Journal of Thoracic Imaging最新文献

{"title":"Computed Tomography-guided Percutaneous Lung Biopsy With Electromagnetic Navigation Compared With Conventional Approaches: An Open-label, Randomized Controlled Trial.","authors":"Qin Liu, Xiaoxia Guo, Ziyin Wang, Hao Xu, Wei Huang, Jingjing Liu, Zhongmin Wang, Fuhua Yan, Zhiyuan Wu, Xiaoyi Ding","doi":"10.1097/RTI.0000000000000763","DOIUrl":"10.1097/RTI.0000000000000763","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to assess the efficiency and safety of computed tomography (CT)-guided percutaneous biopsy of lung lesions with electromagnetic (EM) navigation and compare them with those of conventional approaches.</p><p><strong>Materials and methods: </strong>Seventy-nine patients with lung or liver lesions who needed biopsies were enrolled in this trial. All patients were randomly assigned to the E group underwent CT-guided percutaneous biopsies with the EM navigation system or to the C group treated with conventional approaches.</p><p><strong>Results: </strong>In total, 27 patients with lung lesions were assigned to the E group, and 20 patients were assigned to the C group. The diagnostic success rate was 92.6% and 95% in both groups, respectively ( P >0.9999). The median number of needle repositions in the E group was less than that in the C group (2.0 vs. 2.5, P =0.03). The positioning success rate with 1 or 2 needle repositions for the E group was significantly higher than the C group (81.5% vs. 50%, P =0.03). The median accuracy of the puncture location in the E group was better than that in the C group (2.0 vs. 6.6 mm, P <0.0001). The total procedure time lengthened in the E group compared with the C group (30.5±1.6 vs. 18.3±1.7 min, P <0.0001), but the number of CT acquisitions was not significantly different ( P =0.08). There was no significant difference in complication incidence between the 2 groups ( P =0.44).</p><p><strong>Conclusion: </strong>The EM navigation system is an effective and safe auxiliary tool for CT-guided percutaneous lung biopsy, but lengthen the procedure time.</p><p><strong>Trial registration: </strong>ChiCTR2100043361, registered February 9, 2021-retrospectively registered ( http://www.medresman.org.cn/uc/project/projectedit.aspx?proj=7591 ).</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"247-254"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048326","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 Performance of Cardiovascular Magnetic Resonance Phase Contrast Analysis to Identify Heart Failure With Preserved Ejection Fraction.","authors":"Lu Lin, Chi Ting Kwan, Pui Min Yap, Sau Yung Fung, Hok Shing Tang, Wan Wai Vivian Tse, Cheuk Nam Felix Kwan, Yin Hay Phoebe Chow, Nga Ching Yiu, Yung Pok Lee, Ambrose Ho Tung Fong, Qing-Wen Ren, Mei-Zhen Wu, Ka Chun Kevin Lee, Chun Yu Leung, Andrew Li, David Montero, Varut Vardhanabhuti, JoJo Hai, Chung-Wah Siu, HungFat Tse, Dudley John Pennell, Raad Mohiaddin, Roxy Senior, Kai-Hang Yiu, Ming-Yen Ng","doi":"10.1097/RTI.0000000000000777","DOIUrl":"10.1097/RTI.0000000000000777","url":null,"abstract":"","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"265-267"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140095022","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":"Regional Analysis of Myocardial Strain to Wall Thickness Ratio in Cardiac Amyloidosis and Hypertrophic Cardiomyopathy.","authors":"Michael P Gannon, Cristina P Sison, Shahryar G Saba","doi":"10.1097/RTI.0000000000000772","DOIUrl":"10.1097/RTI.0000000000000772","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Increased left ventricular wall thickness is a hallmark of cardiac amyloidosis (CA). Several other disease states, including hypertrophic cardiomyopathy (HCM), share this common feature. Myocardial strain has emerged as a diagnostic and prognostic tool to differentiate causes of increased left ventricular wall thickness. We sought to determine if regional strain differences were present in CA when compared with HCM when indexed to wall thickness as well as adjusting for important factors such as ejection fraction (EF), age, sex, and hypertension.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We performed a multicenter, retrospective analysis of 122 patients in 3 groups: CA (n=40), HCM (n=44), and controls (n=38). Using commercially available software, we determined peak systolic strain measurements in the base, mid, and apical segments in all 3 cardinal directions of radial strain, circumferential strain, and longitudinal strain. The regional strain was indexed to wall thickness to create a strain to wall thickness (STT) ratio. Analysis of Variance was performed to examine the association of each strain parameter with the disease group, adjusting for age, sex, hypertension, and EF. Multinomial logistic regression was performed to determine which combination of variables can potentially be used to best model the disease group.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Ratios of STT at all 3 levels were significantly different with respect to the cardinal directions of radial, circumferential, and longitudinal strain in a multivariable analysis adjusting for age, sex, and hypertension. Specifically, with respect to the basal segments, the STT ratio across CA, HCM, and normal were significantly different in radial (1.13±0.34 vs. 3.79±0.22 vs. 4.12±0.38; P &lt;0.0001), circumferential (-0.79±0.10 vs. -1.62±0.07 vs. -2.25±0.11; P &lt;0.0001), and longitudinal directions (-0.41±0.09 vs. -1.03±0.06 vs. -1.41±0.10; P &lt;0.0001). When adjusting for age, sex, hypertension and EF, only the base was significantly different between the CA and HCM groups in the radial (1.49±0.37 vs. 3.53±0.24; P &lt;0.0001), circumferential -1.04±0.10 vs. -1.44±0.06; P &lt;0.005), and longitudinal (-0.55±0.10 vs -0.94±0.06; P =0.007) directions. Using multinomial logistic regression, the use of age, left ventricular EF, global longitudinal strain, and basal radial strain yielded a diagnostic model with an area under the receiver operating characteristic curve (AUC) of 0.98. A model excluding age, despite being likely an independent predictor in our cohort, yielded an overall AUC of 0.90. When excluding age, the overall AUC was 0.91 and specifically when discriminating CA from HCM was 0.95.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Regional myocardial strain indexed to wall thickness with an STT ratio can differentiate between etiologies of increased left ventricular wall thickness. Differences in myocardial deformation may be independent of wall thickness. Differences in basal strain when","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"255-264"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139404934","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":"Lung-Reporting and Data System 2.0: Impact of the Updated Approach to Juxtapleural Nodules During Lung Cancer Screening Using the National Lung Cancer Screening Trial Data Set.","authors":"Lydia Chelala, Rydhwana Hossain, Jean Jeudy, Ziad Nader, Julia Kastner, Charles White","doi":"10.1097/RTI.0000000000000756","DOIUrl":"10.1097/RTI.0000000000000756","url":null,"abstract":"<p><strong>Purpose: </strong>To determine the frequency of malignancy of nonperifissural juxtapleural nodules (JPNs) measuring 6 to < 10 mm in a subset of low-dose chest computed tomographies from the National Lung Cancer Screening Trial and the rate of down-classification of such nodules in Lung-Reporting and Data System (RADS) 2.0 compared with Lung-RADS 1.1.</p><p><strong>Materials and methods: </strong>A secondary analysis of a subset of the National Lung Screening Trial was performed. An exemption was granted by the Institutional Review Board. The dominant noncalcified nodule measuring 6 to <10 mm was identified on all available prevalence computed tomographies. Nodules were categorized as pleural or nonpleural. Benign or malignant morphology was recorded. Initial and updated categories based on Lung-RADS 1.1 and Lung-RADS 2.0 were assigned, respectively. The impact of the down-classification of JPN was assessed. Both classification schemes were compared using the McNemar test ( P < 0.01).</p><p><strong>Results: </strong>A total of 2813 patients (62 ± 5 y, 1717 men) with 4408 noncalcified nodules were studied. One thousand seventy-three dominant nodules measuring 6 to <10 mm were identified. Three hundred forty-eight (32.4%) were JPN. The updated scheme allowed down-classification of 310 JPN from categories 3 (n = 198) and 4A (n = 112) to category 2. We, therefore, estimate a 4.8% rate of down-classification to category 2 in the entire National Lung Screening Trial screening group. Two/348 (0.57%) JPN were malignant, both nonbenign in morphology. The false-positive rate decreased in the updated classification ( P < 0.01).</p><p><strong>Conclusion: </strong>This study demonstrates the low malignant potential of benign morphology JPN measuring 6 mm to <10 mm. The Lung-RADS 2.0 approach to JPN is estimated to reduce short-term follow-ups and false-positive results.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"241-246"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54231950","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":"Pre-PCI CT-FFR Predicts Target Vessel Failure After Stent Implantation.","authors":"Zewen Wang, Chunxiang Tang, Rui Zuo, Aiming Zhou, Wei Xu, Jian Zhong, Zhihan Xu, Longjiang Zhang","doi":"10.1097/RTI.0000000000000791","DOIUrl":"10.1097/RTI.0000000000000791","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) before percutaneous coronary intervention (PCI) to predict target vessel failure (TVF) after stent implantation.</p><p><strong>Methods: </strong>This retrospective study included 429 patients (429 vessels) who underwent PCI and stent implantation after CCTA within 3 months. All patients underwent coronary stent implantation between January 2012 and December 2019. A dedicated workstation (Syngo Via, Siemens) was used to analyze and measure the CT-FFR value. The cut-off values of pre-PCI CT-FFR for predicting TVF were defined as 0.80 and the value using the log-rank maximization method, respectively. The primary outcome was TVF, defined as a composite of cardiac death, target vessel myocardial infarction, and clinically driven target vessel revascularization (TVR), which was a secondary outcome.</p><p><strong>Results: </strong>During a median 64.0 months follow-up, the cumulative incidence of TVF was 7.9% (34/429). The cutoff value of pre-PCI CT-FFR based on the log-rank maximization method was 0.74, which was the independent predictor for TVF [hazard ratio (HR): 2.61 (95% CI: 1.13, 6.02); P =0.024] and TVR [HR: 3.63 (95%CI: 1.25, 10.51); P =0.018]. Compared with the clinical risk factor model, pre-PCI CT-FFR significantly improved the reclassification ability for TVF [net reclassification improvement (NRI), 0.424, P <0.001; integrative discrimination index (IDI), 0.011, P =0.022]. Adding stent information to the prediction model resulted in an improvement in reclassification for the TVF (C statistics: 0.711, P =0.001; NRI: 0.494, P <0.001; IDI: 0.020, P =0.028).</p><p><strong>Conclusions: </strong>Pre-PCI CT-FFR ≤0.74 was an independent predictor for TVF or TVR, and integration of clinical, pre-PCI CT-FFR, and stent information models can provide a better risk stratification model in patients with stent implantation.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"232-240"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155652","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":"Radiomics Analysis of Pericoronary Adipose Tissue From Baseline Coronary Computed Tomography Angiography Enables Prediction of Coronary Plaque Progression.","authors":"Rui Chen, Xiaohu Li, Han Jia, Changjing Feng, Siting Dong, Wangyan Liu, Shushen Lin, Xiaomei Zhu, Yi Xu, Yinsu Zhu","doi":"10.1097/rti.0000000000000790","DOIUrl":"https://doi.org/10.1097/rti.0000000000000790","url":null,"abstract":"The relationship between plaque progression and pericoronary adipose tissue (PCAT) radiomics has not been comprehensively evaluated. We aim to predict plaque progression with PCAT radiomics features and evaluate their incremental value over quantitative plaque characteristics.","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":"27 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833230","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":"Deep Learning for Detection of Pneumothorax and Pleural Effusion on Chest Radiographs: Validation Against Computed Tomography, Impact on Resident Reading Time, and Interreader Concordance.","authors":"Ali Tejani, Thomas Dowling, Sreeja Sanampudi, Rana Yazdani, Arzu Canan, Elona Malja, Yin Xi, Suhny Abbara, Ron M Peshock, Fernando U Kay","doi":"10.1097/RTI.0000000000000746","DOIUrl":"10.1097/RTI.0000000000000746","url":null,"abstract":"<p><strong>Purpose: </strong>To study the performance of artificial intelligence (AI) for detecting pleural pathology on chest radiographs (CXRs) using computed tomography as ground truth.</p><p><strong>Patients and methods: </strong>Retrospective study of subjects undergoing CXR in various clinical settings. Computed tomography obtained within 24 hours of the CXR was used to volumetrically quantify pleural effusions (PEfs) and pneumothoraxes (Ptxs). CXR was evaluated by AI software (INSIGHT CXR; Lunit) and by 3 second-year radiology residents, followed by AI-assisted reassessment after a 3-month washout period. We used the area under the receiver operating characteristics curve (AUROC) to assess AI versus residents' performance and mixed-model analyses to investigate differences in reading time and interreader concordance.</p><p><strong>Results: </strong>There were 96 control subjects, 165 with PEf, and 101 with Ptx. AI-AUROC was noninferior to aggregate resident-AUROC for PEf (0.82 vs 0.86, P < 0.001) and Ptx (0.80 vs 0.84, P = 0.001) detection. AI-assisted resident-AUROC was higher but not significantly different from the baseline. AI-assisted reading time was reduced by 49% (157 vs 80 s per case, P = 0.009), and Fleiss kappa for Ptx detection increased from 0.70 to 0.78 ( P = 0.003). AI decreased detection error for PEf (odds ratio = 0.74, P = 0.024) and Ptx (odds ratio = 0.39, P < 0.001).</p><p><strong>Conclusion: </strong>Current AI technology for the detection of PEf and Ptx on CXR was noninferior to second-year resident performance and could help decrease reading time and detection error.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"185-193"},"PeriodicalIF":3.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54231945","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-based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients.","authors":"Kevin B W Groot Lipman, Thierry N Boellaard, Cornedine J de Gooijer, Nino Bogveradze, Eun Kyoung Hong, Federica Landolfi, Francesca Castagnoli, Nargiza Vakhidova, Illaa Smesseim, Ferdi van der Heijden, Regina G H Beets-Tan, Rianne Wittenberg, Zuhir Bodalal, Jacobus A Burgers, Stefano Trebeschi","doi":"10.1097/RTI.0000000000000759","DOIUrl":"10.1097/RTI.0000000000000759","url":null,"abstract":"<p><strong>Purpose: </strong>Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests.</p><p><strong>Materials and methods: </strong>Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient ( r ) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO).</p><p><strong>Results: </strong>We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC ( P = 0.001) and FVC ( P = 0.04) values for the higher PPV patients, but not for DLCO ( P = 0.19).</p><p><strong>Conclusion: </strong>We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"165-172"},"PeriodicalIF":3.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11027965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71415045","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":"Bodyweight-adjusted Contrast Media With Shortened Injection Duration for Step-and-Shoot Coronary Computed Tomography Angiography to Acquire Improved Image Quality.","authors":"Liang Jin, Kun Wang, Xiaodong Wang, Cheng Li, Yingli Sun, Pan Gao, Yi Xiao, Ming Li","doi":"10.1097/RTI.0000000000000696","DOIUrl":"10.1097/RTI.0000000000000696","url":null,"abstract":"<p><strong>Purpose: </strong>Shortened injection durations are not recommended in step-and-shoot coronary computed tomography angiography (CCTA). We aimed to evaluate the image quality of CCTA performed using bodyweight-adjusted iodinated contrast media (ICM) with different injection durations to generate an optimized ICM administration protocol to acquire convincible image quality in step-and-shoot CCTA.</p><p><strong>Materials and methods: </strong>A total of 200 consecutive patients with suspected coronary artery disease (CAD) were enrolled in group A (N=50, 350 mgI/mL, bodyweight×0.8 mL/kg with a 13-s injection duration), group B (N=50, 350 mgI/mL, bodyweight×0.9 mL/kg with a 13-s injection duration), group C (N=50, 350 mgI/mL, bodyweight×0.8 mL/kg with a 12-s injection duration), and group D (N=50, 320 mgI/mL, bodyweight×0.8 mL/kg with a 13-s injection duration). Patient characteristics, ICM administration protocols, quantitative computed tomography (CT) value measurements, and qualitative image scores were analyzed and compared among the groups.</p><p><strong>Results: </strong>Groups A and D achieved the lowest ICM volume, saline volume, injection flow rate, and total iodine and iodine injection rates among the groups. All the CT values of the coronary arteries in all groups were >300 HU. All the observers' average scores exceeded three points. In group A, the CT values showed significant positive correlation with the iodine injection rate ( r =0.226, P <0.001), whereas the signal-to-noise ratio ( r =-0.004, P =0.927) and contrast-to-noise ratio ( r =-0.006, P =0.893) values were not.</p><p><strong>Conclusions: </strong>Bodyweight×0.8 mL/kg with a 13-second injection duration is a comprehensive option for step-and-shoot CCTA with improved image quality, and a 350 mgI/mL iodine concentration is preferred.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"146-156"},"PeriodicalIF":3.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11027974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10650630","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":"Noncontrast Myocardial Characterization in Acute Myocardial Infarction Using Electron Density Imaging.","authors":"Gaston A Rodriguez-Granillo, Juan Cirio, Jose F Vila, Eran Langzam, Thomas Ivanc, Lucia Fontana, Amalia Descalzo, Bibiana Rubilar, Pedro Lylyk","doi":"10.1097/RTI.0000000000000749","DOIUrl":"10.1097/RTI.0000000000000749","url":null,"abstract":"<p><strong>Purpose: </strong>Spectral computed tomography (CT) enables improved tissue characterization, although virtually all research has focused on contrast-enhanced examinations. We hypothesized that changes in myocardial tissue related to acute myocardial infarction (AMI) might potentially be identified without the need for contrast administration using electron density (ED) imaging.</p><p><strong>Patients and methods: </strong>This retrospective observational study involved a small series (n = 15) of patients admitted to our institution with a first AMI without signs of hemodynamic instability and identification of a culprit vessel with invasive coronary angiography during the same admission, who also underwent a noncontrast, low-dose chest CT using a dual-layer spectral CT scanner. Images were assessed in search of dark areas with low density on ED imaging, and the mean percentage ED relative to water (%EDW) was calculated.</p><p><strong>Results: </strong>Using a qualitative approach, ED assessment enabled the identification of 11/15 (73%) affected coronary territories, with a sensitivity of 73% (95% CI: 45; 92%) and a specificity of 87% (95% CI: 69; 96%). AMI segments showed significantly lower ED values than the remote myocardium (103.8 ± 0.8 vs 104.3 ± 0.6 %EDW, P < 0.0001), and a threshold below 103.9 %EDW had a sensitivity of 66% and specificity of 79% for the identification of AMI. In a control group of patients without a history of cardiovascular disease, none had areas with focal reduction of ED following the shape of the myocardial wall.</p><p><strong>Conclusions: </strong>In our preliminary series, ED imaging showed the potential to enable the identification of myocardial tissue changes related to AMI without iodinated contrast requirement.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"173-177"},"PeriodicalIF":3.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54231952","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}