Journal of Thoracic Imaging最新文献

{"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":"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":"2025-05-01","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}

{"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":"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":"2025-05-01","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":"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":"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":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005866/pdf/","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":"OA","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":"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":"2025-05-01","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":"Acute Lung Injury.","authors":"Nupur Verma, Bruno Hochhegger, Sanjay Mukhopadhyay, Pedro Paulo Teixeira E Silva Torres, Tan-Lucien Mohammed","doi":"10.1097/RTI.0000000000000820","DOIUrl":"10.1097/RTI.0000000000000820","url":null,"abstract":"<p><p>Acute lung injury (ALI) is acute pulmonary inflammation with underlying pathology of disruption of the pulmonary vasculature endothelial and alveolar epithelial barriers. ALI is not an uncommon diagnosis and has a myriad of causes including pulmonary infection, (including sepsis), drugs, connective tissue disease, and polytrauma. Patients present clinically with hypoxemia with imaging supportive of bilateral pulmonary findings without pulmonary edema. The imaging findings in ALI mirror pathologic changes, with a transition from an early (\"exudative\") phase to a later fibroblast-rich (\"organizing\" or \"proliferative\") phase to, in some cases, a fibrotic phase. The diagnosis of ALI is separate from, but can clinically overlap in presentation with, acute respiratory distress syndrome and is characterized by diffuse alveolar damage and organizing pneumonia patterns on pathology. Clinical management is most often supportive and can include corticosteroids, mechanical ventilation, and careful fluid management, with the goal of preserving and recovering lung function.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803039","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 Azygos Esophageal Recess Is Not to Be Missed in Screening Lung Cancer With LDCT.","authors":"Mario Mascalchi, Edoardo Cavigli, Giulia Picozzi, Diletta Cozzi, Giulia Raffaella De Luca, Stefano Diciotti","doi":"10.1097/RTI.0000000000000813","DOIUrl":"10.1097/RTI.0000000000000813","url":null,"abstract":"<p><strong>Purpose: </strong>Lesion overlooking and late diagnostic workup can compromise the efficacy of low-dose CT (LDCT) screening of lung cancer (LC), implying more advanced and less curable disease stages. We hypothesized that the azygos esophageal recess (AER) of the right lower lobe (RLL) might be an area prone to lesion overlooking in LC screening.</p><p><strong>Materials and methods: </strong>Two radiologists reviewed the LDCT examinations of all the screen-detected incident LCs observed in the active arm of 2 randomized clinical trials: ITALUNG and national lung screening trial. Those in the AER were compared with those in the remainder of the RLL for possible differences in diagnostic lag according to the Lung-RADS 1.1 recommendations, size, stage, and mortality.</p><p><strong>Results: </strong>Six (11.7%) of 51 screen-detected incident LCs of the RLL were located in the AER. The diagnostic lag time was significantly longer ( P =0.046) in the AER LC (mean 14±9 mo) than in the LC in the remaining RLL (mean 7.3±1 mo). Size and stage at diagnosis were not significantly different. All 6 subjects with LC in the AER and 16 (35.5%) of 45 subjects with LC in the remaining RLL ( P =0.004) died of LC after a median follow-up of 12 years.</p><p><strong>Conclusion: </strong>Our retrospective study indicates that AER might represent a lung region of the RLL prone to have early LC overlooked due to detection or interpretation errors with possible detrimental consequences for the subject undergoing LC screening.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299688","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":"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}