Journal of Thoracic Imaging最新文献

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: 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.

Purpose: To investigate and compare the diagnostic performance of 10 different large language models (LLMs) and 2 board-certified general radiologists in thoracic radiology cases published by The Society of Thoracic Radiology.

Materials and methods: We collected publicly available 124 "Case of the Month" from the Society of Thoracic Radiology website between March 2012 and December 2023. Medical history and imaging findings were input into LLMs for diagnosis and differential diagnosis, while radiologists independently visually provided their assessments. Cases were categorized anatomically (parenchyma, airways, mediastinum-pleura-chest wall, and vascular) and further classified as specific or nonspecific for radiologic diagnosis. Diagnostic accuracy and differential diagnosis scores (DDxScore) were analyzed using the χ2, Kruskal-Wallis, Wilcoxon, McNemar, and Mann-Whitney U tests.

Results: Among the 124 cases, Claude 3 Opus showed the highest diagnostic accuracy (70.29%), followed by ChatGPT 4/Google Gemini 1.5 Pro (59.75%), Meta Llama 3 70b (57.3%), ChatGPT 3.5 (53.2%), outperforming radiologists (52.4% and 41.1%) and other LLMs (P<0.05). Claude 3 Opus DDxScore was significantly better than other LLMs and radiologists, except ChatGPT 3.5 (P<0.05). All LLMs and radiologists showed greater accuracy in specific cases (P<0.05), with no DDxScore difference for Perplexity and Google Bard based on specificity (P>0.05). There were no significant differences between LLMs and radiologists in the diagnostic accuracy of anatomic subgroups (P>0.05), except for Meta Llama 3 70b in the vascular cases (P=0.040).

Conclusions: Claude 3 Opus outperformed other LLMs and radiologists in text-based thoracic radiology cases. LLMs hold great promise for clinical decision systems under proper medical supervision.

Purpose: 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).

Material and methods: 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.

Results: 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).

Conclusions: 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.

Purpose: 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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

Purpose: Delays to biopsy and surgery after lung nodule detection can impact survival from lung cancer. The aim of this study was to identify factors associated with delay in a lung cancer screening (LCS) program.

Materials and methods: We evaluated patients in an LCS program from May 2015 through October 2021 with a malignant lung nodule classified as lung CT screening reporting and data system (Lung-RADS) 4B/4X. A cutoff of more than 30 days between screening computed tomography (CT) and first tissue sampling and a cutoff of more than 60 days between screening CT and surgery were considered delayed. We evaluated the relationship between delays to first tissue sampling and surgery and patient sex, age, race, smoking status, median income by zip code, language, Lung-RADS category, and site of surgery (academic vs community hospital).

Results: A total of 185 lung cancers met the inclusion criteria, of which 150 underwent surgical resection. The median time from LCS CT to first tissue sampling was 42 days, and the median time from CT to surgery was 52 days. 127 (69%) patients experienced a first tissue sampling delay and 60 (40%) had a surgical delay. In multivariable analysis, active smoking status was associated with delay to first tissue sampling (odds ratio: 3.0, CI: 1.4-6.6, P = 0.005). Only performing enhanced diagnostic CT of the chest before surgery was associated with delayed lung cancer surgery (odds ratio: 30, CI: 3.6-252, P = 0.02). There was no statistically significant difference in delays with patients' sex, age, race, language, or Lung-RADS category.

Conclusion: Delays to first tissue sampling and surgery in a LCS program were associated with current smoking and performing diagnostic CT before surgery.

Purpose: To explore and compare the diagnostic values of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) parameters of primary lesions and lymph nodes (LNs) to predict mediastinal LN metastasis in patients with non-small cell lung cancer.

Patients and methods: Sixty-one patients with non-small cell lung cancer underwent preoperative magnetic resonance imaging, including multiple b -value DWI. The DWI parameters, including apparent diffusion coefficient (ADC) from a mono-exponential model, true diffusion (D) coefficient, pseudo-diffusion (D*) coefficient, and perfusion fraction (f) from a bi-exponential model, distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index (α) from a stretched-exponential model of primary tumors and LNs and the size characteristics of LNs, were measured and compared. Multivariate logistic regression analysis was used to establish models for predicting mediastinal LN metastasis. Receiver operating characteristic analysis was applied to evaluate diagnostic performances.

Results: The DWI parameters of primary tumors showed no statistical significance between LN metastasis-positive and LN metastasis-negative groups. Nonmetastatic LNs had significantly higher ADC, D, DDC, and α values compared with metastatic LNs (all P < 0.05). The short-dimension, long-dimension, and short-long dimension ratio of metastatic LNs was significantly larger than those of nonmetastatic ones (all P < 0.05). The D value showed the best diagnostic performance among all DWI-derived single parameters, and the short dimension of LNs performed the same among all the size variables. Furthermore, the combination of DWI parameters (ADC and D) and the short dimension of LNs can significantly improve diagnostic efficiency.

Conclusions: The ADC, D, DDC, and α from the mono-exponential, bi-exponential, and stretched-exponential models were demonstrated efficient in differentiating benign from metastatic LNs, and the combination of ADC, D, and short dimension of LNs may have a better diagnostic performance than DWI or size-derived parameters either in combination or individually.

Purpose: Inflammatory changes in epicardial (EAT) and pericardial adipose tissue (PAT) are associated with increased overall cardiovascular risk. Using routine, preinterventional cardiac CT data, we examined the predictive value of quantity and quality of EAT and PAT for outcome after transcatheter aortic valve replacement (TAVR).

Materials and methods: Cardiac CT data of 1197 patients who underwent TAVR at the in-house heart center between 2011 and 2020 were retrospectively analyzed. The amount and density of EAT and PAT were quantified from single-slice CT images at the level of the aortic valve. Using established risk scores and known independent risk factors, a clinical benchmark model (BMI, Chronic kidney disease stage, EuroSCORE 2, STS Prom, year of intervention) for outcome prediction (2-year mortality) after TAVR was established. Subsequently, we tested whether the additional inclusion of area and density values of EAT and PAT in the clinical benchmark model improved prediction. For this purpose, the cohort was divided into a training (n=798) and a test cohort (n=399).

Results: Within the 2-year follow-up, 264 patients died. In the training cohort, particularly the addition of EAT density to the clinical benchmark model showed a significant association with outcome (hazard ratio 1.04, 95% CI: 1.01-1.07; P =0.013). In the test cohort, the outcome prediction of the clinical benchmark model was also significantly improved with the inclusion of EAT density (c-statistic: 0.589 vs. 0.628; P =0.026).

Conclusions: EAT density as a surrogate marker of EAT inflammation was associated with 2-year mortality after TAVR and may improve outcome prediction independent of established risk parameters.