Journal of Thoracic Imaging最新文献

Purpose: This study investigated the prognostic value and risk reclassification ability of coronary atherosclerosis progression through serial coronary computed tomography angiography (CCTA).

Materials and methods: 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.

Results: 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.

Conclusions: Coronary atherosclerosis progression through CCTA significantly increased the prognostic value and risk stratification for MACE compared with baseline risk factor evaluation and CCTA only.

Purpose: To investigate the imaging performance and parametric analysis of magnetic resonance imaging (MRI) immediately after microwave ablation (MWA) of lung malignancies.

Materials and methods: 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.

Results: 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).

Conclusions: 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.

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.

Purpose: 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.

Materials and methods: 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.

Results: 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).

Conclusions: 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.

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.

Purpose: 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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

Purpose: 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.

Materials and methods: 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.

Results: 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.

Conclusions: 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.

Purpose: 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.

Materials and methods: 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.

Results: 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.

Conclusions: US-TTNB is an effective and safe procedure for pleural lesions and peripheral lung lesions.

Purpose: 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.

Materials and methods: 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.

Results: 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).

Conclusions: 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.