis: Official Recommendations from the Radiology Working Group of the Pulmonary Fibrosis FoundationRadiology: Cardiothoracic Imaging2021Volume: 3Issue: 1See More RSNA Education Exhibits Intrathoracic Abnormal Air: Where Abnormal Air Will Be Located and Where it Comes FromDigital Posters2019Up, Down, and All Around: How the Distribution of Lung Disease is Affected by Ventilation, Perfusion, Immune, and Other Factors  Digital Posters2020The Lasting Lung Effects of the Legalizing Leaf: What a Radiologist Needs to Know about MarijuanaDigital Posters2019 RSNA Case Collection Pneumothorax Ex VacuoRSNA Case Collection2022Hybrid Intralobar Sequestration and Congenital Pulmonary Airway MalformationRSNA Case Collection2020Spontaneous Intercostal Lung HerniaRSNA Case Collection2021 Vol. 307, No. 1 Metrics Altmetric Score PDF download

We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.

BACKGROUND:The diagnosis of constrictive bronchiolitis (CB) in previously deployed individuals, and evaluation of respiratory symptoms more broadly, presents considerable challenges, including using consistent histopathologic criteria and clinical assessments. RESEARCH QUESTION:What are the recommended diagnostic workup and associated terminology of respiratory symptoms in previously deployed individuals? STUDY DESIGN AND METHODS:Nineteen experts participated in a three-round modified Delphi study, ranking their level of agreement for each statement with an a priori definition of consensus. Additionally, rank-order voting on the recommended diagnostic approach and terminology was performed. RESULTS:Twenty-five of 28 statements reached consensus, including the definition of CB as a histologic pattern of lung injury that occurs in some previously deployed individuals while recognizing the importance of considering alternative diagnoses. Consensus statements also identified a diagnostic approach for the previously deployed individual with respiratory symptoms, distinguishing assessments best performed at a local or specialty referral center. Also, deployment-related respiratory disease (DRRD) was proposed as a broad term to subsume a wide range of potential syndromes and conditions identified through noninvasive evaluation or when surgical lung biopsy reveals evidence of multicompartmental lung injury that may include CB. INTERPRETATION:Using a modified Delphi technique, consensus statements provide a clinical approach to possible CB in previously deployed individuals. Use of DRRD provides a broad descriptor encompassing a range of postdeployment respiratory findings. Additional follow-up of individuals with DRRD is needed to assess disease progression and to define other features of its natural history, which could inform physicians better and lead to evolution in this nosology.

Introduction: To demonstrate semantic, radiomics, and the combined risk models related to the prognoses of pulmonary pleomorphic carcinomas (PCs). Methods: We included 85 patients (M:F = 71:14; age, 35-88 [mean, 63 years]) whose imaging features were divided into training (n = 60) and test (n = 25) sets. Nineteen semantic and 142 radiomics features related to tumors were computed. Semantic risk score (SRS) model was built using the Cox-least absolute shrinkage and selection operator (LASSO) approach. Radiomics risk score (RRS) from CT and PET features and combined risk score (CRS) adopting both semantic and radiomics features were also constructed. Risk groups were stratified by the median of the risk scores of the training set. Survival analysis was conducted with the Kaplan-Meier plots. Results: Of 85 PCs, adenocarcinoma was the most common epithelial component found in 63 (73 %) tumors. In SRS model, four features were stratified into high- and low-risk groups (HR, 4.119; concordance index ([Cindex], 0.664) in the test set. In RRS model, five features helped improve the stratification (HR, 3.716; C-index, 0.591) and in CRS model, three features helped perform the best stratification (HR, 4.795; C-index, 0.617). The two significant features of CRS models were the SUVmax and the histogram feature of energy ([CT Firstorder Energy]). Conclusion: In PCs of the lungs, the combined model leveraging semantic and radiomics features provides a better prognosis compared to using semantic and radiomics features separately. The high SUVmax of solid portion (CT Firstorder Energy) of tumors is associated with poor prognosis in lung PCs.

INTRODUCTION:Between 2001 and 2015, 2.77 million U.S. military service members completed over 5 million deployments to Southwest Asia. There are concerns that deployment-related environmental exposures may be associated with adverse pulmonary health outcomes. Accurate pulmonary diagnosis often requires histopathological biopsy. These lung biopsies are amenable to chemical analysis of retained particulates using scanning electron microscopy with energy dispersive X-ray analysis (SEM/EDXA).METHOD:A retrospective review of SEM/EDXA data collected in conjunction with pathologic diagnostic consultations at the Joint Pathology Center from 2011 to 2016 was conducted. Sections adjacent to those obtained for pathologic diagnosis were prepared for SEM/EDXA particle analysis, which provides qualitative identification of elements present in each particle and semiquantitative estimations of elemental weight percent. The review includes comparison of the particle analysis data and diagnostic findings, the particle count for the standard field analyzed, and types of particles identified.RESULTS:Nonneoplastic lung biopsy specimens from 25 deployed and 7 nondeployed U.S. service members were analyzed as part of the Joint Pathology Center pathologic consultations. The major exogenous particle types identified in both groups include aluminum silicates, other silicates, silica, and titanium dioxide. Endogenous particle types identified include calcium salts and iron-containing particles consistent with hemosiderin. These particles are present in deployed and nondeployed service members and are particle types commonly identified in lung biopsy specimens from urban dwelling adults. Rare particles containing other elements such as cerium and iron alloys were identified in some cases. Possible sources of these materials include diesel fuel and occupational and other environmental exposures.CONCLUSION:Scanning electron microscopy with energy dispersive X-ray particle analysis of inhaled particulates retained in lung tissue from deployed service members identifies particles commonly present in inhaled dust. In this small case series, we were not able to detect particle profiles that were common and unique to deployed patients only.

This review article aims to address mysteries existing between Interstitial Lung Abnormality (ILA) and Nonspecific Interstitial Pneumonia (NSIP). The concept and definition of ILA are based upon CT scans from multiple large-scale cohort studies, whereas the concept and definition of NSIP originally derived from pathology with evolution to multi-disciplinary diagnosis. NSIP is the diagnosis as Interstitial Lung Disease (ILD) with clinical significance, whereas only a part of subjects with ILA have clinically significant ILD. Eventually, both ILA and NSIP must be understood in the context of chronic fibrosing ILD and progressive ILD, which remains to be further investigated.

PURPOSE:To document and compare prevalences of pulmonary pathology diagnoses among US Service members deployed during the Global War on Terrorism and non-deployed US service members. Difficulties establishing associations between deployment-related exposures and pulmonary pathology reported among US military service members deployed during the Global War on Terrorism include retrospective estimations of exposures, documenting medical outcomes and lack of comparison groups.METHODS:Pulmonary diagnoses reported between 2002 and 2015 were identified from the records of the former Armed Forces Institute of Pathology and The Joint Pathology Center. Military service and deployment were confirmed by the Defense Manpower Data Center. Diagnoses were reviewed and coded due to variations in diagnostic terminology. Propensity matching and adjusted binomial modeling were applied to comparisons between the deployed and non-deployed to address possible confounding variables.RESULTS:404 deployed and 2006 non-deployed service members were included. Demographic differences and the date of pathology report complicate unadjusted comparisons. The deployed had no significant increased prevalence of neoplastic conditions. Propensity matching identified a significant increased prevalence of organizing pneumonia in the non-deployed. An adjusted binomial model identified significant increased prevalences of small airways disease, constrictive bronchiolitis and hypersensitivity pneumonitis in the deployed. Both diagnoses were strongly associated with the date of pathology report. Small airways disease, constrictive bronchiolitis comprised 5% of deployed surgical lung biopsy diagnoses.CONCLUSION:This is the largest study of post-deployment pulmonary pathology diagnoses to date, and contains a comparison group. It provides context for studies of pulmonary outcomes among the deployed.

BACKGROUND:Multidisciplinary discussion (MDD) is widely recommended for patients with interstitial lung disease (ILD), but published primary data from MDD has been scarce, and factors influencing MDD other than chest computed tomography (CT) and lung histopathology interpretations have not been well-described.METHODS:Single institution MDD of 179 patients with ILD.RESULTS:MDD consensus clinical diagnoses included autoimmune-related ILD, chronic hypersensitivity pneumonitis, smoking-related ILD, idiopathic pulmonary fibrosis, medication-induced ILD, occupation-related ILD, unclassifiable ILD, and a few less common pulmonary disorders. In 168 of 179 patients, one or more environmental exposures or pertinent features of the medical history were identified, including recreational/avocational, residential, and occupational exposures, systemic autoimmune disease, malignancy, medication use, and family history. The MDD process demonstrated the importance of comprehensively assessing these exposures and features, beyond merely noting their presence, for rendering consensus clinical diagnoses. Precise, well-defined chest CT and lung histopathology interpretations were rendered at MDD, including usual interstitial pneumonia, nonspecific interstitial pneumonia, and organizing pneumonia, but these interpretations were associated with a variety of MDD consensus clinical diagnoses, demonstrating their nonspecific nature in many instances. In 77 patients in which MDD consensus diagnosis differed from referring diagnosis, assessment of environmental exposures and medical history was found retrospectively to be the most impactful factor.CONCLUSIONS:A comprehensive assessment of environmental exposures and pertinent features of the medical history guided MDD. In addition to rendering consensus clinical diagnoses, MDD presented clinicians with opportunities to initiate environmental remediation, behavior modification, or medication alteration likely to benefit individual patients with ILD.

Light chain deposition disease is a rare condition that results in the deposition of light chains in organs and their subsequent dysfunction. It is often the consequence of unchecked light chain production by a plasma cell clone. Rarely does it manifest with solely pulmonary involvement, especially in the young otherwise healthy patient. This article highlights the presentation and diagnosis of pulmonary light chain deposition disease in an active duty solider, the discovery of a plasma cell clone responsible for his symptoms, and the therapy targeted at the plasma cell clone-inducing pulmonary disease. This therapy included a novel successful treatment with an autologous stem cell transplantation. To date, it is among the first such documented successful bone marrow transplantations in treatment of isolated pulmonary light chain deposition disease.