Journal of Thoracic Imaging最新文献

{"title":"Artificial Intelligence in Coronary Computed Tomography: Current Applications, Future Potentials, and Real-world Challenges.","authors":"Lorenzo Giarletta, Brian Zhou, Riccardo Marano, Carlo N De Cecco, Marly van Assen","doi":"10.1097/RTI.0000000000000873","DOIUrl":"10.1097/RTI.0000000000000873","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly transforming cardiac computed tomography (CT) imaging by enhancing image acquisition, reconstruction, and analysis to improve diagnostic accuracy and overall clinical workflow. Deep learning reconstruction (DLR) algorithms optimize image quality while reducing radiation and contrast media doses. AI-driven tools for coronary artery segmentation and CAD-RADS classification ensure greater reproducibility and efficiency in coronary artery disease (CAD) assessment. Beyond anatomic evaluation, AI enhances functional imaging with CT-derived fractional flow reserve and myocardial CT perfusion imaging, improving the noninvasive identification of myocardial ischemia associated with flow-limiting coronary lesions. AI also plays a key role in CAD phenotyping through automating quantification and characterization of total plaque burden and identifying rupture-prone plaques and high-risk patients. Radiomics and machine learning models analyzing pericoronary adipose tissue (PCAT) propose new biomarkers of coronary inflammation, refining risk stratification and disease monitoring. Fusion models integrating clinical, imaging, and laboratory data are emerging as powerful tools for comprehensive cardiovascular risk prognostication, surpassing traditional clinical risk scores. Looking ahead, generative AI and large language models (LLMs) could revolutionize radiology workflows by automating report generation and relevant clinical data extraction and integration, while digital twins may enable real-time simulation of patient-specific models that predicts disease progression and treatment response. Despite these advances, challenges like data diversity and standardization, model interpretability, and regulatory approval must be further addressed for AI to reach full integration into clinical practice. As AI-driven technologies continue to evolve, interdisciplinary collaboration will be essential to ensure responsible implementation, ultimately advancing precision medicine in cardiovascular care.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145960668","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":"Artificial Intelligence in Cardiovascular MRI: From Imaging to Biomechanics and Diagnosis.","authors":"Amin Mahmoodi, Akhilesh Yeluru, Jerjes Aguirre-Chavez, Kathryn Lamar-Bruno, Karan Punjabi, Shant Malkasian, Albert Song, Evan Masutani, Albert Hsiao","doi":"10.1097/RTI.0000000000000864","DOIUrl":"10.1097/RTI.0000000000000864","url":null,"abstract":"<p><p>In this review, we highlight how artificial intelligence, specifically deep learning, is reshaping every aspect of cardiovascular magnetic resonance imaging: from planning and acquisition to reconstruction, analysis, and clinical report generation. We first introduce core machine learning paradigms and concepts, then survey recent deep learning advances to automate and enhance multiple aspects of MRI. We highlight the range of recent advances to provide a conceptual understanding of how the field has rapidly evolved in the last 10 years, enabling improvements in acquisition speed, spatial resolution, suppression of artifacts, and correction for motion. Automation of postprocessing is providing us a deeper look into detailed analysis of regional cardiac function and measurement of hemodynamics, and a greater ability to automatically integrate interpretation with nonimaging clinical data to support prognostication and management. Advances in artificial intelligence will continue to shape our practice of clinical cardiovascular MRI to provide greater efficiency and enrich our ability to guide the management of patients with cardiovascular disease.</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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12810196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145649848","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":"Quantitative CT and Artificial Intelligence in Chronic Lung Disease.","authors":"Andrea S Oh, Stephen M Humphries, Augustine Chung, S Samuel Weigt, Matthew Brown, Grace Hyun J Kim, David Lee, John A Belperio, Jonathan G Goldin","doi":"10.1097/RTI.0000000000000867","DOIUrl":"10.1097/RTI.0000000000000867","url":null,"abstract":"<p><p>Computed tomography (CT) is routinely used in diagnosing and managing patients with chronic lung diseases such as chronic obstructive pulmonary disease (COPD) and fibrosing interstitial lung disease (ILD). Visual assessment of disease morphology/phenotype and extent correlates with lung function and patient prognosis, but it is limited by reader subjectivity and interobserver variability. Quantitative CT (QCT) techniques based on density and texture-based features of the lungs have shown stronger correlations with physiologic and survival outcomes in both COPD and ILD cohort studies. Moreover, recent advances in computer processing capabilities have led to the implementation of machine and deep learning-based approaches, allowing for greater robustness and reproducibility beyond visual assessment and density-based methods. This review focuses on QCT and artificial intelligence (AI) techniques for COPD, ILD, and bronchiolitis obliterans syndrome in lung and hematopoietic stem cell transplant recipients. Current challenges and limitations for adoption of these techniques and future directions of QCT and AI in thoracic imaging are also discussed.</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":"145795266","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":"Quantitative Chest Computed Tomography and Machine Learning for Subphenotyping Small Airways Disease in Long COVID.","authors":"Rodrigo Caruso Chate, Carlos Roberto Ribeiro Carvalho, Marcio Valente Yamada Sawamura, João Marcos Salge, Eduardo Kaiser Ururahy Nunes Fonseca, Paula Terra Martins Almeida Amaral, Celina de Almeida Lamas, Luis Augusto Visani de Luna, Fernando Uliana Kay, Antonildes Nascimento Assunção Junior, Cesar Higa Nomura","doi":"10.1097/RTI.0000000000000861","DOIUrl":"10.1097/RTI.0000000000000861","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate imaging phenotypes in posthospitalized COVID-19 patients by integrating quantitative CT (QCT) and machine learning (ML), with a focus on small airway disease (SAD) and its correlation with plethysmography.</p><p><strong>Materials and methods: </strong>In this single-center cross-sectional retrospective study, a subanalysis of a larger prospective cohort, 257 adult survivors from the initial COVID-19 peak (mean age, 56±13 y; 49% male) were evaluated. Patients were admitted to a quaternary hospital between March 30 and August 31, 2020 (median length of stay: 16 [8-26] d) and underwent plethysmography along with volumetric inspiratory and expiratory chest CT 6 to 12 months after hospitalization. QCT parameters were derived using AI-Rad Companion Chest CT (Siemens Healthineers).</p><p><strong>Results: </strong>Hierarchical clustering of QCT parameters identified 4 phenotypes among survivors, named \"SAD,\" \"intermediate,\" \"younger fibrotic,\" and \"older fibrotic,\" based on clinical and imaging characteristics. The SAD cluster (n=37, 14%) showed higher residual volume (RV) and RV/total lung capacity (TLC) ratios as well as lower FEF 25-75 /forced vital capacity (FVC) on plethysmography. The older fibrotic cluster (n=42, 16%) had the lowest TLC and FVC values. The younger fibrotic cluster (n=79, 31%) demonstrated lower RV and RV/TLC ratios and higher FEF 25-75 than the other phenotypes. The intermediate cluster (n=99, 39%) exhibited characteristics that were intermediate between those of SAD and fibrotic phenotypes.</p><p><strong>Conclusion: </strong>The integration of inspiratory and expiratory chest CT with quantitative analysis and ML enables the identification of distinct imaging phenotypes in long COVID patients, including a unique SAD cluster strongly associated with specific pulmonary function abnormalities.</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":"145304350","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":"Preface for Symposium on Artificial Intelligence in Cardiothoracic Imaging.","authors":"Chi Wan Koo","doi":"10.1097/RTI.0000000000000851","DOIUrl":"10.1097/RTI.0000000000000851","url":null,"abstract":"","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":"41 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259753","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":"Bridging the Gap: A Comprehensive Review of Radiology and Pathology in Acute Lung Injury.","authors":"Ana P S Lima, Desiree A Marshall, Eric Morrell, Sudhakar N J Pipavath","doi":"10.1097/RTI.0000000000000837","DOIUrl":"10.1097/RTI.0000000000000837","url":null,"abstract":"<p><p>Acute respiratory distress syndrome (ARDS) is a life-threatening condition characterized by widespread inflammation in the lungs. It is associated with high mortality and morbidity in critically ill patients. ARDS are conditions that cause acute respiratory failure due to noncardiogenic pulmonary edema, leading to severe hypoxemia and diffuse, bilateral lung injury. These conditions represent a spectrum of lung injury with varying severity and complexity. ARDS is a more severe form of ALI. ALI can also describe a range of clinical and paraclinical findings that include one or both pathologic patterns of organizing pneumonia (OP) or diffuse alveolar damage (DAD). The pathologic correlate of ARDS is DAD. This damage can be triggered by various risk factors, including pneumonia, sepsis, trauma, and the inhalation of harmful substances. The alveolar capillary damage that accompanies DAD leads to a loss in barrier function and is associated with the accumulation of fluid into the alveolar space. This fluid accumulation (pulmonary edema), along with subsequent organization and scarring, impairs gas exchange, which leads to hypoxemia and respiratory failure. Despite advances in understanding the pathophysiology of ARDS and improvements in supportive care, the mortality rates from ARDS still range from 25% to 45%. It is crucial to recognize that radiographic and histologic findings in a patient with ARDS can vary significantly depending on the phase of the disease. This is because the pathophysiological processes underlying these conditions evolve over time, leading to changes in both clinical presentation and imaging findings. Misinterpretation of these findings could lead to incorrect diagnoses and inappropriate treatment strategies. Therefore, understanding the temporal evolution of this condition is essential for accurate diagnosis and effective management. Our paper seeks to examine the existing literature focusing on radiology and pathology at different phases of injury and resolution to enhance management of ARDS.</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":"144477632","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":"Bridging the Gap Between Radiology and Microscopy Using microCT: Implications for Neoplastic and Non-neoplastic Lung Disease.","authors":"Stijn E Verleden, Annemiek Snoeckx, Dieter Peeters, Wen Wen, Reinier Wener, Paul Van Schil, Senada Koljenovic, Annelies Janssens, Danny D Jonigk, Maximilian Ackermann, Therese S Lapperre, Jeroen M H Hendriks","doi":"10.1097/RTI.0000000000000847","DOIUrl":"10.1097/RTI.0000000000000847","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate lung cancer TNM staging depends on macroscopic and microscopic tumor evaluation of resection specimens. However, small nodules (<1 cm) are difficult to extract and correlate with in vivo imaging. We investigated whether microCT could better localize lesions or guide pathology to otherwise undetected abnormalities.</p><p><strong>Materials and methods: </strong>Paired ex vivo CT and microCT were performed after inflating and freezing surgically removed lung lobes (resolution 80 to 120 µm). Rigorous matching between CT, microCT, and histopathology was performed on areas containing abnormalities on microCT.</p><p><strong>Results: </strong>A total of 57 lobectomy specimens were analyzed. MicroCT-guided microscopic examination led to 2 additional primary carcinomas, 2 separate tumor nodules from the primary lung tumor, and 1 atypical adenomatous hyperplasia lesion that were not evident before surgery. For both patients with separate tumor nodules, the cT1 stage was upgraded to a pT3. In addition, the microCT provided insight into underlying structural disease (ie, emphysema and fibrosis).</p><p><strong>Conclusions: </strong>In 5 out of 57 resection specimens (9%), microCT showed additional (pre-)cancerous lesions. This explorative study suggests that lobar microCT could serve as a valuable guide for pathologists by pointing them toward areas that may warrant further investigation. In this way, it is a practical and beneficial tool, capable of facilitating a more precise TNM classification in tumor resection specimens, which needs further validation in a prospective study.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976726","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":"Pulmonary Embolism Survival Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data.","authors":"Zhusi Zhong, Helen Zhang, Fayez H Fayad, Andrew C Lancaster, John Sollee, Shreyas Kulkarni, Cheng Ting Lin, Jie Li, Xinbo Gao, Scott Collins, Colin F Greineder, Sun H Ahn, Harrison X Bai, Zhicheng Jiao, Michael K Atalay","doi":"10.1097/RTI.0000000000000831","DOIUrl":"10.1097/RTI.0000000000000831","url":null,"abstract":"<p><strong>Purpose: </strong>Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using computed tomography pulmonary angiography (CTPA), clinical data, and PE Severity Index (PESI) scores to predict PE survival.</p><p><strong>Materials and methods: </strong>In total, 918 patients (median age 64 y, range 13 to 99 y, 48% male) with 3978 CTPAs were identified via retrospective review across 3 institutions. To predict survival, an AI model was used to extract disease-related imaging features from CTPAs. Imaging features and clinical variables were then incorporated into independent DL models to predict survival outcomes. Cross-modal fusion CoxPH models were used to develop multimodal models from combinations of DL models and calculated PESI scores. Five multimodal models were developed as follows: (1) using CTPA imaging features only, (2) using clinical variables only, (3) using both CTPA and clinical variables, (4) using CTPA and PESI score, and (5) using CTPA, clinical variables, and PESI score. Performance was evaluated using the concordance index (c-index). Kaplan-Meier analysis was performed to stratify patients into high-risk and low-risk groups. Additional factor-risk analysis was conducted to account for right ventricular (RV) dysfunction.</p><p><strong>Results: </strong>For both data sets, the multimodal models incorporating CTPA features, clinical variables, and PESI score achieved higher c-indices than PESI alone. Following the stratification of patients into high-risk and low-risk groups by models, survival outcomes differed significantly (both P <0.001). A strong correlation was found between high-risk grouping and RV dysfunction.</p><p><strong>Conclusions: </strong>Multiomic DL models incorporating CTPA features, clinical data, and PESI achieved higher c-indices than PESI alone for PE survival prediction.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812346","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":"Real-world Evaluation of Computer-aided Pulmonary Nodule Detection Software Sensitivity and False Positive Rate.","authors":"Raquelle El Alam, Khushboo Jhala, Mark M Hammer","doi":"10.1097/RTI.0000000000000835","DOIUrl":"10.1097/RTI.0000000000000835","url":null,"abstract":"<p><strong>Purpose: </strong>Evaluate the false positive rate (FPR) of nodule detection software in real-world use.</p><p><strong>Materials and methods: </strong>A total of 250 nonenhanced chest computed tomography (CT) examinations were randomly selected from an academic institution and submitted to the ClearRead nodule detection system (Riverain Technologies). Detected findings were reviewed by a thoracic imaging fellow. Nodules were classified as true nodules, lymph nodes, or other findings (branching opacity, vessel, mucus plug, etc.), and FPR was recorded. FPR was compared with the initial published FPR in the literature. True diagnosis was based on pathology or follow-up stability. For cases with malignant nodules, we recorded whether malignancy was detected by clinical radiology report (which was performed without software assistance) and/or ClearRead.</p><p><strong>Results: </strong>Twenty-one CTs were excluded due to a lack of thin-slice images, and 229 CTs were included. A total of 594 findings were reported by ClearRead, of which 362 (61%) were true nodules and 232 (39%) were other findings. Of the true nodules, 297 were solid nodules, of which 79 (27%) were intrapulmonary lymph nodes. The mean findings identified by ClearRead per scan was 2.59. ClearRead mean FPR was 1.36, greater than the published rate of 0.58 ( P <0.0001). If we consider true lung nodules <6 mm as false positive, FPR is 2.19. A malignant nodule was present in 30 scans; ClearRead identified it in 26 (87%), and the clinical report identified it in 28 (93%) ( P =0.32).</p><p><strong>Conclusion: </strong>In real-world use, ClearRead had a much higher FPR than initially reported but a similar sensitivity for malignant nodule detection compared with unassisted radiologists.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044902","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":"The Society of Thoracic Radiology Mentorship Program: A Paradigm for Professional Societies.","authors":"Georgeann McGuinness, Linda B Haramati, Chi Wan Koo, Baskaran Sundaram","doi":"10.1097/RTI.0000000000000834","DOIUrl":"10.1097/RTI.0000000000000834","url":null,"abstract":"<p><p>The Society of Thoracic Radiology (STR) membership enthusiastically embraced the launch of its mentorship program, with peaks in participation and engagement after annual meetings and during the COVID pandemic. The program provides a valuable resource for early to mid-career thoracic radiologists, especially those lacking local resources. This report describes the program's inception and design, and summarizes the program's successes and challenges at 5 years, based on a 2023 mentorship survey. STR mentees, spanning early to mid-career stages, most frequently sought mentorship in career development, graduate medical education, research portfolio development, publishing, cardiac imaging, grant funding, and artificial intelligence. Mentors offered expertise in these areas, plus lung cancer screening, career development, and workplace navigation. The committee prioritized creating dyads based on mutual interest and expertise, achieving mutual top-choice match rates of 70% to 97%. Enduring dyads flourished as the program matured. At 5 years, a survey of participants was fielded. Mentees reported moderate to high program impact on scholarly activities, leadership, networking, clinical service, education, and career satisfaction. Mentors described satisfaction in their roles, highlighting networking, career satisfaction, and the opportunity to influence upcoming generations of cardiothoracic radiologists, thereby impacting the field's future. Most participants expressed high career satisfaction. Descriptive comments further enriched findings. Survey results confirmed that strengthening dyad formation and enhancing mentoring outcomes remain pivotal. Remote mentorship, while valuable, presents challenges-personal connections and contextual familiarity, considered essential to successful mentorship relationships, are typically absent in these settings. Activities to potentially enhance the STR mentorship program are offered.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057097","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}