Journal of Thoracic Imaging最新文献

Chest wall reconstruction (CWR) is a complex and evolving field that clinically benefits from the use of multimodal radiologic imaging. This review summarizes the essential role of multimodal imaging, such as ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), in preoperative and postoperative CWR evaluation. Preoperative CWR planning involves characterization of defects, assessment of surrounding structures, and guidance for surgical approach and implant selection. Postoperative CWR evaluation focuses on monitoring graft/flap viability, assessing structural integrity, and identifying complications such as infection or hardware failure. This article guided radiologists in approaching CWR cases and creating effective reports to guide patient management.

Purpose: To identify preoperative CT findings linked to retrosternal adherence-related intraoperative cardiovascular injury and develop predictive scores with the potential to guide surgical planning.

Materials and methods: A retrospective study was conducted on patients undergoing CT within 30 days of sternotomy (first sternotomy or resternotomy) from 2019 to 2023. CT images were reviewed for retrosternal adherence patterns, classified as distance, contact, or adherence, and localized by segment (upper, middle, or lower retrosternal thirds) or by organ (innominate vein, aorta, right ventricle, right atrium, or pulmonary artery). Logistic regression was used to identify the significant predictors from which the scores were developed.

Results: Out of 429 patients, 105 (24%) had cardiovascular injuries, including re-entry and postcardiopulmonary bypass injuries. Middle third adherence (P<0.001), calcification (P<0.001), and age (P=0.002) were significant predictors in the segment approach. Aortic (P=0.001) and right atrial (P=0.034) adherence, calcification (P<0.001), and age (P=0.001) were significant predictors in the organ-specific approach. CAST (Calcification, Age, Sternal Thirds) and ARCA (Aorta, Right Atrium, Calcification, Age) scores were derived to predict intraoperative cardiovascular injuries.

Conclusions: Preoperative CT can identify patients at high risk for intraoperative cardiovascular injury during sternotomy. The CAST and ARCA scores offer a reliable, CT-based approach for assessing this risk, potentially enhancing surgical planning and preemptive intervention strategies, thereby improving outcomes in high-risk cardiac reoperations.

Artificial intelligence (AI) is rapidly transforming cardiac computed tomography (CT) imaging by enhancing image acquisition, reconstruction, and analysis to improve diagnostic accuracy and overall clinical workflow. Deep learning reconstruction (DLR) algorithms optimize image quality while reducing radiation and contrast media doses. AI-driven tools for coronary artery segmentation and CAD-RADS classification ensure greater reproducibility and efficiency in coronary artery disease (CAD) assessment. Beyond anatomic evaluation, AI enhances functional imaging with CT-derived fractional flow reserve and myocardial CT perfusion imaging, improving the noninvasive identification of myocardial ischemia associated with flow-limiting coronary lesions. AI also plays a key role in CAD phenotyping through automating quantification and characterization of total plaque burden and identifying rupture-prone plaques and high-risk patients. Radiomics and machine learning models analyzing pericoronary adipose tissue (PCAT) propose new biomarkers of coronary inflammation, refining risk stratification and disease monitoring. Fusion models integrating clinical, imaging, and laboratory data are emerging as powerful tools for comprehensive cardiovascular risk prognostication, surpassing traditional clinical risk scores. Looking ahead, generative AI and large language models (LLMs) could revolutionize radiology workflows by automating report generation and relevant clinical data extraction and integration, while digital twins may enable real-time simulation of patient-specific models that predicts disease progression and treatment response. Despite these advances, challenges like data diversity and standardization, model interpretability, and regulatory approval must be further addressed for AI to reach full integration into clinical practice. As AI-driven technologies continue to evolve, interdisciplinary collaboration will be essential to ensure responsible implementation, ultimately advancing precision medicine in cardiovascular care.

Purpose: The diagnosis of connective tissue disease-associated interstitial lung diseases (CTD-ILD) is connected to radiation exposure due to periodical CT scans. This study aims to investigate the alternative imaging method, Phase-Resolved Functional Lung (PREFUL), regarding its performance in low-field MRI. A comparison of PREFUL, photon-counting CT (PCCT) and pulmonary function tests (PFT) was performed to identify correlations that could restructure the diagnostics of CTD-ILD.

Materials and methods: In this prospective single-center study, free-breathing PREFUL acquisitions of CTD-ILD patients were done after clinically indicated PCCT imaging. The severity and extent of CTD-ILD in PCCT were assessed via the Warrick score and used as a reference. Spearman's correlation coefficient (r) was calculated to examine the association between PREFUL, PCCT, and PFT.

Results: The data of 31 CTD-ILD patients (64.32±12.36 y, 10 men) were evaluated. Most correlations of PREFUL parameters with PFT were found with the Tiffeneau-Pinelli index (FEV1/FVC). The Warrick score showed excellent inter-rater agreement and correlations (P<0.05) with the PFT parameters forced vital capacity (FVC) and the diffusing capacity of the lung for carbon monoxide corrected for hemoglobin (DLCOc) [FVC: r=-0.43, DLCOc SB: r=-0.65, DLCOc/VA: r=-0.50]. No correlation was found between PREFUL parameters and PCCT.

Conclusions: The feasibility of PREFUL using low-field MRI was demonstrated in patients with CTD-ILD. Several correlations between PREFUL and PFT parameters were found, indicating that MRI can quantify lung function impairment. Nevertheless, CT remains the gold standard for CTD-ILD assessment and further research in PREFUL is needed.

Computed tomography (CT) is routinely used in diagnosing and managing patients with chronic lung diseases such as chronic obstructive pulmonary disease (COPD) and fibrosing interstitial lung disease (ILD). Visual assessment of disease morphology/phenotype and extent correlates with lung function and patient prognosis, but it is limited by reader subjectivity and interobserver variability. Quantitative CT (QCT) techniques based on density and texture-based features of the lungs have shown stronger correlations with physiologic and survival outcomes in both COPD and ILD cohort studies. Moreover, recent advances in computer processing capabilities have led to the implementation of machine and deep learning-based approaches, allowing for greater robustness and reproducibility beyond visual assessment and density-based methods. This review focuses on QCT and artificial intelligence (AI) techniques for COPD, ILD, and bronchiolitis obliterans syndrome in lung and hematopoietic stem cell transplant recipients. Current challenges and limitations for adoption of these techniques and future directions of QCT and AI in thoracic imaging are also discussed.

Purpose: Acute pulmonary embolism (APE) is the third leading cardiovascular cause of death. Current risk assessment approaches emphasize right ventricular (RV) dysfunction and thrombus burden quantification via computed tomography pulmonary angiography (CTPA). Traditional scoring systems, such as the Modified Miller Score (MMS) or Refined Miller Score (RMS), estimate thrombus burden but tend to oversimplify partial vessel occlusion. This study proposes a novel Obstruction Index (OI) derived from direct thrombus and vessel area measurements from CTPA imaging to improve quantification accuracy.

Materials and methods: This retrospective study analyzed imaging data from 20 patients with intermediate- to high-risk APE. Pre-randomization and posttreatment CTPA scans were assessed for RV/LV ratio, MMS, RMS, and OI. OI was derived from measured thrombus and vessel areas at defined pulmonary artery levels and from the calculated obstruction ratio. Correlations between RV/LV ratio reduction and reduction of MMS, RMS, and OI were evaluated using the Spearman correlation.

Results: Mean RV/LV ratio reduced significantly post treatment (1.498±0.396 to 1.156±0.275), as did MMS (-4.5±4.3), RMS (-4.925±4.2), and OI (-4.49±3.9). OI demonstrated a stronger correlation with RV/LV ratio reduction (r=0.448, P=0.048) compared with MMS (r=0.279, P=0.234) and RMS (r=0.261, P=0.265).

Conclusions: The OI outperforms MMS and RMS in accuracy when reflecting thrombus burden reduction and shows statistically significant correlation with RV/LV ratio reduction. Direct thrombus and vessel area measurements appear to be superior for precise and reproducible APE quantification, and are especially useful for posttreatment imaging follow-ups.

Purpose: To investigate the feasibility of using 60 kVp coronary CT angiography (CCTA) combined with deep learning-based CT reconstruction as a screening tool on asymptomatic patients.

Materials and methods: A total of 156 asymptomatic patients (body mass index, 24.4 ± 2.2 kg/m2) with at least one coronary artery disease (CAD) risk factor were prospectively enrolled for taking an experimental ultra-low dose 60 kVp CCTA followed by a routine 120 kVp CCTA. Stenosis detection, plaque analysis, and image quality assessment were performed on both scans, with 120 kVp CCTA serving as the reference.

Results: The mean effective dose and mean contrast medium (CM) dosage were 0.4 ± 0.1 mSv and 27.0 ± 3.2 mL, respectively, for 60 kVp CCTA, corresponding to a 91.5% and 50.0% reduction as compared with 120 kVp CCTA. In both analyses for all plaque types and noncalcific plaques, the sensitivity, specificity, and accuracy in stenosis detection were >92% with 60 kVp CCTA on per-segment, per-vessel, and per-patient basis, and in particular, the negative predictive value was ≥ 97%. However, compared to 120 kVp CCTA, 60 kVp CCTA led to a significant overestimation in plaque volume and stenosis severity (P<0.01), as well as inferior subjective scores regarding vessel and lumen delineation (P<0.05).

Conclusions: Despite overestimation in plaque volume and stenosis severity, 60 kVp CCTA showed excellent stenosis detection capability with ultra-low radiation dose and reduced CM dosage that may potentially be adopted as a screening tool for asymptomatic patients in routine practice.

In this review, we highlight how artificial intelligence, specifically deep learning, is reshaping every aspect of cardiovascular magnetic resonance imaging: from planning and acquisition to reconstruction, analysis, and clinical report generation. We first introduce core machine learning paradigms and concepts, then survey recent deep learning advances to automate and enhance multiple aspects of MRI. We highlight the range of recent advances to provide a conceptual understanding of how the field has rapidly evolved in the last 10 years, enabling improvements in acquisition speed, spatial resolution, suppression of artifacts, and correction for motion. Automation of postprocessing is providing us a deeper look into detailed analysis of regional cardiac function and measurement of hemodynamics, and a greater ability to automatically integrate interpretation with nonimaging clinical data to support prognostication and management. Advances in artificial intelligence will continue to shape our practice of clinical cardiovascular MRI to provide greater efficiency and enrich our ability to guide the management of patients with cardiovascular disease.

This review traces the historical path of artificial intelligence (AI) methods that have been applied to medical image interpretation. Early AI approaches, which were based on clinical expertise and domain-specific medical knowledge, established the basis for data-driven methods, initiating the radiomics era and leading to the widespread use of deep learning in medical imaging. More recently, transformer architectures-originally developed for natural language processing-have been adapted for medical image analysis. In the first section, we explore the literature on the use of AI, specifically addressing lung nodules and lung cancer. AI has been effective in detecting lung nodules, evaluating their characteristics, and predicting cancer risk, while also addressing technical issues like kernel conversion. In lung cancer, AI has been applied to various clinical needs, including prognosis evaluation, mutation identification, treatment response analysis, operability prediction, treatment-related pneumonitis, and clinical information extraction. In the following section, we explore foundation models, multimodal AI, and a multiomic approach in the field of lung nodules and lung cancer. Finally, as AI models continue to evolve, so too must the approaches for evaluating their real-world utility; thus, we outline relevant methods for evaluating the performance and application of AI in thoracic radiology.

Purpose: Accurate lung cancer TNM staging depends on macroscopic and microscopic tumor evaluation of resection specimens. However, small nodules (<1 cm) are difficult to extract and correlate with in vivo imaging. We investigated whether microCT could better localize lesions or guide pathology to otherwise undetected abnormalities.

Materials and methods: Paired ex vivo CT and microCT were performed after inflating and freezing surgically removed lung lobes (resolution 80 to 120 µm). Rigorous matching between CT, microCT, and histopathology was performed on areas containing abnormalities on microCT.

Results: A total of 57 lobectomy specimens were analyzed. MicroCT-guided microscopic examination led to 2 additional primary carcinomas, 2 separate tumor nodules from the primary lung tumor, and 1 atypical adenomatous hyperplasia lesion that were not evident before surgery. For both patients with separate tumor nodules, the cT1 stage was upgraded to a pT3. In addition, the microCT provided insight into underlying structural disease (ie, emphysema and fibrosis).

Conclusions: In 5 out of 57 resection specimens (9%), microCT showed additional (pre-)cancerous lesions. This explorative study suggests that lobar microCT could serve as a valuable guide for pathologists by pointing them toward areas that may warrant further investigation. In this way, it is a practical and beneficial tool, capable of facilitating a more precise TNM classification in tumor resection specimens, which needs further validation in a prospective study.