CT colonography (CTC) is an established alternative to optical colonoscopy in cases of incomplete examination or obstructive colorectal lesions; however, its role in colon cancer management remains incompletely defined and likely underutilized. This review provides a clinically oriented synthesis of current evidence on the role of contrast-enhanced CTC (CE-CTC) in colon cancer, focusing on diagnosing, staging, preoperative planning, and multidisciplinary decision-making. By combining luminal distension with intravenous contrast enhancement, CE-CTC may improve visualization of both intraluminal and transmural tumor extent compared with conventional CT. This approach may facilitate precise tumor localization and assessment of mural invasion and circumferential tumor involvement, thereby supporting clinical decision-making regarding neoadjuvant treatment. Furthermore, detailed mesenteric vascular mapping may support surgical planning by guiding lymph node dissection and central vascular ligation, with the goal of achieving oncologically adequate (R0) resection. In an evolving therapeutic landscape, CE-CTC may serve as a complementary imaging modality to enhance preoperative assessment and support individualized management strategies. Prospective studies are needed to further define its role and integration into clinical pathways.

Emphysema's significant morbidity and mortality underscore the need for reliable outcome metrics in clinical trials. However, commonly accepted chronic obstructive pulmonary disease outcome measures do not adequately capture emphysema severity or progression. Computed tomography (CT) metrics have been validated as accurate indicators of pathological emphysema and predictors of chronic obstructive pulmonary disease progression, exacerbations, and mortality. This position paper reviews the evidence supporting CT densitometry as a biomarker for emphysema, establishes implementation standards, and highlights areas for future research. A systematic literature review addressed three key questions: whether CT densitometry can be used as a diagnostic biomarker of emphysema, whether CT densitometry can be used as a prognostic biomarker, and whether longitudinal change in densitometry can be used as a disease progression monitoring biomarker. Emphysema metrics, such as the percentage of low attenuation areas below -950 Hounsfield units, are validated, highly reproducible diagnostic and prognostic biomarkers. Volume-adjusted lung density is recommended for disease monitoring. Both metrics demonstrate a scan-rescan intraclass correlation coefficient of 0.99 with proper technique. The paper also discusses relevant CT physics, techniques, and sources of variation, including technical factors, physiological changes, and software analysis. Key recommendations for clinical trials include using standardized CT techniques, proper subject selection, and longitudinal evaluation with volume-adjusted lung density.

Computed tomography colonography (CTC), also known as virtual colonoscopy, is a well-tolerated, minimally invasive and effective procedure. Used for over two decades and supported by extensive studies and meta-analyses, CTC has demonstrated performance comparable to that of optical colonoscopy (OC). However, CTC remains generally underutilized in many countries, including the United States of America; in contrast, in some countries, such as the United Kingdom, it is widely used. CTC requires bowel preparation with laxative and fecal contrast-agent tagging, followed by colonic distension with low-pressure, automated, CO2 insufflation. It enables detailed image analysis with postprocessing software and is highly sensitive and specific for detecting cancers and significant benign precursors ≥ 10 mm (adenomatous and sessile-serrated polyps) years before potential malignant transformation. After reviewing the state of the art of CTC acquisition, analysis and reporting, we wrote this article to update the new, potential and emerging CTC indications. CTC is increasingly used after incomplete OC, for undetermined colonic anomalies, in elderly and/or fragile patients or when OC is refused. Recent routine clinical use has broadened CTC’s applications, proving its usefulness in local colon-cancer staging, preoperative laparoscopic surgery planning, and selecting patients with severe diverticular disease for elective sigmoidectomy. Beyond its excellent performance in detecting advanced adenomas and cancers, CTC provides precise staging of locally advanced tumors, guiding decisions on neoadjuvant therapy, and coupled with contrast-enhanced thoracic–abdominal–pelvic acquisition, enables comprehensive, preoperative evaluation for laparoscopic colectomy.

L’analyse de la littérature fait apparaître les résultats de deux essais randomisés et contrôlés réalisés chez des sujets fumeurs ou anciens fumeurs qui montrent une réduction significative de la mortalité par cancer du poumon dans le groupe dépisté par scanner faible dose, comparée à un groupe contrôle. Un programme non randomisé de grande ampleur, multinational et prospectif chez des fumeurs à risque, a montré qu’après 10 scanners de dépistage annuels, le taux de survie spécifique au cancer dépisté était de 81 %. Des recommandations concernant la sélection des candidats au dépistage en fonction de l’âge et du tabagisme cumulé ont pu être établies. Des modèles standardisés d’interprétation des scanners permettent de classer les nodules en fonction de leur typologie et de leurs dimensions. L’absence de lésion suspecte conduit à répéter le scanner à un an ; les lésions hautement suspectes font l’objet d’examens complémentaires, voire de résection chirurgicale, et les lésions indéterminées sont contrôlées par un nouveau scanner à 3 mois ou à 6 mois en fonction de leur taille et du contexte clinique. Un programme organisé de dépistage est déjà mis en place en Angleterre. En Europe, des essais cliniques suivis de recommandations ont été menés dans différents pays. En France, une étude clinique non randomisé est en cours (2400 femmes, fumeuses ou ex fumeuses, entre 50 et 74 ans) afin d’évaluer les comorbidités liées au tabac, les modalités de lecture des scanners et le rôle potentiel de l’intelligence artificielle. L’Institut national du cancer (INCa) est en charge de déployer un programme national au terme d’une étude pilote.

The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment. (c) 2023 SPLF. Published by Elsevier Masson SAS. All rights reserved.

Despite significant therapeutic advances, lung cancer remains the biggest killer among cancers. In France, there is no national screening program against lung cancer. Thus, in this perspective, the Foch Hospital decided to implement a pilot and clinical low-dose CT screening program to evaluate the efficiency of such screening. The purpose of this study was to describe the prevalent findings of this low-dose CT screening program. Participants were recruited in the screening program through general practitioners (GPs), pharmacists, and specialists from June 2023 to June 2024. The inclusion criteria included male or female participants aged 50 to 80 years, current smokers or former smokers who had quit less than 15 years prior, with a smoking history of over 20 pack-years. Chest CT scans were conducted at Foch Hospital using a low-dose CT protocol based on volume mode with a multi-slice scanner (>= 60 slices) without contrast injection. In total, 477 participants were recruited in the CT scan screening, 235 (49%) were males with a median age of 60 years [56-67] and 35 smoke pack-years [29-44] and 242 females (51%) with a median age of 60 years [55-60] and 30 smoke pack-years [25-40]. Eight participants showed positive nodules on CT scan, as a 1.7% rate. 66.7% of diagnosed cancers were in early stages (0-I). It is feasible to implement structured lung cancer screening using low-dose CT in a real-world setting among the general population. This approach successfully identifies most early-stage cancers that could be treated curatively.

Purpose: To compare radiology residents' diagnostic performances to detect pulmonary emboli (PEs) on CT pulmonary angiographies (CTPAs) with deep -learning (DL)-based algorithm support and without. Methods: Fully anonymized CTPAs (n = 207) of patients suspected of having acute PE served as input for PE detection using a previously trained and validated DL -based algorithm. Three residents in their first three years of training, blinded to the index report and clinical history, read the CTPAs first without, and 2 months later with the help of artificial intelligence (AI) output, to diagnose PE as present, absent or indeterminate. We evaluated concordances and discordances with the consensus -reading results of two experts in chest imaging. Results: Because the AI algorithm failed to analyze 11 CTPAs, 196 CTPAs were analyzed; 31 (15.8 %) were PEpositive. Good -classification performance was higher for residents with AI -algorithm support than without (AUROCs: 0.958 [95 % CI: 0.921-0.979] vs. 0.894 [95 % CI: 0.850-0.931], p < 0.001, respectively). The main finding was the increased sensitivity of residents' diagnoses using the AI algorithm (92.5 % vs. 81.7 %, respectively). Concordance between residents (kappa: 0.77 [95 % CI: 0.76-0.78]; p < 0.001) improved with AIalgorithm use (kappa: 0.88 [95 % CI: 0.87-0.89]; p < 0.001). Conclusion: The AI algorithm we used improved between -resident agreements to interpret CTPAs for suspected PE and, hence, their diagnostic performances.

Our objective in this review is to familiarize radiologists with the spectrum of initial and progressive CT manifestations of pulmonary complications observed in adult patients with primary immunodeficiency diseases, including primary antibody deficiency (PAD), hyper-IgE syndrome (HIES), and chronic granulomatous disease (CGD). In patients with PAD, recurrent pulmonary infections may lead to airway remodeling with bronchial wall-thickening, bronchiectasis, mucus-plugging, mosaic perfusion, and expiratory air-trapping. Interstitial lung disease associates pulmonary lymphoid hyperplasia, granulomatous inflammation, and organizing pneumonia and is called granulomatous-lymphocytic interstitial lung disease (GLILD). The CT features of GLILD are solid and semi-solid pulmonary nodules and areas of air space consolidation, reticular opacities, and lymphadenopathy. These features may overlap those of mucosa-associated lymphoid tissue (MALT) lymphoma, justifying biopsies. In patients with HIES, particularly the autosomal dominant type (Job syndrome), recurrent pyogenic infections lead to permanent lung damage. Secondary infections with aspergillus species develop in pre-existing pneumatocele and bronchiectasis areas, leading to chronic airway infection. The complete spectrum of CT pulmonary aspergillosis may be seen including aspergillomas, chronic cavitary pulmonary aspergillosis, allergic bronchopulmonary aspergillosis (ABPA)-like pattern, mixed pattern, and invasive. Patients with CGD present with recurrent bacterial and fungal infections leading to parenchymal scarring, traction bronchiectasis, cicatricial emphysema, airway remodeling, and mosaicism. Invasive aspergillosis, the major cause of mortality, manifests as single or multiple nodules, areas of airspace consolidation that may be complicated by abscess, empyema, or contiguous extension to the pleura or chest wall. CLINICAL RELEVANCE STATEMENT: Awareness of the imaging findings spectrum of pulmonary complications that can occur in adult patients with primary immunodeficiency diseases is important to minimize diagnostic delay and improve patient outcomes. KEY POINTS: • Unexplained bronchiectasis, associated or not with CT findings of obliterative bronchiolitis, should evoke a potential diagnosis of primary autoantibody deficiency. • The CT evidence of various patterns of aspergillosis developed in severe bronchiectasis or pneumatocele in a young adult characterizes the pulmonary complications of hyper-IgE syndrome. • In patients with chronic granulomatous disease, invasive aspergillosis is relatively frequent, often asymptomatic, and sometimes mimicking or associated with non-infectious inflammatory pulmonary lesions.

HomeRadiologyVol. 309, No. 2 PreviousNext Reviews and CommentaryEditorialCure Rate of Lung Cancer Diagnosed at Annual CT ScreeningPhilippe A. Grenier Philippe A. Grenier Author AffiliationsFrom the Department of Clinical Research and Innovation, Hôpital Foch, 40 rue Worth, 92150 Suresnes, France.Address correspondence to the author (email: [email protected]).Philippe A. Grenier Published Online:Nov 7 2023https://doi.org/10.1148/radiol.232698MoreSectionsFull textPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Henschke CI, Miettinen OS, Yankelevitz DF, Libby DM, Smith JP. Radiographic screening for cancer. Proposed paradigm for requisite research. Clin Imaging 1994;18(1):16–20. Crossref, Medline, Google Scholar2. Henschke CI, McCauley DI, Yankelevitz DF, et al. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999;354(9173):99–105. Crossref, Medline, Google Scholar3. Henschke CI, Yankelevitz DF, Libby DM, Pasmantier MW, Smith JP, Miettinen OS; International Early Lung Cancer Action Program Investigators. Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med 2006;355(17):1763–1771. [Published correction appears in N Engl J Med 2008;359(8):877.] Crossref, Medline, Google Scholar4. Henschke CI, Yip R, Shaham D, et al; International Early Lung Cancer Action Program Investigators. A 20-year Follow-up of the International Early Lung Cancer Action Program (I-ELCAP). Radiology 2023;309(2):e231988. Link, Google Scholar5. Dal Maso L, Panato C, Tavilla A, et al; EUROCARE-5 Working Group. Cancer cure for 32 cancer types: results from the EUROCARE-5 study. Int J Epidemiol 2020;49(5):1517–1525. Crossref, Medline, Google Scholar6. Tralongo P, Surbone A, Serraino D, Dal Maso L. Major patterns of cancer cure: Clinical implications. Eur J Cancer Care (Engl) 2019;28(6):e13139. Crossref, Medline, Google Scholar7. Morgan H, Ellis L, O’Dowd EL, Murray RL, Hubbard R, Baldwin DR. What is the Definition of Cure in Non-small Cell Lung Cancer? Oncol Ther 2021;9(2):365–371. Crossref, Medline, Google Scholar8. Carter D, Vazquez M, Flieder DB, et al. ELCAP, NY-ELCAP Comparison of pathologic findings of baseline and annual repeat cancers diagnosed on CT screening. Lung Cancer 2007;56(2):193–199. Crossref, Medline, Google Scholar9. Morrison AS. The effects of early treatment, lead time and length bias on the mortality experienced by cases detected by screening. Int J Epidemiol 1982;11(3):261–267. Google Scholar10. Gierada DS, Pinsky PF. Survival Following Detection of Stage I Lung Cancer by Screening in the National Lung Screening Trial. Chest 2021;159(2):862–869. Crossref, Medline, Google ScholarArticle HistoryReceived: Oct 6 2023Revision requested: Oct 10 2023Revision received: Oct 18 2023Accepted: Oct 18 2023Published online: Nov 07 2023 FiguresReferencesRelatedDetailsRecommended Articles A 20-year Follow-up of the International Early Lung Cancer Action Program (I-ELCAP)Radiology2023Volume: 309Issue: 2Use of Preoperative FDG PET/CT and Survival of Patients with Resectable Non–Small Cell Lung CancerRadiology2022Volume: 305Issue: 1pp. 219-227Radiomics-based Cluster Groups to Predict Clinical-Pathologic and Genomic Characteristics of Stage I Lung AdenocarcinomaRadiology2022Volume: 303Issue: 3pp. 673-674Lung Cancer Deaths in the National Lung Screening Trial Attributed to Nonsolid NodulesRadiology2016Volume: 281Issue: 2pp. 589-596Lung-RADS: Pushing the LimitsRadioGraphics2017Volume: 37Issue: 7pp. 1975-1993See More RSNA Education Exhibits Management of Solitary Pulmonary Nodules: Pushing the Limits Beyond the GuidelinesDigital Posters2019Role Of Radiology In Addressing The Challenge Of Lung Cancer After Lung Transplantation.Digital Posters2021Pulmonary Neuroendocrine Tumors: Where Are We? A Pictorial Review with Radiologic-Pathologic CorrelationDigital Posters2019 RSNA Case Collection Carcinoid tumor of the lungRSNA Case Collection2020Bronchial CarcinoidRSNA Case Collection2020Endobronchial carcinoidRSNA Case Collection2020 Vol. 309, No. 2 Metrics Altmetric Score PDF download

L’apport de l’intelligence artificielle (IA) en imagerie médicale est un sujet qui suscite actuellement de très nombreuses expérimentations. Le développement des méthodes dites en deep learning (DL), en particulier l’usage des réseaux neuronaux de convolution (CNNs), permet des gains de performances comparés aux méthodes classiques de machine learning. La radiomique est une autre approche dont l’objectif est d’extraire, au sein des images exprimant une pathologie, un grand nombre de données quantitatives, non accessibles à l’œil humain, qui viennent alimenter des modèles de machine learning pour fournir des probabilités diagnostiques ou pronostiques. La radiographie thoracique offre un domaine presque parfait pour le développement des algorithmes de DL pour une interprétation automatique des examens. Les algorithmes actuels sont capables de détecter jusqu’à 14 types d’anomalies, quand elles sont présentes, de façon isolée ou en association. Le scanner thoracique est un autre champ important d’application de l’IA. Différents algorithmes sont entraînés, puis validés, spécifiquement pour la détection et caractérisation de nodules pulmonaires, la détection d’embolie pulmonaire ou l’analyse quantitative de l’étendue des maladies diffuses pulmonaires (emphysème, atteintes des bronches et bronchioles, pneumonies infectieuses, pneumonies interstitielles). L’analyse des images peut aussi être associée à celles des données cliniques, biologiques ou fonctionnelles (analyse multi-omics) pour des approches prédictives de pronostic ou de réponse aux traitements.

Purpose: Since the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs. Methods: The validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists. Results: The algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4–95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8–95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes. Conclusions: The DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs.

Interstitial lung disease (ILD), one of the most common extramuscular manifestations of idiopathic inflammatory myopathies (IIMs), carries a poor prognosis. Myositis-specific autoantibody (MSA)-positivity is a key finding for IIM diagnosis. We aimed to identify IIM-associated lung patterns, evaluate potential CT–ILD finding–MSA relationships, and assess intra- and interobserver reproducibility in a large IIM population. All consecutive IIM patients (2003–2019) were included. Two chest radiologists retrospectively assessed all chest CT scans. Multiple correspondence and hierarchical cluster analyses of CT findings identified and characterized ILD-patient subgroups. Classification and regression-tree analyses highlighted CT-scan variables predicting three patterns. Three independent radiologists read CT scans twice to assign patients according to CT–ILD-pattern clusters. Among 257 IIM patients, 94 (36.6%) had ILDs; 87 (93%) of them were MSA-positive. ILD–IIM distribution was 54 (57%) ASyS, 21 (22%) DM, 15 (16%) IMNM, and 4 (4%) IBM. Cluster analysis identified three ILD-patient subgroups. Consolidation characterized cluster 1, with significantly (p < 0.05) more frequent anti-MDA5–autoantibody-positivity. Significantly more cluster-2 patients had a reticular pattern, without cysts and with few consolidations. All cluster-3 patients had cysts and anti-PL12 autoantibodies. Clusters 2 and 3 included significantly more ASyS patients. Intraobserver concordances to classify patients into those three clusters were good-to-excellent (Cohen κ 0.64–0.81), with good interobserver reliability (Fleiss’s κ 0.56). Despite the observed IIM heterogeneity, CT-scan criteria enabled ILD assignment to the three clusters, which were associated with MSAs. Radiologist identification of those clusters could facilitate diagnostic screening and therapeutics. Interstitial lung disease in patients with idiopathic inflammatory myopathy could be classified into three clusters according to CT-scan criteria, and these clusters were significantly associated with myositis-specific autoantibodies. • Cluster analysis discerned three homogeneous groups of interstitial lung disease (ILD) for which cysts, consolidations, and reticular pattern were discriminatory, and associated with myositis-specific autoantibodies. • Like muscle- and extramuscular-specific phenotypes, myositis-specific autoantibodies are also associated with specific ILD patterns in patients with idiopathic inflammatory myopathies.

Journal Article BJR functional imaging of the lung special feature: introductory editorial Get access Philippe A Grenier, Philippe A Grenier Foch Hospital, Suresnes, France Search for other works by this author on: Oxford Academic Google Scholar Eric A Hoffman, Eric A Hoffman University of Iowa Carver College of Medicine, Iowa City, Iowa, USA Search for other works by this author on: Oxford Academic Google Scholar Nicholas Screaton, Nicholas Screaton Royal Papworth Hospital, Cambridge, UK Search for other works by this author on: Oxford Academic Google Scholar Joon Beom Seo Joon Beom Seo University of Ulsan College of Medicine, Songpa-gu, Seoul, South Korea Search for other works by this author on: Oxford Academic Google Scholar British Journal of Radiology, Volume 95, Issue 1132, 1 April 2022, 20229004, https://doi.org/10.1259/bjr.20229004 Published: 21 March 2022

HomeRadiologyVol. 304, No. 3 PreviousNext Reviews and CommentaryEditorialDeep Learning Assessment of Emphysema Progression at CT Predicts OutcomesPhilippe A. Grenier Philippe A. Grenier Author AffiliationsFrom the Department of Clinical Research and Innovation, Hôpital Foch, 40 rue Worth, Suresnes 92150, France.Address correspondence to the author (email: [email protected]).Philippe A. Grenier Published Online:May 17 2022https://doi.org/10.1148/radiol.220627MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Lynch DA , Austin JHM , Hogg JC , et al. CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society. Radiology 2015;277(1):192–205. Link, Google Scholar2. Regan EA , Hokanson JE , Murphy JR , et al. Genetic epidemiology of COPD (COPDGene) study design. COPD 2010;7(1):32–43. Crossref, Medline, Google Scholar3. Bhatt SP , Washko GR , Hoffman EA , et al. Imaging Advances in Chronic Obstructive Pulmonary Disease. Insights from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) Study. Am J Respir Crit Care Med 2019;199(3):286–301. Crossref, Medline, Google Scholar4. Lynch DA , Moore CM , Wilson C , et al. CT-based Visual Classification of Emphysema: Association with Mortality in the COPDGene Study. Radiology 2018;288(3):859–866. Link, Google Scholar5. Ash SY , San José Estépar R , Fain SB , et al. Relationship between Emphysema Progression at CT and Mortality in Ever-Smokers: Results from the COPDGene and ECLIPSE Cohorts. Radiology 2021;299(1):222–231. Link, Google Scholar6. El Kaddouri B , Strand MJ , Baraghoshi D , et al. Fleischner Society Visual Emphysema CT Patterns Help Predict Progression of Emphysema in Current and Former Smokers: Results from the COPDGene Study. Radiology 2021;298(2):441–449. Link, Google Scholar7. Humphries SM , Notary AM , Centeno JP , et al. Deep Learning Enables Automatic Classification of Emphysema Pattern at CT. Radiology 2020;294(2):434–444. Link, Google Scholar8. Oh AS , Baraghoshi D , Lynch DA , et al. Emphysema Progression on CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study. Radiology 2022;304(3):672–679. Link, Google ScholarArticle HistoryReceived: Mar 15 2022Revision requested: Mar 28 2022Revision received: Mar 30 2022Accepted: Apr 1 2022Published online: May 17 2022Published in print: Sept 2022 FiguresReferencesRelatedDetailsAccompanying This ArticleEmphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene StudyMay 17 2022RadiologyRecommended Articles Emphysema at CT in Smokers with Normal Spirometry: Why It Is Clinically SignificantRadiology2020Volume: 296Issue: 3pp. 650-651Spatial Compactness of Emphysema at CT and Disease SeverityRadiology2021Volume: 301Issue: 3pp. 710-711Visual Emphysema at Chest CT in GOLD Stage 0 Cigarette Smokers Predicts Disease Progression: Results from the COPDGene StudyRadiology2020Volume: 296Issue: 3pp. 641-649CT-based Visual Classification of Emphysema: Association with Mortality in the COPDGene StudyRadiology2018Volume: 288Issue: 3pp. 859-866Fleischner Society Visual Emphysema CT Patterns Help Predict Progression of Emphysema in Current and Former Smokers: Results from the COPDGene StudyRadiology2020Volume: 298Issue: 2pp. 441-449See More RSNA Education Exhibits Lung Cancer Screening Beyond the Nodules: Smoking-related Lung DiseaseDigital Posters2020Air trapping in Diffuse Lung Diseases: An Imaging Approach to the Differential Diagnosis with Pathologic Correlation (Mechanisms and Imaging Clues)Digital Posters2022Beyond Idiopathic In Pulmonary Fibrosis: Genetics, Immunology And MoreDigital Posters2021 RSNA Case Collection LymphangioleiomyomatosisRSNA Case Collection2021Pneumocystis Jirovecii Pneumonia RSNA Case Collection2021Bronchial AtresiaRSNA Case Collection2020 Vol. 304, No. 3 Metrics Altmetric Score PDF download

Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk smoker populations have shown a reduction in the number of lung cancer deaths in the screening group compared to a control group. Even if various countries are currently considering the implementation of LCS programs, recurring doubts and fears persist about the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) can potentially increase the efficiency of LCS. The objective of this article is to review the performances of AI algorithms developed for different tasks that make up the interpretation of LCS CT scans, and to estimate how these AI algorithms may be used as a second reader. Despite the reduction in lung cancer mortality due to LCS with LDCT, many smokers die of comorbid smoking-related diseases. The identification of CT features associated with these comorbidities could increase the value of screening with minimal impact on LCS programs. Because these smoking-related conditions are not systematically assessed in current LCS programs, AI can identify individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis and offer an opportunity for treatment and prevention.

BackgroundAlthough advanced medical imaging technologies give detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection of respiratory diseases and follow-ups is still lacking. The novel method of x-ray dark-field chest imaging might fill this gap but has not yet been studied in living humans. Enabling the assessment of microstructural changes in lung parenchyma, this technique presents a more sensitive alternative to conventional chest x-rays, and yet requires only a fraction of the dose applied in CT. We studied the application of this technique to assess pulmonary emphysema in patients with chronic obstructive pulmonary disease (COPD).MethodsIn this diagnostic accuracy study, we designed and built a novel dark-field chest x-ray system (Technical University of Munich, Munich, Germany)—which is also capable of simultaneously acquiring a conventional thorax radiograph (7 s, 0·035 mSv effective dose). Patients who had undergone a medically indicated chest CT were recruited from the department of Radiology and Pneumology of our site (Klinikum rechts der Isar, Technical University of Munich, Munich, Germany). Patients with pulmonary pathologies, or conditions other than COPD, that might influence lung parenchyma were excluded. For patients with different disease stages of pulmonary emphysema, x-ray dark-field images and CT images were acquired and visually assessed by five readers. Pulmonary function tests (spirometry and body plethysmography) were performed for every patient and for a subgroup of patients the measurement of diffusion capacity was performed. Individual patient datasets were statistically evaluated using correlation testing, rank-based analysis of variance, and pair-wise post-hoc comparison.FindingsBetween October, 2018 and December, 2019 we enrolled 77 patients. Compared with CT-based parameters (quantitative emphysema ρ=–0·27, p=0·089 and visual emphysema ρ=–0·45, p=0·0028), the dark-field signal (ρ=0·62, p<0·0001) yields a stronger correlation with lung diffusion capacity in the evaluated cohort. Emphysema assessment based on dark-field chest x-ray features yields consistent conclusions with findings from visual CT image interpretation and shows improved diagnostic performance than conventional clinical tests characterising emphysema. Pair-wise comparison of corresponding test parameters between adjacent visual emphysema severity groups (CT-based, reference standard) showed higher effect sizes. The mean effect size over the group comparisons (absent–trace, trace–mild, mild–moderate, and moderate–confluent or advanced destructive visual emphysema grades) for the COPD assessment test score is 0·21, for forced expiratory volume in 1 s (FEV1)/functional vital capacity is 0·25, for FEV1% of predicted is 0·23, for residual volume % of predicted is 0·24, for CT emphysema index is 0·35, for dark-field signal homogeneity within lungs is 0·38, for dark-field signal texture within lungs is 0·38, and for dark-field-based emphysema severity is 0·42.InterpretationX-ray dark-field chest imaging allows the diagnosis of pulmonary emphysema in patients with COPD because this technique provides relevant information representing the structural condition of lung parenchyma. This technique might offer a low radiation dose alternative to CT in COPD and potentially other lung disorders.FundingEuropean Research Council, Deutsche Forschungsgemeinschaft, Royal Philips, and Karlsruhe Nano Micro Facility.

The purpose of our work was to assess the independent and incremental value of AI-derived quantitative determination of lung lesions extent on initial CT scan for the prediction of clinical deterioration or death in patients hospitalized with COVID-19 pneumonia. 323 consecutive patients (mean age 65 ± 15 years, 192 men), with laboratory-confirmed COVID-19 and an abnormal chest CT scan, were admitted to the hospital between March and December 2020. The extent of consolidation and all lung opacities were quantified on an initial CT scan using a 3D automatic AI-based software. The outcome was known for all these patients. 85 (26.3%) patients died or experienced clinical deterioration, defined as intensive care unit admission. In multivariate regression based on clinical, biological and CT parameters, the extent of all opacities, and extent of consolidation were independent predictors of adverse outcomes, as were diabetes, heart disease, C-reactive protein, and neutrophils/lymphocytes ratio. The association of CT-derived measures with clinical and biological parameters significantly improved the risk prediction (p = 0.049). Automated quantification of lung disease at CT in COVID-19 pneumonia is useful to predict clinical deterioration or in-hospital death. Its combination with clinical and biological data improves risk prediction.