Journal of Thoracic Imaging最新文献

{"title":"60 kVp Coronary CT Angiography as a Screening Tool on Asymptomatic Patients: An Initial Experience.","authors":"Yicheng Han, Liying Peng, Guozhi Zhang, Shifeng Yang, Congshan Ji, Hui Gu, Ximing Wang","doi":"10.1097/RTI.0000000000000869","DOIUrl":"10.1097/RTI.0000000000000869","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the feasibility of using 60 kVp coronary CT angiography (CCTA) combined with deep learning-based CT reconstruction as a screening tool on asymptomatic patients.</p><p><strong>Materials and methods: </strong>A total of 156 asymptomatic patients (body mass index, 24.4 ± 2.2 kg/m 2 ) with at least one coronary artery disease (CAD) risk factor were prospectively enrolled for taking an experimental ultra-low dose 60 kVp CCTA followed by a routine 120 kVp CCTA. Stenosis detection, plaque analysis, and image quality assessment were performed on both scans, with 120 kVp CCTA serving as the reference.</p><p><strong>Results: </strong>The mean effective dose and mean contrast medium (CM) dosage were 0.4 ± 0.1 mSv and 27.0 ± 3.2 mL, respectively, for 60 kVp CCTA, corresponding to a 91.5% and 50.0% reduction as compared with 120 kVp CCTA. In both analyses for all plaque types and noncalcific plaques, the sensitivity, specificity, and accuracy in stenosis detection were >92% with 60 kVp CCTA on per-segment, per-vessel, and per-patient basis, and in particular, the negative predictive value was ≥ 97%. However, compared to 120 kVp CCTA, 60 kVp CCTA led to a significant overestimation in plaque volume and stenosis severity ( P <0.01), as well as inferior subjective scores regarding vessel and lumen delineation ( P <0.05).</p><p><strong>Conclusions: </strong>Despite overestimation in plaque volume and stenosis severity, 60 kVp CCTA showed excellent stenosis detection capability with ultra-low radiation dose and reduced CM dosage that may potentially be adopted as a screening tool for asymptomatic patients in routine practice.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145710282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Retrosternal Adhesions Using Preoperative CT to Predict Cardiovascular Injury During Sternotomy.","authors":"André Vaz, Ludmila Mintzu Young, Marcelo Biscegli Jatene, Fabio Biscegli Jatene, Leonardo Augusto Miana","doi":"10.1097/RTI.0000000000000870","DOIUrl":"10.1097/RTI.0000000000000870","url":null,"abstract":"<p><strong>Purpose: </strong>To identify preoperative CT findings linked to retrosternal adherence-related intraoperative cardiovascular injury and develop predictive scores with the potential to guide surgical planning.</p><p><strong>Materials and methods: </strong>A retrospective study was conducted on patients undergoing CT within 30 days of sternotomy (first sternotomy or resternotomy) from 2019 to 2023. CT images were reviewed for retrosternal adherence patterns, classified as distance, contact, or adherence, and localized by segment (upper, middle, or lower retrosternal thirds) or by organ (innominate vein, aorta, right ventricle, right atrium, or pulmonary artery). Logistic regression was used to identify the significant predictors from which the scores were developed.</p><p><strong>Results: </strong>Out of 429 patients, 105 (24%) had cardiovascular injuries, including re-entry and postcardiopulmonary bypass injuries. Middle third adherence ( P <0.001), calcification ( P <0.001), and age ( P =0.002) were significant predictors in the segment approach. Aortic ( P =0.001) and right atrial ( P =0.034) adherence, calcification ( P <0.001), and age ( P =0.001) were significant predictors in the organ-specific approach. CAST (Calcification, Age, Sternal Thirds) and ARCA (Aorta, Right Atrium, Calcification, Age) scores were derived to predict intraoperative cardiovascular injuries.</p><p><strong>Conclusions: </strong>Preoperative CT can identify patients at high risk for intraoperative cardiovascular injury during sternotomy. The CAST and ARCA scores offer a reliable, CT-based approach for assessing this risk, potentially enhancing surgical planning and preemptive intervention strategies, thereby improving outcomes in high-risk cardiac reoperations.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146004496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic Significance of Phase-Resolved Functional Lung Low-field Magnetic Resonance Imaging in Comparison to Photon-Counting CT and Pulmonary Function Tests in Connective Tissue Disease-associated Interstitial Lung Diseases.","authors":"Ramona Muecke, Iram Shahzadi, Gunter Assmann, Michael Schmidt, Julius Henning Niehoff, Jens Vogel-Claussen, Andreas Voskrebenzev, Robert Grimm, Lynn Johann Frohwein, Saher Saeed, Jan Borggrefe, Christoph Moenninghoff","doi":"10.1097/RTI.0000000000000872","DOIUrl":"10.1097/RTI.0000000000000872","url":null,"abstract":"<p><strong>Purpose: </strong>The diagnosis of connective tissue disease-associated interstitial lung diseases (CTD-ILD) is connected to radiation exposure due to periodical CT scans. This study aims to investigate the alternative imaging method, Phase-Resolved Functional Lung (PREFUL), regarding its performance in low-field MRI. A comparison of PREFUL, photon-counting CT (PCCT) and pulmonary function tests (PFT) was performed to identify correlations that could restructure the diagnostics of CTD-ILD.</p><p><strong>Materials and methods: </strong>In this prospective single-center study, free-breathing PREFUL acquisitions of CTD-ILD patients were done after clinically indicated PCCT imaging. The severity and extent of CTD-ILD in PCCT were assessed via the Warrick score and used as a reference. Spearman's correlation coefficient ( r ) was calculated to examine the association between PREFUL, PCCT, and PFT.</p><p><strong>Results: </strong>The data of 31 CTD-ILD patients (64.32±12.36 y, 10 men) were evaluated. Most correlations of PREFUL parameters with PFT were found with the Tiffeneau-Pinelli index (FEV1/FVC). The Warrick score showed excellent inter-rater agreement and correlations ( P <0.05) with the PFT parameters forced vital capacity (FVC) and the diffusing capacity of the lung for carbon monoxide corrected for hemoglobin (DLCOc) [FVC: r =-0.43, DLCOc SB: r =-0.65, DLCOc/VA: r =-0.50]. No correlation was found between PREFUL parameters and PCCT.</p><p><strong>Conclusions: </strong>The feasibility of PREFUL using low-field MRI was demonstrated in patients with CTD-ILD. Several correlations between PREFUL and PFT parameters were found, indicating that MRI can quantify lung function impairment. Nevertheless, CT remains the gold standard for CTD-ILD assessment and further research in PREFUL is needed.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13095058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145901417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not a Fluke: Pulmonary Manifestations of Parasitic Infection.","authors":"Andrew E Moore, Jeffrey P Kanne, Jennifer A Febbo","doi":"10.1097/RTI.0000000000000890","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000890","url":null,"abstract":"<p><p>Parasitic infections affect approximately two billion people globally, most commonly in developing countries in the tropics and subtropical regions. However, they are being seen in developed countries with increasing frequency due to immigration and travel. Many parasitic infections present with nonspecific and overlapping clinical findings, and many present with chest involvement. Thoracic manifestations of parasitic infection can be subdivided into 2 major categories: those that result in nondestructive-type physiological response with patchy, migratory, and transient pulmonary opacities, and those that undergo a locally destructive-type manifestation with lung cysts or linear tracts, smooth muscle denervation, chronic and recurrent inflammation, or even osseous destruction. Considering a third nontraditional category of parasitic infections that rarely result in thoracic pathology can be a useful diagnostic tool to broaden the differential diagnosis and assist the radiologist in considering additional pathogen disease patterns. Recognizing the temporal, zonal, and other classic thoracic manifestations in conjunction with the common systemic imaging findings can assist in accurate and timely diagnosis.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Condensed Imaging Review of Thoracic Tuberculosis.","authors":"Naomi Pygott, Reem Bedir, Judith L Babar, Maria T A Wetscherek","doi":"10.1097/RTI.0000000000000884","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000884","url":null,"abstract":"<p><p>Tuberculosis (TB) remains a challenging global health issue and is considered the leading cause of death from infectious disease. Imaging plays a pivotal role in the diagnosis and management of tuberculosis. This review aims to provide a comprehensive overview of the disease mechanisms and associated radiologic features in active thoracic TB, including dissemination routes, covering pulmonary and extrapulmonary thoracic TB (lymph node, pleural, cardiovascular, and musculoskeletal involvement). We highlight TB complications, diagnostic challenges including post-tuberculous lung disease, imaging correlations with sputum smear positivity, and diagnostic mimics. The review emphasises special clinical scenarios that may pose diagnostic challenges, such as immunocompromised patients with human immunodeficiency virus infection, organ transplantation, and autoimmune disorders treated with biological agents, and drug-resistant TB. We discuss the imaging modalities used in the assessment of thoracic TB and the radiographic monitoring recommendations. A good understanding and recognition of the whole spectrum of disease patterns is crucial for individual patient care as well as for controlling TB spread in the community.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147640329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editors' Recognition for Reviewing in 2025.","authors":"Jeffrey P Kanne, Dorith Shaham, Prabhakar Rajiah","doi":"10.1097/RTI.0000000000000880","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000880","url":null,"abstract":"","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147505809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Global Perspective on Viral Pneumonia.","authors":"Maya Vella, Brett M Elicker","doi":"10.1097/RTI.0000000000000874","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000874","url":null,"abstract":"<p><p>There is growing global recognition of the importance of viruses as a cause of lower respiratory tract infections (LRIs) from widespread outbreaks such as the recent COVID-19 pandemic, to seasonal viruses such as those caused by influenza and adenovirus. Worldwide, viruses are a common cause of respiratory infections and mortality due to LRIs. Advances in rapid virus detection have led to a greater understanding of their prevalence and importance as etiological agents of illness. With multiple epidemics and pandemics of viral pneumonia over the past several decades, there is an increasing need to understand the global variations and impacts of these viruses. This review summarizes recent global trends in viral pneumonias and provides an overview of current diagnostic, treatment, and imaging challenges in the setting of viral infections.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147464189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variability in Mediastinal Lymph Node Measurements in Chest Contrast-enhanced CT: Time to Change the Paradigm?","authors":"Alon Olesinksi, Richard Lederman, Yusef Azraq, Leo Joskowicz, Jacob Sosna","doi":"10.1097/RTI.0000000000000859","DOIUrl":"10.1097/RTI.0000000000000859","url":null,"abstract":"<p><strong>Purpose: </strong>Measurement of mediastinal lymph nodes (LNs) is an integral part of patient assessment, and is performed by manually measuring the short axis length (SAL) of the LNs on axial slices. LNs with SAL ≥10 mm are considered pathologically enlarged. We aimed to quantify the interobserver agreement and variability of SAL measurements, compare them to automatically computed SALs from manual LN delineations, and establish the mean SAL measurement error.</p><p><strong>Materials and methods: </strong>Two radiologists independently measured the SALs of 451 LNs in 40 contrast-enhanced chest CT (CECT) scans. One of them also manually delineated the LN contours in each CECT slice, and this served to automatically classify LN as normal/enlarged based on their SALs. Differences between SAL measurements and Bland-Altman statistics were computed.</p><p><strong>Results: </strong>The normal/enlarged LN overall agreement (371 normal, 52 enlarged) between both radiologists was 93.8% (423/451). For agreement/disagreement, the SAL differences were 1.1 (1.0) mm (17%) and 3.5 (3.2) mm (40%). The disagreement differences were nearly twice as large as the agreement differences. The agreement between the manual and the computed SALs for both radiologists was 92.7% (418/451), similar to the interobserver variability.</p><p><strong>Conclusion: </strong>Classification of mediastinal lymph nodes based on SAL measurements demonstrates high agreement. It indicates that SAL measurements automatically computed from manual LN delineations could be a reliable and time-saving tool. In cases of disagreement, the ±2 mm error supports the use of 3 size categories: normal (<8 mm), possibly enlarged (8 to 12 mm), and definitely enlarged (>12 mm).</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chest Computed Tomography-Based Radiomics and Machine Learning for Classifying Mediastinal Lymphadenopathy Caused By Hematologic Malignancies and Metastatic Abdominopelvic Solid Cancers.","authors":"Haoru Wang, Qian Hu, Yingxue Tong, Huiru Zhu, Ling He, Jinhua Cai","doi":"10.1097/RTI.0000000000000860","DOIUrl":"10.1097/RTI.0000000000000860","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the role of chest CT radiomics in classifying mediastinal lymphadenopathy caused by hematologic malignancies and abdominopelvic solid cancers.</p><p><strong>Materials and methods: </strong>A total of 231 patients with mediastinal lymphadenopathy were selected from the Mediastinal-Lymph-Node-SEG collection in The Cancer Imaging Archive, including 145 patients with hematologic malignancies (74 with chronic lymphocytic leukemia and 71 with lymphoma) and 86 with abdominopelvic solid cancers. Patients were randomly stratified into train and test sets in a 7:3 ratio. Radiomics features were extracted from enhanced CT images of mediastinal lymph nodes, followed by feature selection using univariate analysis and least absolute shrinkage and selection operator regression. A support vector machine algorithm was used to develop classification models, with performance evaluated using the area under the receiver operating characteristic curve (AUC-ROC), accuracy, and 95% CI.</p><p><strong>Results: </strong>For differentiating mediastinal lymphadenopathy between hematologic malignancies and abdominopelvic solid cancers, the model incorporated 23 features and achieved an AUC-ROC of 0.931 (95% CI: 0.891-0.971) and an accuracy of 0.866 in the train set, and an AUC-ROC of 0.830 (95% CI: 0.730-0.929) and an accuracy of 0.759 in the test set. For distinguishing chronic lymphocytic leukemia from lymphoma, the model utilized 4 features, achieving an AUC-ROC of 0.880 (95% CI: 0.813-0.947) and an accuracy of 0.752 in the train set, and an AUC-ROC of 0.872 (95% CI: 0.763-0.982) and an accuracy of 0.836 in the test set.</p><p><strong>Conclusions: </strong>Chest CT radiomics shows promise for classifying mediastinal lymphadenopathy in patients with hematologic malignancies and abdominopelvic solid cancers.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Artificial Intelligence to Transform Thoracic Radiology for Lung Nodules and Lung Cancer: Applications, Challenges, and Future Directions.","authors":"Geewon Lee, Hwan-Ho Cho, Dong Young Jeong, Jong Hoon Kim, You Jin Oh, Sung Goo Park, Ho Yun Lee","doi":"10.1097/RTI.0000000000000866","DOIUrl":"10.1097/RTI.0000000000000866","url":null,"abstract":"<p><p>This review traces the historical path of artificial intelligence (AI) methods that have been applied to medical image interpretation. Early AI approaches, which were based on clinical expertise and domain-specific medical knowledge, established the basis for data-driven methods, initiating the radiomics era and leading to the widespread use of deep learning in medical imaging. More recently, transformer architectures-originally developed for natural language processing-have been adapted for medical image analysis. In the first section, we explore the literature on the use of AI, specifically addressing lung nodules and lung cancer. AI has been effective in detecting lung nodules, evaluating their characteristics, and predicting cancer risk, while also addressing technical issues like kernel conversion. In lung cancer, AI has been applied to various clinical needs, including prognosis evaluation, mutation identification, treatment response analysis, operability prediction, treatment-related pneumonitis, and clinical information extraction. In the following section, we explore foundation models, multimodal AI, and a multiomic approach in the field of lung nodules and lung cancer. Finally, as AI models continue to evolve, so too must the approaches for evaluating their real-world utility; thus, we outline relevant methods for evaluating the performance and application of AI in thoracic radiology.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145543660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}