Journal of thoracic disease最新文献

Background: Pulmonary adenocarcinoma is the most common pathological type of malignant pulmonary nodules, of which, invasive adenocarcinoma (IAC) is associated with a risk of recurrence. Pulmonary tuberculoma can sometimes present as noncalcified, solid pulmonary nodule with imaging characteristics such as lobulation, spiculation, and pleural indentation, which is difficult to distinguish from IAC. Magnetic resonance imaging (MRI) as a non-ionizing modality can be complementary tool for nodules assessment. This study aimed to evaluate the potential of the combined conventional and modified anatomical MRI sequences for differential diagnosis of IAC and tuberculoma.

Methods: Sixty-seven patients with 82 noncalcified nodules underwent computed tomography (CT) and MRI (T1WI-starVIBE, T1WI-VIBE, T2WI-TSE-fBLADE). Two radiologists independently assessed nodule dimensions and morphologic features. The inter-method agreement of morphologic features assessment by CT and MRI sequences were compared using Kappa test. Multivariate logistic regression analyses were applied to identify independent predictors of IAC. Receiver operating characteristic (ROC) analysis was performed to investigate the differential diagnosis capability.

Results: Thirty-eight IACs and 44 tuberculomas were identified. Readers 1 and 2 underestimated the nodules mean diameter with T1WI-starVIBE (T1WI-VIBE, T2WI-TSE-fBLADE) by 0.86±1.71 mm (1.19±2.06, 0.15±1.96 mm) and 0.99±1.75 mm (1.27±2.04, 0.19±1.91 mm). The inter-method agreements between MRI and CT were "fair" to "excellent" in the evaluation of morphological features except for spiculation. Compared with the tuberculoma group, the IAC group was significant with unclear margin (T1WI-starVIBE, T1WI-VIBE), irregular morphology (CT, MRI), lobulation (CT, MRI), spiculation (T1WI-starVIBE, T2WI-TSE-fBLADE) and air bronchogram (CT, T1WI-starVIBE and T1WI-VIBE) (P<0.05). The area under the curve (AUC) values for the logistic model by the combination of CT and MRI were 0.867/0.877 (sensitivity 73.68%/76.32%, specificity 86.36%/86.36%) and were significantly higher than that by T1WI-starVIBE (P=0.002) and T1WI-TSE-fBLADE (P=0.03) (reader 1), as well as higher than that by CT (P=0.045) and T1WI-starVIBE (P=0.003) (reader 2).

Conclusions: The combined conventional and modified anatomical MRI sequences has diagnostic potential in distinguishing pulmonary IAC from tuberculoma.

Background: Lung cancer remains one of the leading causes of cancer-related deaths worldwide. This study utilized clinical risk factors along with intratumoral radiomics, peritumoral radiomics, and intratumoral subregional features extracted from computed tomography (CT) lung-window images for individual and integrated modeling to classify solid pulmonary nodules and identify the optimal model, thereby improving diagnostic accuracy while minimizing unnecessary invasive procedures.

Methods: CT images of 230 pathologically confirmed solitary solid pulmonary nodules were retrospectively collected from two hospitals. Among the 167 patients from the first hospital, 20% (n=34) served as the test set, while the remaining 80% (n=133) were used as the training and development set for 5-fold cross-validation, while data from the second hospital (n=63) served as an external test set. Intratumoral and peritumoral regions of interest (ROIs) were delineated on lung window images, and relevant radiomics features were extracted. Multiple machine learning algorithms-including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Support Vector Classifier (Linear SVC) etc.-were employed to construct predictive models for distinguishing benign from malignant solid pulmonary nodules.

Results: A triple-feature model (intratumoral, peritumoral, clinical) achieved superior diagnostic performance [area under the receiver operating characteristic curve (AUC): training 0.932, 95% confidence interval (CI): 0.897-0.960; test 0.833, 95% CI: 0.773-0.890; external test 0.741, 95% CI: 0.618-0.864] with high sensitivity/specificity. The intratumoral-peritumoral dual-modality model showed optimal cross-center robustness external test, AUC =0.808 (95% CI: 0.700-0.922). Habitat imaging revealed heterogeneity, AUC =0.750 (95% CI: 0.676-0.825). Decision curve analysis confirmed the triple-model's clinical utility. SHAP identified age, gender, and key radiomics (e.g., gradient_firstorder_Skewness_Intra) as top predictors. Multi-center test confirmed generalizability, positioning this integrated framework as a robust tool to reduce invasive procedures in pulmonary nodule management.

Conclusions: The multi-combination models developed in this study enhance the diagnostic accuracy for distinguishing benign from malignant solid pulmonary nodules, with the triple-feature model demonstrating the highest diagnostic performance. This approach has the potential to spare patients from unnecessary invasive procedures and strengthen clinical decision-making in the management of pulmonary nodules.

Background: Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery associated with prolonged hospitalization and increased morbidity. This study aimed to develop a simple preoperative risk score for predicting POAF.

Methods: We retrospectively analyzed 1,165 consecutive patients who underwent cardiac surgery between 2015 and 2024 at Osaka Metropolitan University Hospital. Patients with a prior history of atrial fibrillation (AF) or incomplete preoperative assessment were excluded. Clinical, electrocardiographic, and echocardiographic variables were evaluated, and independent predictors of POAF were identified by multivariable logistic regression. A weighted risk score was constructed and internally validated using 1,000 bootstrap resamples.

Results: POAF occurred in 496 patients (42.6%), of whom 22 (1.9%) progressed to persistent AF and 12 (1.0%) experienced cardiogenic stroke during hospitalization. Independent predictors of POAF included age, electrocardiographic left atrial overload, mitral valve surgery, echocardiographic left atrial enlargement, and obstructive ventilatory defect. The resulting risk score ranged from 0 to 8 or 10 points and demonstrated acceptable calibration and moderate discrimination [area under the curve (AUC): 0.701], with similar performance after bootstrap validation (AUC: 0.707). Subgroup analysis revealed that chronic kidney disease without dialysis independently predicted progression to persistent AF.

Conclusions: We developed a pragmatic preoperative risk score for predicting POAF using readily available parameters. The findings corroborate existing evidence regarding established POAF risk factors, and the proposed score demonstrated moderate predictive accuracy.

Background: Nutritional and inflammatory status influence latent tuberculosis infection (LTBI) susceptibility, but the role of composite biomarkers like advanced lung cancer inflammation index (ALI) is unclear. This study aimed to explore the association of the ALI with LTBI.

Methods: This cross-sectional study analyzed the National Health and Nutrition Examination Survey (NHANES) data of 3,010 adult participants in the USA from 2011 to 2012, of whom 382 were diagnosed with LTBI. Multivariate logistic regression, subgroup analysis, and interaction assessment were performed to investigate the association between ALI and LTBI. Restricted cubic spline (RCS) and threshold effect analysis were employed to examine the non-linear relationship. Mediation analysis was conducted to assess the mediating role of ALI. The adjusted receiver operating characteristic (ROC) curve was utilized to evaluate the prognostic value of ALI.

Results: After adjusting for all covariates, we found a significant inverse relationship between ALI and LTBI [odds ratio (OR) =0.765, 95% confidence interval (CI): 0.601-0.973], which persisted among men, participants without diabetes, and those with hypertension. The risk of LTBI tended to decrease with increasing tertiles of ALI (P for trend =0.002). The RCS curve revealed a non-linear relationship between log₂-ALI and LTBI (P for nonlinear =0.04), which was further confirmed by threshold effect analysis. ALI partially mediated the associations of smoking and poverty income ratio with LTBI (P=0.02). Compared with other indicators, although ALI had a slightly higher area under the curve (AUC) (0.7809, 95% CI: 0.7575-0.8044) in predicting LTBI, the difference was not statistically significant (all DeLong test P>0.05).

Conclusions: This study identified a significant non-linear association between ALI and LTBI risk. These preliminary findings highlight ALI as a potential integrated biomarker, but require validation in prospective studies.

This review summarizes advances in thoracic aortic disease research published in 2025, with literature retrieved from 2,829 articles via PubMed searches, focusing on risk prediction, pathogenesis, and treatment strategies. In risk prediction, key advances include: biomechanical validation of ascending aortic length (AAL) as a stable morphological predictor; the relationship between clonal hematopoiesis and thoracic aortic aneurysm (TAA) linked to Janus kinase 2 (JAK2) (V617F) mutation variant allele frequency (VAF); the iAorta artificial intelligence (AI) system achieving acute aortic syndrome (AAS) detection on non-contrast computed tomography (CT); and sex differences revealed by the DisSEXion study demonstrating significantly higher type A dissection incidence in women. Additional advances include cardiac magnetic resonance (CMR)-derived three-dimensional (3D) distensibility and displacement mapping, high biaxiality ratio analysis, integration of phenotypic age acceleration with genetic risk, reduced plasma cartilage oligomeric matrix protein (COMP) levels as a warning biomarker, gene-specific differences in hereditary thoracic aortic disease (HTAD), air pollution as an environmental risk factor, and causal association between remnant cholesterol and aortic disease. In pathogenesis, the hexosamine biosynthetic pathway-integrated stress response (HBP-ISR) axis provides a common therapeutic target for both hereditary and sporadic disease; piezo type mechanosensitive ion channel component (PIEZO)1 regulates transforming growth factor (TGF)-β signaling with the Yoda1 agonist showing therapeutic potential; Calpain-2-mediated endothelial focal adhesion disruption plays a critical role in dissection; CX3CR1+ macrophages can be targeted by C-C chemokine receptor (CCR)2 antagonist, which mitigates aneurysm progression; the cyclin-dependent kinase-like (CDKL)1-ciliary function association introduces ciliary biology into aortic pathophysiology; and developmental origin-dependent susceptibility differences among smooth muscle cells (SMCs) are identified. In treatment, the frozen elephant trunk (FET) strategy shows excellent long-term survival and functional recovery. The Society of Thoracic Surgeons (STS) risk model improves preoperative assessment. Analysis clarifies high mortality risks associated with coronary and mesenteric malperfusion. PERSEVERE study confirms favorable aortic remodeling with Ascyrus Medical Dissection Stent (AMDS) hybrid prosthesis. Importantly, research emphasizes the critical role of hospital rescue capability and regionalized aortic care networks, with evidence supporting efficient inter-hospital transfer systems to improve outcomes. Advances in compliant stent-grafts and total endovascular aortic arch repair techniques expand treatment options for high-risk patients.

Background: Invasive pulmonary aspergillosis (IPA) increasingly affects non-neutropenic patients, posing diagnostic challenges due to nonspecific clinical features and limited sensitivity of conventional tests. Early identification is critical for improving outcomes. This study aimed to develop and validate a machine learning-based tool for early risk stratification of IPA in this population.

Methods: A systematic meta-analysis identified independent IPA risk factors in non-neutropenic patients. A retrospective cohort of non-neutropenic patients was analyzed. Least absolute shrinkage and selection operator (LASSO) regression selected predictive features, and multiple machine learning (ML) algorithms were evaluated. The optimal model (random forest) was externally validated on an independent cohort. SHapley Additive exPlanations (SHAP) values interpreted feature importance.

Results: A retrospective cohort of 524 hospitalized non-neutropenic patients (422 training, 102 validation) adhering to established diagnostic criteria was analyzed. Meta-analysis confirmed diabetes [odds ratio (OR) =1.43], respiratory disease (OR =1.76), corticosteroid exposure (OR =1.48), and smoking history (OR =1.64) as key risk factors. The random forest model incorporated nine predictors (including antibiotic use, viral pneumonia, intensive care unit (ICU) admission, low protein levels, and bacterial infections) and achieved high accuracy [area under the curve (AUC) =0.950, sensitivity =0.857, specificity =0.905]. External validation maintained robust discrimination (AUC =0.856; sensitivity and specificity =0.733). SHAP analysis revealed critical synergistic interactions, particularly between prolonged antibiotic use and viral pneumonia in elevating IPA risk.

Conclusions: This validated ML tool integrates meta-evidence with clinical data for early, accurate IPA risk stratification in immunocompetent patients. It effectively captures nonlinear predictor interactions and demonstrates strong generalizability, supporting clinical utility.

Background: The presence of a small aortic annulus (SAA) in patients undergoing aortic valve replacement (AVR) remains a clinical challenge in cardiac surgery. Continuous suture technique (CST) has been considered to allow implantation of relatively larger prostheses compared with interrupted pledgeted suture (IPS). This study aimed to compare early clinical outcomes of CST versus IPS in patients with SAA who underwent surgical AVR.

Methods: Between 2023 to 2025, 250 patients who received surgical AVR with implanted prostheses size <23 mm at our department were enrolled for retrospective analysis: 99 in the CST group and 151 in the IPS group. Early clinical outcomes were analyzed using propensity score matching, with postoperative peak aortic velocity as the primary endpoint.

Results: The CST group exhibited comparable early clinical outcomes to the IPS group, with no postoperative paravalvular leak observed. Shorter surgery time (P=0.03) and cross-clamp time (P=0.045) were shown in the raw CST group; this advantage also reached a significant difference in the isolated AVR subgroup analysis. Our cohort presented that AVR with CST was associated with significantly lower peak aortic velocity compared with IPS in both raw cohort {2.0 [interquartile range (IQR), 1.7-2.2] vs. 2.2 (IQR, 2.0-2.6) m/s; P<0.001} and propensity-matched analyses (P=0.03). Multivariable linear regression also confirmed CST as an independent predictor of reduced postoperative peak aortic velocity (coefficient -0.192; 95% confidence interval, -0.292 to -0.092; P<0.001).

Conclusions: The treatment with CST in AVR could be a valid and well-promoted alternative strategy for patients with SAA and showed a trend toward better hemodynamic performance compared to the traditional IPS approach.