Journal of Thoracic Imaging最新文献

{"title":"Automatic Quantification of Abnormal Lung Parenchymal Attenuation on Chest Computed Tomography Images Using Densitometry and Texture-based Analysis.","authors":"Alysson R S Carvalho, Alan Guimarães, Rodrigo Basilio, Marco A Conrado da Silva, Sandro Colli, Carolina Galhós de Aguiar, Rafael C Pereira, Liseane G Lisboa, Bruno Hochhegger, Rosana S Rodrigues","doi":"10.1097/RTI.0000000000000804","DOIUrl":"10.1097/RTI.0000000000000804","url":null,"abstract":"<p><strong>Purpose: </strong>To compare texture-based analysis using convolutional neural networks (CNNs) against lung densitometry in detecting chest computed tomography (CT) image abnormalities.</p><p><strong>Material and methods: </strong>A U-NET was used for lung segmentation, and an ensemble of 7 CNN architectures was trained for the classification of low-attenuation areas (LAAs; emphysema, cysts), normal-attenuation areas (NAAs; normal parenchyma), and high-attenuation areas (HAAs; ground-glass opacities, crazy paving/linear opacity, consolidation). Lung densitometry also computes (LAAs, ≤-950 HU), NAAs (-949 to -700 HU), and HAAs (-699 to -250 HU). CNN-based and densitometry-based severity indices (CNN and Dens, respectively) were calculated as (LAA+HAA)/(LAA+NAA+HAA) in 812 CT scans from 176 normal subjects, 343 patients with emphysema, and 293 patients with interstitial lung disease (ILD). The correlation between CNN-derived and densitometry-derived indices was analyzed, alongside a comparison of severity indices among patient subgroups with emphysema and ILD, using the Spearman correlation and ANOVA with Bonferroni correction.</p><p><strong>Results: </strong>CNN-derived and densitometry-derived severity indices (SIs) showed a strong correlation (ρ=0.90) and increased with disease severity. CNN-SIs differed from densitometry SIs, being lower for emphysema and higher for moderate to severe ILD cases. CNN estimations for normal attenuation areas were higher than those from densitometry across all groups, indicating a potential for more accurate characterization of lung abnormalities.</p><p><strong>Conclusions: </strong>CNN outputs align closely with densitometry in assessing lung abnormalities on CT scans, offering improved estimates of normal areas and better distinguishing similar abnormalities. However, this requires higher computing power.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299684","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":"Automated 3D-Body Composition Analysis as a Predictor of Survival in Patients With Idiopathic Pulmonary Fibrosis.","authors":"Luca Salhöfer, Francesco Bonella, Mathias Meetschen, Lale Umutlu, Michael Forsting, Benedikt Michael Schaarschmidt, Marcel Klaus Opitz, Jens Kleesiek, Rene Hosch, Sven Koitka, Vicky Parmar, Felix Nensa, Johannes Haubold","doi":"10.1097/RTI.0000000000000803","DOIUrl":"10.1097/RTI.0000000000000803","url":null,"abstract":"<p><strong>Purpose: </strong>Idiopathic pulmonary fibrosis (IPF) is the most common interstitial lung disease, with a median survival time of 2 to 5 years. The focus of this study is to establish a novel imaging biomarker.</p><p><strong>Materials and methods: </strong>In this study, 79 patients (19% female) with a median age of 70 years were studied retrospectively. Fully automated body composition analysis (BCA) features (bone, muscle, total adipose tissue, intermuscular, and intramuscular adipose tissue) were combined into Sarcopenia, Fat, and Myosteatosis indices and compared between patients with a survival of more or less than 2 years. In addition, we divided the cohort at the median (high=≥ median, low=<median) of the respective BCA index and tested the impact on the overall survival using the Kaplan-Meier methodology, a log-rank test, and adjusted multivariate Cox-regression analysis.</p><p><strong>Results: </strong>A high Sarcopenia and Fat index and low Myosteatosis index were associated with longer median survival (35 vs. 16 mo for high vs. low Sarcopenia index, P =0.066; 44 vs. 14 mo for high vs. low Fat index, P <0.001; and 33 vs. 14 mo for low vs. high Myosteatosis index, P =0.0056) and better 5-year survival rates (34.0% vs. 23.6% for high vs. low Sarcopenia index; 47.3% vs. 9.2% for high vs. low Fat index; and 11.2% vs. 42.7% for high vs. low Myosteatosis index). Adjusted multivariate Cox regression showed a significant impact of the Fat (HR=0.71, P =0.01) and Myosteatosis (HR=1.12, P =0.005) on overall survival.</p><p><strong>Conclusion: </strong>The fully automated BCA provides biomarkers with a predictive value for the overall survival in patients with IPF.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11837968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057111","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":"Diagnostic Accuracy of Ultrasound Guidance in Transthoracic Needle Biopsy: A Systematic Review and Meta-Analysis.","authors":"Simon Lemieux, Lorence Pinard, Raphaël Marchand, Sonia Kali, Stephan Altmayer, Vicky Mai, Steeve Provencher","doi":"10.1097/RTI.0000000000000811","DOIUrl":"10.1097/RTI.0000000000000811","url":null,"abstract":"<p><strong>Purpose: </strong>To perform a systematic review and meta-analysis of relevant studies to assess the diagnostic accuracy and safety outcomes of ultrasound (US)-guided transthoracic needle biopsy (TTNB) for peripheral lung and pleural lesions.</p><p><strong>Materials and methods: </strong>A search was performed through Medline, Embase, Web of Science, and Cochrane Central from inception up to September 23, 2022 for diagnostic accuracy studies reporting US-guided TTNB (Prospero registration: CRD42021225168). The primary outcome was diagnostic accuracy, which was assessed by sensitivity, specificity, likelihood ratios (LR), and diagnostic odds ratio. Sensitivity and subgroup analyses were performed to evaluate inter-study heterogeneity. The secondary outcome was the frequency of complications. Random-effects models were used for the analyses. The risk of bias and the applicability of the included studies were assessed using the QUADAS-2 tool. Publication bias was assessed by testing the association between the natural logarithm of the diagnostic odds ratio and the effective sample size.</p><p><strong>Results: </strong>Of the 7841 citations identified, 83 independent cohorts (11,767 patients) were included in the analysis. The pooled sensitivity of US-TTNB was 88% (95% CI: 86%-91%, 80 studies). Pooled specificity was 100% (95% CI: 99%-100%, 72 studies), resulting in positive LR, negative LR, and diagnostic odds ratio of 946 (-743 to 2635), 0.12 (0.09 to 0.14), and 8141 (1344 to 49,321), respectively. Complications occurred in 4% (95% CI: 3%-5%) of the procedures, with pneumothorax being the most frequent (3%; 95% CI: 2%-3%, 72 studies) and resulting in chest tube placement in 0.4% (95% CI: 0.2%-0.7%, 64 studies) of the procedures.</p><p><strong>Conclusions: </strong>US-TTNB is an effective and safe procedure for pleural lesions and peripheral lung lesions.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299686","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":"Metastatic Lung Lesion Changes in Follow-up Chest CT: The Advantage of Deep Learning Simultaneous Analysis of Prior and Current Scans With SimU-Net.","authors":"Neta Kenneth Portal, Shalom Rochman, Adi Szeskin, Richard Lederman, Jacob Sosna, Leo Joskowicz","doi":"10.1097/RTI.0000000000000808","DOIUrl":"10.1097/RTI.0000000000000808","url":null,"abstract":"<p><strong>Purpose: </strong>Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.</p><p><strong>Materials and methods: </strong>SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient. It is part of a fully automatic pipeline for the detection, segmentation, matching, and classification of metastatic lung lesions in longitudinal chest CT scans. A data set of 5040 metastatic lung lesions in 344 pairs of 208 prior and current chest CT scans from 79 patients was used for training/validation (173 scans, 65 patients) and testing (35 scans, 14 patients) of a standalone 3D U-Net models and 3 simultaneous SimU-Net models. Outcome measures were the lesion detection and segmentation precision, recall, Dice score, average symmetric surface distance (ASSD), lesion matching, and classification of lesion changes from computed versus manual ground-truth annotations by an expert radiologist.</p><p><strong>Results: </strong>SimU-Net achieved a mean lesion detection recall and precision of 0.93±0.13 and 0.79±0.24 and a mean lesion segmentation Dice and ASSD of 0.84±0.09 and 0.33±0.22 mm. These results outperformed the standalone 3D U-Net model by 9.4% in the recall, 2.4% in Dice, and 15.4% in ASSD, with a minor 3.6% decrease in precision. The SimU-Net pipeline achieved perfect precision and recall (1.0±0.0) for lesion matching and classification of lesion changes.</p><p><strong>Conclusions: </strong>Simultaneous deep learning analysis of metastatic lung lesions in prior and current chest CT scans with SimU-Net yields superior accuracy compared with individual analysis of each scan. Implementation of SimU-Net in the radiological workflow may enhance efficiency by automatically computing key metrics used to evaluate metastatic lung lesions and their temporal changes.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985249","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":"Optimizing Quantum Iterative Reconstruction for Ultra-high-resolution Photon-counting Computed Tomography of the Lung.","authors":"Adrienn Tóth, Jordan H Chamberlin, Gregory Puthoff, Dhiraj Baruah, Jim O'Doherty, Dhruw Maisuria, Aaron M McGuire, U Joseph Schoepf, Reginald F Munden, Ismail M Kabakus","doi":"10.1097/RTI.0000000000000802","DOIUrl":"10.1097/RTI.0000000000000802","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to find the optimal strength level of QIR for ultra-high-resolution (UHR) PCCT of the lung.</p><p><strong>Materials and methods: </strong>This retrospective study included 24 patients who had unenhanced chest CT with the novel UHR scan protocol on the PCCT scanner between March 24, 2023 and May 18, 2023. Two sets of reconstructions were made using different slice thicknesses: standard resolution (SR, 1 mm) and ultra-high-resolution (UHR, 0.2 mm), reconstructed with all strength levels of QIR (0 to 4). Attenuation of the lung parenchyma, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were assessed as objective criteria of image quality. Two fellowship-trained radiologists compared image quality and noise level, sharpness of the images, and the airway details using a 5-point Likert scale. Wilcoxon signed-rank test was used for statistical analysis of reader scores, and one-way repeated measures analysis of variance for comparing the objective image quality scores.</p><p><strong>Results: </strong>Objective image quality linearly improved with higher strength levels of QIR, reducing image noise by 66% from QIR-0 to QIR-4 ( P <0.001). Subjective image noise was best for QIR-4 ( P <0.001). Readers rated QIR-1 and QIR-2 best for SR, and QIR-2 and QIR-3 best for UHR in terms of subjective image sharpness and airway detail, without significant differences between them ( P =0.48 and 0.56, respectively).</p><p><strong>Conclusions: </strong>Higher levels of QIR provided excellent objective image quality, but readers' preference was for intermediate levels. Considering all metrics, we recommend QIR-3 for ultra-high-resolution PCCT of the lung.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134291","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":"Longitudinal Changes of CT-radiomic and Systemic Inflammatory Features Predict Survival in Advanced Non-Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors.","authors":"Maurizio Balbi, Giulia Mazzaschi, Ludovica Leo, Lucas Moron Dalla Tor, Gianluca Milanese, Cristina Marrocchio, Mario Silva, Rebecca Mura, Pasquale Favia, Giovanni Bocchialini, Francesca Trentini, Roberta Minari, Luca Ampollini, Federico Quaini, Giovanni Roti, Marcello Tiseo, Nicola Sverzellati","doi":"10.1097/RTI.0000000000000801","DOIUrl":"10.1097/RTI.0000000000000801","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to determine whether longitudinal changes in CT radiomic features (RFs) and systemic inflammatory indices outperform single-time-point assessment in predicting survival in advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs).</p><p><strong>Materials and methods: </strong>We retrospectively acquired pretreatment (T0) and first disease assessment (T1) RFs and systemic inflammatory indices from a single-center cohort of stage IV NSCLC patients and computed their delta (Δ) variation as [(T1-T0)/T0]. RFs from the primary tumor were selected for building baseline-radiomic (RAD) and Δ-RAD scores using the linear combination of standardized predictors detected by LASSO Cox regression models. Cox models were generated using clinical features alone or combined with baseline and Δ blood parameters and integrated with baseline-RAD and Δ-RAD. All models were 3-fold cross-validated. A prognostic index (PI) of each model was tested to stratify overall survival (OS) through Kaplan-Meier analysis.</p><p><strong>Results: </strong>We included 90 ICI-treated NSCLC patients (median age 70 y [IQR=42 to 85], 63 males). Δ-RAD outperformed baseline-RAD for predicting OS [c-index: 0.632 (95%CI: 0.628 to 0.636) vs. 0.605 (95%CI: 0.601 to 0.608) in the test splits]. Integrating longitudinal changes of systemic inflammatory indices and Δ-RAD with clinical data led to the best model performance [Integrated-Δ model, c-index: 0.750 (95% CI: 0.749 to 0.751) in training and 0.718 (95% CI: 0.715 to 0.721) in testing splits]. PI enabled significant OS stratification within all the models ( P -value <0.01), reaching the greatest discriminative ability in Δ models (high-risk group HR up to 7.37, 95% CI: 3.9 to 13.94, P <0.01).</p><p><strong>Conclusion: </strong>Δ-RAD improved OS prediction compared with single-time-point radiomic in advanced ICI-treated NSCLC. Integrating Δ-RAD with a longitudinal assessment of clinical and laboratory data further improved the prognostic performance.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142074368","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":"Radiomics Analysis for the Identification of Invasive Pulmonary Subsolid Nodules From Longitudinal Presurgical CT Scans.","authors":"Apurva Singh, Leonid Roshkovan, Hannah Horng, Andrew Chen, Sharyn I Katz, Jeffrey C Thompson, Despina Kontos","doi":"10.1097/RTI.0000000000000800","DOIUrl":"10.1097/RTI.0000000000000800","url":null,"abstract":"<p><strong>Purpose: </strong>Effective identification of malignant part-solid lung nodules is crucial to eliminate risks due to therapeutic intervention or lack thereof. We aimed to develop delta radiomics and volumetric signatures, characterize changes in nodule properties over three presurgical time points, and assess the accuracy of nodule invasiveness identification when combined with immediate presurgical time point radiomics signature and clinical biomarkers.</p><p><strong>Materials and methods: </strong>Cohort included 156 part-solid lung nodules with immediate presurgical CT scans and a subset of 122 nodules with scans at 3 presurgical time points. Region of interest segmentation was performed using ITK-SNAP, and feature extraction using CaPTk. Image parameter heterogeneity was mitigated at each time point using nested ComBat harmonization. For 122 nodules, delta radiomics features (ΔR AB = (R B -R A )/R A ) and delta volumes (ΔV AB = (V B -V A )/V A ) were computed between the time points. Principal Component Analysis was performed to construct immediate presurgical radiomics (Rs 1 ) and delta radiomics signatures (ΔRs 31 + ΔRs 21 + ΔRs 32 ). Identification of nodule pathology was performed using logistic regression on delta radiomics and immediate presurgical time point signatures, delta volumes (ΔV 31 + ΔV 21 + ΔV 32 ), and clinical variable (smoking status, BMI) models (train test split (2:1)).</p><p><strong>Results: </strong>In delta radiomics analysis (n= 122 nodules), the best-performing model combined immediate pre-surgical time point and delta radiomics signatures, delta volumes, and clinical factors (classification accuracy [AUC]): (77.5% [0.73]) (train); (71.6% [0.69]) (test).</p><p><strong>Conclusions: </strong>Delta radiomics and volumes can detect changes in nodule properties over time, which are predictive of nodule invasiveness. These tools could improve conventional radiologic assessment, allow for earlier intervention for aggressive nodules, and decrease unnecessary intervention-related morbidity.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019375","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":"Mesothelioma Mimicking a Mediastinal Tumor in the Prevascular Compartment: A Case Report.","authors":"Tomoki Takahashi, Yoshiyuki Ozawa, Hidekazu Hattori, Masahiko Nomura, Takahiro Ueda, Tomoya Horiguchi, Kazuyoshi Imaizumi, Yasushi Matsuda, Yasushi Hoshikawa, Yuka Kondo-Kawabe, Tetsuya Tsukamoto, Hiroyuki Nagata, Yoshiharu Ohno","doi":"10.1097/RTI.0000000000000809","DOIUrl":"10.1097/RTI.0000000000000809","url":null,"abstract":"","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"W96-W99"},"PeriodicalIF":1.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299687","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":"Left Atrial Strain for Prediction of Left Ventricular Reverse Remodeling After ST-segment Elevation Myocardial Infarction by Cardiac Magnetic Resonance Feature Tracking.","authors":"Zhaoxia Yang, Yuanyuan Tang, Wenzhe Sun, Jinyang Wen, Dazhong Tang, Yi Luo, Chunlin Xiang, Lu Huang, Liming Xia","doi":"10.1097/RTI.0000000000000795","DOIUrl":"10.1097/RTI.0000000000000795","url":null,"abstract":"<p><strong>Purpose: </strong>The study aimed to investigate the potential utility of left atrial (LA) strain by using cardiac magnetic resonance feature-tracking (CMR-FT) to predict left ventricular reverse remodeling (LVRR) following ST-segment elevation myocardial infarction (STEMI).</p><p><strong>Materials and methods: </strong>Patients with a first STEMI treated by primary percutaneous coronary intervention were consecutively enrolled in the prospective study and underwent CMR scans at 5 days and 4 months. LA global longitudinal strain (reservoir strain [εs], conduit strain [εe], booster strain [εa]) and corresponding strain rate were assessed by CMR-FT using cine images. LVRR was defined as a reduction in the LV end-systolic volume index of >10% from baseline to follow-up. Logistic regression analyses were performed to determine the predictors of LVRR.</p><p><strong>Results: </strong>Of 90 patients analyzed, patients with LVRR (n=35, 39%) showed higher values of LA strain and strain rate and less extensive infarct size (IS) compared with patients without LVRR (n=55, 61%) at initial and second CMR. The LVRR group demonstrated significant improvements in LV and LA cardiac function over time, especially the obvious increase in LA strain and strain rate. In multivariate logistic regression analyses, εs and εe, together with IS, were independent predictors of LVRR. The combination of εs and IS could optimally predict the LVRR with the highest area under the curve of 0.743.</p><p><strong>Conclusions: </strong>Post-STEMI patients with LVRR presented better recovery from cardiac function and LA deformation compared with patients without LVRR. Assessment of εs and εe by using CMR-FT after STEMI enabled prediction of LVRR.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"367-375"},"PeriodicalIF":1.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297193","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":"Incidental Apical Pleuroparenchymal Scarring on Computed Tomography: Diagnostic Yield, Progression, Morphologic Features and Clinical Significance.","authors":"Danielle Toussie, Mark Finkelstein, Dexter Mendoza, Jose Concepcion, Jadranka Stojanovska, Lea Azour, Jane P Ko, William H Moore, Ayushi Singh, Arielle Sasson, Priya Bhattacharji, Corey Eber","doi":"10.1097/RTI.0000000000000794","DOIUrl":"10.1097/RTI.0000000000000794","url":null,"abstract":"<p><strong>Purpose: </strong>Apical pleuroparenchymal scarring (APPS) is commonly seen on chest computed tomography (CT), though the imaging and clinical features, to the best of our knowledge, have never been studied. The purpose was to understand APPS's typical morphologic appearance and associated clinical features.</p><p><strong>Patients and methods: </strong>A random generator selected 1000 adult patients from all 21516 chest CTs performed at urban outpatient centers from January 1, 2016 to December 31, 2016. Patients with obscuring apical diseases were excluded to eliminate confounding factors. After exclusions, 780 patients (median age: 64 y; interquartile range: 56 to 72 y; 55% males) were included for analysis. Two radiologists evaluated the lung apices of each CT for the extent of abnormality in the axial plane (mild: <5 mm, moderate: 5 to 10 mm, severe: >10 mm), craniocaudal plane (extension halfway to the aortic arch, more than halfway, vs below the arch), the predominant pattern (nodular vs reticular and symmetry), and progression. Cohen kappa coefficient was used to assess radiologists' agreement in scoring. Ordinal logistic regression was used to determine associations of clinical and imaging variables with APPS.</p><p><strong>Results: </strong>APPS was present on 65% (507/780) of chest CTs (54% mild axial; 80% mild craniocaudal). The predominant pattern was nodular and symmetric. Greater age, female sex, lower body mass index, greater height, and white race were associated with more extensive APPS. APPS was not found to be associated with lung cancer in this cohort.</p><p><strong>Conclusion: </strong>Classifying APPS by the extent of disease in the axial or craniocaudal planes, in addition to the predominant pattern, enabled statistically significant associations to be determined, which may aid in understanding the pathophysiology of apical scarring and potential associated risks.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":"376-384"},"PeriodicalIF":1.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155647","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}