Journal of Thoracic Imaging最新文献

Purpose: To describe the spectrum of computed tomography (CT) findings in various chronic pulmonary aspergillosis (CPA) subtypes.

Material and methods: This retrospective study analyzed the CT scans of consecutively diagnosed CPA cases. Two radiologists independently evaluated the CT findings (both qualitatively and quantitatively) to characterize the lung cavities, intracavitary contents, pericavitary opacities and fibrosis, mediastinal shift, pleural thickening, and underlying structural lung disease. Patients were then classified into CPA subtypes, and between-group differences were assessed using the sample t test, Wilcoxon test, χ2 test, and Fisher exact test.

Results: Among 103 patients with CPA (mean age: 47.26 ± 1.98 y; 69 men), 77.7%, 15.5%, and 6.8% were categorized as chronic cavitary pulmonary aspergillosis, chronic fibrosing pulmonary aspergillosis (CFPA), and single/simple aspergilloma, respectively. The mean symptom duration was 2.7 ± 3.96 years, with cough being the most common symptom (86.4%). Underlying post-tubercular lung abnormalities were observed in 97.1% of the patients. Cavities were observed in all patients (100%), most commonly in the left upper lobe (68.0%). The difference in cavity number among CPA subtypes was statistically significant (P = 0.003), with 87.5% CFPA and 41.5% chronic cavitary pulmonary aspergillosis cases showing multiple cavities. The overall median cavity wall thickness was 6 mm (interquartile range: 2.8), with the highest value in the CFPA. Pericavitary fibrosis was observed in 70.9% of overall cases and in 100% of CFPA cases (P < 0.001). Intracavitary contents were identified in 89.3% of patients. The median pleural thickness was 8 mm (interquartile range: 4), which was significantly different among CPA subtypes (P = 0.001). There was excellent interobserver agreement (k = 0.94) between the two readers. Posterior intercostal lymph nodes were identified in 66%, a novel CPA observation.

Conclusion: Comprehensive qualitative and quantitative assessment of CT findings improves the characterization of the CPA subtypes. The number and size of lung cavities, mediastinal shift, and pleural thickness, among other quantitative parameters, vary significantly across CPA subtypes, facilitating more accurate differentiation between them.

Acute pulmonary injury can occur in response to any number of inciting factors. The body's response to these insults is much less diverse and usually categorizable as one of several patterns of disease defined by histopathology, with corresponding patterns on chest CT. Common patterns of acute injury include diffuse alveolar damage, organizing pneumonia, acute eosinophilic pneumonia, and hypersensitivity pneumonitis. The ultimate clinical diagnosis is multidisciplinary, requiring a detailed history and relevant laboratory investigations from referring clinicians, identification of injury patterns on imaging by radiologists, and sometimes tissue evaluation by pathologists. In this review, several clinical diagnoses will be explored, grouped by imaging pattern, with a representative clinical presentation, a review of the current literature, and a discussion of typical imaging findings. Additional information on terminology and disambiguation will be provided to assist with comprehension and standardization of descriptions. The focus will be on the acute phase of illness from presentation to diagnosis; treatment methods and chronic sequela of acute disease are beyond the scope of this review.

Purpose: To develop and validate an accurate computed tomography-based radiomics model for predicting high-grade (micropapillary/solid) patterns in T1-stage lung invasive adenocarcinoma (IAC) after propensity score matching (PSM).

Materials and methods: We enrolled 546 participants from 2 cohorts with histologically diagnosed lung IAC after complete surgical resection between January 2020 and August 2021. The patients were divided into high-grade and non-high-grade groups and matched using PSM. Matched patient HRCT images were used to delineate regions of interest from tumors and extract radiomics features, and the random forest method was used to construct a radiomics model. The area under the receiver operating characteristic curve (area under the curve) was used to evaluate the model's performance, and external validation was performed to assess the model's generalizability.

Results: Before PSM, there was no statistically significant difference in age between the two groups, though nodule type and sex exhibited significant differences (P < 0.05) in both cohorts. After PSM, we matched 176 and 97 pairs of patients in the 2 cohorts. In both cohorts, sex and nodule type were equal between the two groups, with a higher percentage of males and solid nodules in both groups. Our model exhibited moderate predictive performance after PSM, with area under the curve values of 0.75 (95% CI: 0.70-0.80) and 0.71 (95% CI: 0.63-0.80) for the development and external validation cohorts, respectively.

Conclusion: Although the nodule type compromised the validity of the model's performance, our results suggest that our acute computed tomography-based radiomics model could preoperatively predict micropapillary/solid patterns in patients with stage I lung IAC after PSM.

Purpose: To assess the performance of a newly introduced deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in reducing the dose of pediatric chest CT by using the image data of below 3-year-old patients with congenital heart disease (CHD).

Materials and methods: The lung image available from routine-dose cardiac CT angiography (CTA) on below 3 years patients with CHD was employed as a reference for evaluating the paired low-dose chest CT. A total of 191 subjects were prospectively enrolled, where the dose for chest CT was reduced to ~0.1 mSv while the cardiac CTA protocol was kept unchanged. The low-dose chest CT images, obtained with the AIIR and the hybrid iterative reconstruction (HIR), were compared in image quality, ie, overall image quality and lung structure depiction, and in diagnostic performance, ie, severity assessment of pneumonia and airway stenosis.

Results: Compared with the reference, lung image quality was not found significantly different on low-dose AIIR images (all P>0.05) but obviously inferior with the HIR (all P<0.05). Compared with the HIR, low-dose AIIR images also achieved a closer pneumonia severity index (AIIR 4.32±3.82 vs. Ref 4.37±3.84, P>0.05; HIR 5.12±4.06 vs. Ref 4.37±3.84, P<0.05) and airway stenosis grading (consistently graded: AIIR 88.5% vs. HIR 56.5% ) to the reference.

Conclusions: AIIR has the potential for large dose reduction in chest CT of patients below 3 years of age while preserving image quality and achieving diagnostic results nearly equivalent to routine dose scans.

Purpose: To investigate the left ventricular (LV) trabecular complexity and evaluate its relationship with LV cardiac function and especially myocardial strain in patients with hypertrophic cardiomyopathy (HCM).

Materials and methods: A total of 100 patients were retrospectively recruited in the study, including 50 obstructive hypertrophic cardiomyopathy (HOCM) and 50 nonobstructive HCM (NOHCM). Fifty age-matched and sex-matched healthy participants were also enrolled. The global and regional LV fractal dimensions (FDs), the global radial, circumferential, and longitudinal strain (GRS, GCS, and GLS) for LV were measured. FDs and myocardial strain parameters among 3 groups with post hoc paired comparisons. Correlations analysis and receiver operating characteristic analysis were performed.

Results: Mean global FD, max basal FD, and max apical FD were higher in patients with HCM compared with the healthy individuals (1.310 ± 0.046 vs 1.229 ± 0.027, 1.388 ± 0.089 vs 1.313 ± 0.039, 1.393 ± 0.108 vs 1.270 ± 0.041, all P < 0.001). Patients with HOCM showed significantly higher max apical FD than patients with NOHCM (1.432 ± 0.100 vs 1.355 ± 0.102, P < 0.001). The increased global FD was associated with reduced myocardial deformation across all 3 measures of global strain (GCS: r = 0.529, P < 0.001; GLS: r = 0.54, P < 0.001; GRS: r = -0.253, P = 0.002). Max apical FD yielded an area under the curve of 0.73 (95% CI: 0.63-0.83) for discriminating HOCM from NOHCM.

Conclusions: LV trabecular complexity is compensatively increased in patients with HCM and the max apical FD was more pronounced in patients with HOCM. The increased LV global trabecular complexity might be correlated with LV systolic dysfunction.

Purpose: To assess the accuracy of transaortic valve replacement (TAVR) planning CT examinations with a broad detector and a single dose of contrast media to diagnose CAD in a large patient cohort.

Materials and methods: In this retrospective study, consecutive patients who underwent a dedicated TAVR planning CT examination with a single contrast media dose and a 320-detector row between November 2017 and March 2021 were screened for inclusion. Inclusion criteria were a complete and correctly performed CT exam as well as an invasive coronary angiography (ICA). The scan consisted of 3 series: (1) ECG-triggered calcium score series over the heart. (2) ECG triggered i.v. CM scanning over the heart covering the entire cardiac cycle (0% to 100%). (3) non-ECG triggered scanning over the thoracic-abdominal area including subclavian and femoral arteries. For 2 and 3, a single i.v. CM bolus was used (300 mg iodine per kg total body weight of iodixanol, minimum 47 mL, maximum 75 mL at 100 kVp; 90 mL at 120 kVp). CT-derived CAD was defined as either free of obstructive CAD (<50%) or showing obstructive disease (>50%), further subclassified in moderate stenosis (50%-70%), or severe stenosis (>70%) for each vessel. ICA data were used as standard of reference.

Results: We studied 599 patients (78.6±7.5 y, 358 men). In ICA, 428 of 2396 coronary vessels (17.8%) demonstrated stenosis of 50% or more. In a per-patient analysis, CTA had a sensitivity of 97.6% and specificity of 84.3% for the detection of patients with at least one vessel with stenosis of 50% or more as well as a NPV and PPV of 97.8% and 82.2%, respectively. In a per-vessel analysis, CTA had 80.8% sensitivity and 88.1% specificity for the detection of stenosis of 50% or more, as well as an NPV and PPV of 95.5% and 59.6%, respectively.

Conclusions: Single-dose TAVR planning CT imaging with a wide detector has high sensitivity and NPV to exclude at least moderate CAD in TAVR candidates.

Purpose: Computed tomography (CT) is crucial in oncologic imaging for precise diagnosis and staging. Beam-hardening artifacts from contrast media in the superior vena cava can degrade image quality and obscure adjacent structures, complicating lymph node assessment. This study examines the use of virtual monoenergetic reconstruction with photon-counting detector CT (photon-counting CT) to mitigate these artifacts.

Materials and methods: The retrospective study included 50 patients who underwent thoracoabdominal scans. Virtual monoenergetic reconstructions at nine keV levels (60 to 140 keV) were analyzed for Hounsfield Unit (HU) stability, image noise, and artifact index in various regions of interest (ROIs): mediastinal adipose tissue (ROI 1 to 3) and vascular stations (ROI 4 to 6) were compared with reference tissue (ROI 7 to 8). The diagnostic image quality of the keV levels was assessed using a 5-point Likert Scale.

Results: Lower keV values (60 to 80) exhibited higher image noise and lower HU stability in mediastinal adipose tissue compared with higher energies, with optimal noise reduction observed at 130 keV (ROI 1 to 3). HU stability in vascular structures (ROI 4 to 6) significantly improved above 80 keV, with the best performance at 140 keV. Artifact levels decreased progressively from 60 to 140 keV. Visually, keV levels of 110 keV (96% Likert ≥4) and 120 keV (60% Likert 4) were rated most diagnostically valuable, consistent with technical findings.

Conclusion: Virtual monoenergetic reconstructions with photon-counting CT effectively reduce beam-hardening artifacts near the superior vena cava, enhancing the visualization of lymph nodes and adjacent structures. This technology advances oncologic imaging by improving diagnostic accuracy in areas previously affected by artifact-related image degradation.

The term "aspiration" describes lung injury that results from unintentional passage of contents other than air into the lungs and bronchial tree, commonly from the gastrointestinal and upper respiratory tracts. Only a small proportion of aspiration-related events are symptomatic, especially in predisposed individuals such as patients with diminished consciousness, impaired swallowing, oesophageal motility disorders, and reflux disease. Aspiration-related syndromes can be classified based on the onset of presentation, composition of the aspirated substance, and anatomic site of injury. When considering the injury site, the aspirated material can either obstruct the airways, thereby presenting with obstructive features (eg, atelectasis on radiographs); it can alternatively affect the lung parenchyma. Lung injury due to aspiration of noxious agents such as acid or lipids is termed chemical aspiration pneumonitis. The aspirated contents can alternatively lead to lung parenchymal infection when the contents are infected, which is termed aspiration pneumonia. We have reviewed the imaging manifestations of various entities related to aspiration and have classified the abnormalities based on the site of injury and the nature of the culprit agent. Differences between "aspiration pneumonitis" and "aspiration pneumonia" are also explored. This is followed by an illustration of a few specific situations related to aspiration in pediatric and adult patients.

Purpose: 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).

Materials and methods: 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.

Results: 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).

Conclusion: 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.

Purpose: 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.

Materials and methods: 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)).

Results: 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).

Conclusions: 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.