Journal of Thoracic Imaging最新文献

{"title":"Coronary Atherosclerosis Progression Provides Incremental Prognostic Value and Optimizes Risk Reclassification by Computed Tomography Angiography.","authors":"Qingchao Meng, Yunqiang An, Li Zhao, Na Zhao, Hankun Yan, Jingxi Wang, Yutao Zhou, Bin Lu, Yang Gao","doi":"10.1097/RTI.0000000000000793","DOIUrl":"10.1097/RTI.0000000000000793","url":null,"abstract":"<p><strong>Purpose: </strong>This study investigated the prognostic value and risk reclassification ability of coronary atherosclerosis progression through serial coronary computed tomography angiography (CCTA).</p><p><strong>Materials and methods: </strong>This study enrolled patients with suspected or confirmed coronary artery disease who underwent serial CCTA. Coronary atherosclerosis progression was represented by coronary artery calcium score (CACS) and segment stenosis score (SSS) progression. The baseline and follow-up CCTA characteristics and coronary atherosclerosis progression were compared. Furthermore, the incremental prognostic value and reclassification ability of three models (model 1, baseline risk factors; model 2, model 1 + SSS; and model 3, model 2 + SSS progression) for major adverse cardiovascular events (MACEs) were compared.</p><p><strong>Results: </strong>In total, 516 patients (aged 56.40 ± 9.56 y, 67.4% men) were enrolled. During a mean follow-up of 65.29 months, 114 MACE occurred. The MACE group exhibited higher CACS and SSS than the non-MACE group at baseline and follow-up CCTA ( P < 0.001), and demonstrated higher coronary atherosclerosis progression than the non-MACE group (ΔSSS: 2.63 ± 2.50 vs 1.06 ± 1.78, P < 0.001; ΔCACS: 115.15 ± 186.66 vs 89.91 ± 173.08, P = 0.019). SSS progression provided additional prognostic information (C-index = 0.757 vs 0.715, P < 0.001; integrated discrimination index = 0.066, P < 0.001) and improved the reclassification ability of risk (categorical-net reclassification index = 0.149, P = 0.015) compared with model 2.</p><p><strong>Conclusions: </strong>Coronary atherosclerosis progression through CCTA significantly increased the prognostic value and risk stratification for MACE compared with baseline risk factor evaluation and CCTA only.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"385-391"},"PeriodicalIF":2.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617538","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 Value of Magnetic Resonance Imaging in Assessing Immediate Efficacy After Microwave Ablation of Lung Malignancies.","authors":"Fandong Zhu, Chen Yang, Jianyun Wang, Tong Zhou, Qianling Li, Subo Wang, Zhenhua Zhao","doi":"10.1097/RTI.0000000000000797","DOIUrl":"10.1097/RTI.0000000000000797","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the imaging performance and parametric analysis of magnetic resonance imaging (MRI) immediately after microwave ablation (MWA) of lung malignancies.</p><p><strong>Materials and methods: </strong>We retrospectively analyzed the MRI performance immediately after MWA of 34 cases of lung malignancies. The ablation zone parameters of lung malignancies were measured, including the long diameter (L), short diameter (S), and safety margin of the ablation zone on plain computed tomography (CT), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI) after MWA. The study calculated the tumor volume (V 0 ), the ablation zone volume (V 1 ), and the ratio of V 0 to V 1 (V%). Statistical differences between the parameters were analyzed.</p><p><strong>Results: </strong>The ablation area of the lesion exhibited central low signal and peripheral high signal on T2WI, central high signal and peripheral equal or high signal on T1WI, and circumferential enhancement in the periphery. The safety margin measured on T2WI was greater than that measured on plain CT and T1WI. On plain CT, the L, S, and V 1 were smaller in the effective treatment group than in the ineffective treatment group ( P <0.05). On T1WI, the V% and safety margin were greater in the effective treatment group than in the ineffective treatment group ( P =0.009 and P =0.016, respectively).</p><p><strong>Conclusions: </strong>MRI may be a new, valuable method to assess immediate efficacy after MWA for lung malignancies using the ablation zone parameters V% on T1WI and safety margin on T2WI.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"392-398"},"PeriodicalIF":2.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11495527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141635553","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":"A Case of Colloid Adenocarcinoma of the Lung With Coarse Calcification.","authors":"Hikaru Watanabe, Katsunori Oikado, Yoshinao Sato, Ryota Ichikawa, Hironori Ninomiya, Mingyon Mun, Masayuki Nakao, Yosuke Matsuura, Junji Ichinose, Takashi Terauchi","doi":"10.1097/RTI.0000000000000814","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000814","url":null,"abstract":"","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":"39 6","pages":"W108-W110"},"PeriodicalIF":2.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512037","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 Case of Nonsmoker Pulmonary Langerhans Cell Histiocytosis With Multiple Pulmonary Nodules Disappeared and Appeared.","authors":"Midori Ueno, Haruka Oku, Yo Todoroki, Yu Murakami, Yoshiko Hayashida, Kei Yamasaki, Kazuhiro Yatera, Eisuke Katafuchi, Shohei Shimajiri, Takatoshi Aoki","doi":"10.1097/RTI.0000000000000810","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000810","url":null,"abstract":"<p><p>We present a non-smoker woman in her 40s with PLCH who presented with atypical imaging findings of multiple pulmonary noncavitary nodules without air cysts with repeated waxing and waning.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":"39 6","pages":"W104-W107"},"PeriodicalIF":2.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512038","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":"Impact of Photon-counting Detector Computed Tomography on a Quantitative Interstitial Lung Disease Machine Learning Model.","authors":"Chi Wan Koo, Sean J Huls, Francis Baffour, Cynthia H McCollough, Lifeng Yu, Brian J Bartholmai, Zhongxing Zhou","doi":"10.1097/RTI.0000000000000807","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000807","url":null,"abstract":"<p><strong>Purpose: </strong>Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model.</p><p><strong>Materials and methods: </strong>A QML model analyzed 52 CT exams from patients who underwent same-day conventional and PCD-CT for suspected ILD. Lin's concordance correlation coefficient (CCC) assessed agreement between conventional and PCD-CT QML results. A CCC >0.90 was regarded as excellent, 0.9 to 0.8 as good, and <0.80 as a poor concordance. Spearman rank correlation evaluated the association between pulmonary function test results (PFT) and QML features (reticulation [R], honeycombing [HC], ground glass [GG], interstitial lung disease [ILD], and vessel-related structures [VRS]). Correlations were statistically significant if the 95% CI did not include 0.00 and P value <0.05.</p><p><strong>Results: </strong>Conventional and PCD-CT QML results had good to excellent concordance (CCC ≥0.8) except for total HC (CCC <0.8), likely related to better PCD-CT honeycombing delineation. Overall, compared with conventional CT, PCD-CT had consistently more statistically significant correlation with PFT for HC (9 PCD vs. 2 conventional of 28 total and regional associations), similar correlation for R (20 PCD vs. 18 conventional of 28 associations) and VRS (19 PCD vs. 23 conventional of 28 associations), and less correlation for GG extent (12 PCD vs. 20 conventional associations).</p><p><strong>Conclusions: </strong>There is strong agreement between conventional and PCD-CT QML ILD features except for HC. PCD-CT improved HC but decreased GG extent correlation with PFT. Therefore, even though most quantitative features were not impacted by the newer PCD-CT technology, model adjustment is necessary.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512036","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":"Drug-induced Acute Lung Injury: A Comprehensive Radiologic Review.","authors":"Fatemeh Saber Hamishegi, Ria Singh, Dhiraj Baruah, Jordan Chamberlin, Mohamed Hamouda, Selcuk Akkaya, Ismail Kabakus","doi":"10.1097/RTI.0000000000000816","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000816","url":null,"abstract":"<p><p>Drug-induced acute lung injury is a significant yet often underrecognized clinical challenge, associated with a wide range of therapeutic agents, including chemotherapy drugs, antibiotics, anti-inflammatory drugs, and immunotherapies. This comprehensive review examines the pathophysiology, clinical manifestations, and radiologic findings of drug-induced acute lung injury across different drug categories. Common imaging findings are highlighted to aid radiologists and clinicians in early recognition and diagnosis. The review emphasizes the importance of immediate cessation of the offending drug and supportive care, which may include corticosteroids. Understanding these patterns is crucial for prompt diagnosis and management, potentially improving patient outcomes.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331321","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":"Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.","authors":"Xiao-Yan Wang, Shao-Hong Wu, Jiao Ren, Yan Zeng, Li-Li Guo","doi":"10.1097/RTI.0000000000000817","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000817","url":null,"abstract":"<p><strong>Purpose: </strong>This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor (EGFR) and TP53 mutations and to assess the models' capacities to identify patients who are suitable for TKI-targeted therapy and those with poor prognoses.</p><p><strong>Materials and methods: </strong>A total of 267 patients with lung adenocarcinomas who underwent genetic testing and noncontrast chest computed tomography from our hospital were retrospectively included. Clinical information and imaging characteristics were gathered, and high-throughput feature acquisition on all defined regions of interest (ROIs) was carried out. We selected features and constructed clinical models, radiomics models, deep learning models, and ensemble models to predict EGFR status with all patients and TP53 status with EGFR-positive patients, respectively. The validity and reliability of each model were expressed as the area under the curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score.</p><p><strong>Results: </strong>We constructed 7 kinds of models for 2 different dichotomies, namely, the clinical model, the radiomics model, the DL model, the rad-clin model, the DL-clin model, the DL-rad model, and the DL-rad-clin model. For EGFR- and EGFR+, the DL-rad-clin model got the highest AUC value of 0.783 (95% CI: 0.677-0.889), followed by the rad-clin model, the DL-clin model, and the DL-rad model. In the group with an EGFR mutation, for TP53- and TP53+, the rad-clin model got the highest AUC value of 0.811 (95% CI: 0.651-0.972), followed by the DL-rad-clin model and the DL-rad model.</p><p><strong>Conclusion: </strong>Our progressive binary classification models based on radiomics and deep learning may provide a good reference and complement for the clinical identification of TKI responders and those with poor prognoses.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331322","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 Bronchiectasis Progression in Low-dose Screening for Lung Cancer: Frequency and Predictors.","authors":"Qiang Cai, Natthaya Triphuridet, Yeqing Zhu, Rowena Yip, David F Yankelevitz, Mark Metersky, Claudia I Henschke","doi":"10.1097/RTI.0000000000000812","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000812","url":null,"abstract":"<p><strong>Purpose: </strong>Bronchiectasis is associated with loss of lung function, substantial use of health care resources, and increased morbidity and mortality in people with cardiopulmonary diseases. We assessed the frequency of progression or new development of bronchiectasis and predictors of progression in participants in low-dose computed tomography (CT) screening programs.</p><p><strong>Materials and methods: </strong>We reviewed our prospectively enrolled screening cohort in the Early Lung and Cardiac Action Program cohort of smokers, aged 40 to 90, between 2010 and 2019, and medical records to assess the progression of bronchiectasis after five or more years of follow-up after baseline low-dose CT. Logistic and multivariate-analysis-of-covariance regression analyses were used to examine factors associated with bronchiectasis progression.</p><p><strong>Results: </strong>Among 2182 baseline screening participants, we identified 534 (mean age: 65±9 y; 53.6% women) with follow-up screening of 5+ years (median follow-up: 103.2 mo). Of the 534 participants, 34 (6.4%) participants had progressed (25/126, 19.8%) or newly developed (9/408, 2.2%) bronchiectasis. Significant predictors of progression (progressed+newly developed) were: age (P=0.03), pack-years of smoking (P=0.004), baseline components of the ELCAP Bronchiectasis Score, including the severity of bronchial dilatation (P=0.01), its extent (P=0.01), bronchial wall thickening (P=0.04), and mucoid impaction (P<0.001).</p><p><strong>Conclusions: </strong>Assuming similar progression rates, ~136 out of 2182 participants are expected to progress on follow-up screening. This study sheds light on bronchiectasis progression and its significant predictors in a low-dose CT screening program. We recommend reporting bronchiectasis as participants who have smoked are at increased risk, and continued assessment over the entire period of participation in the low-dose CT screening program would allow for the identification of possible causes, early warning, and even early treatment.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299683","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 Diagnostic Performance of Large Language Models and General Radiologists in Thoracic Radiology Cases: A Comparative Study.","authors":"Yasin Celal Gunes, Turay Cesur","doi":"10.1097/RTI.0000000000000805","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000805","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate and compare the diagnostic performance of 10 different large language models (LLMs) and 2 board-certified general radiologists in thoracic radiology cases published by The Society of Thoracic Radiology.</p><p><strong>Materials and methods: </strong>We collected publicly available 124 \"Case of the Month\" from the Society of Thoracic Radiology website between March 2012 and December 2023. Medical history and imaging findings were input into LLMs for diagnosis and differential diagnosis, while radiologists independently visually provided their assessments. Cases were categorized anatomically (parenchyma, airways, mediastinum-pleura-chest wall, and vascular) and further classified as specific or nonspecific for radiologic diagnosis. Diagnostic accuracy and differential diagnosis scores (DDxScore) were analyzed using the χ2, Kruskal-Wallis, Wilcoxon, McNemar, and Mann-Whitney U tests.</p><p><strong>Results: </strong>Among the 124 cases, Claude 3 Opus showed the highest diagnostic accuracy (70.29%), followed by ChatGPT 4/Google Gemini 1.5 Pro (59.75%), Meta Llama 3 70b (57.3%), ChatGPT 3.5 (53.2%), outperforming radiologists (52.4% and 41.1%) and other LLMs (P<0.05). Claude 3 Opus DDxScore was significantly better than other LLMs and radiologists, except ChatGPT 3.5 (P<0.05). All LLMs and radiologists showed greater accuracy in specific cases (P<0.05), with no DDxScore difference for Perplexity and Google Bard based on specificity (P>0.05). There were no significant differences between LLMs and radiologists in the diagnostic accuracy of anatomic subgroups (P>0.05), except for Meta Llama 3 70b in the vascular cases (P=0.040).</p><p><strong>Conclusions: </strong>Claude 3 Opus outperformed other LLMs and radiologists in text-based thoracic radiology cases. LLMs hold great promise for clinical decision systems under proper medical supervision.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299689","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":"Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.","authors":"Wesley Bocquet, Roger Bouzerar, Géraldine François, Antoine Leleu, Cédric Renard","doi":"10.1097/RTI.0000000000000806","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000806","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).</p><p><strong>Material and methods: </strong>This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location.</p><p><strong>Results: </strong>The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6 mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively).</p><p><strong>Conclusions: </strong>At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299685","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}