Identification of early-stage pulmonary adenocarcinomas before surgery, especially in cases of subcentimeter cancers, would be clinically important and could provide guidance to clinical decision making. In this study, we developed a deep learning system based on 3D convolutional neural networks and multitask learning, which automatically predicts tumor invasiveness, together with 3D nodule segmentation masks. The system processes a 3D nodule-centered patch of preprocessed CT and learns a deep representation of a given nodule without the need for any additional information. A dataset of 651 nodules with manually segmented voxel-wise masks and pathological labels of atypical adenomatous hyperplasia (AAH), adenocarcinomas in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA) was used in this study. We trained and validated our deep learning system on 523 nodules and tested its performance on 128 nodules. An observer study with 2 groups of radio-logists, 2 senior and 2 junior, was also investigated. We merged AAH and AIS into one single category AAH-AIS, comprising a 3-category classification in our study. The proposed deep learning system achieved better classification performance than the radiologists; in terms of 3-class weighted average F1 score, the model achieved 63.3% while the radiologists achieved 55.6%, 56.6%, 54.3%, and 51.0%, respectively. These results suggest that deep learning methods improve the yield of discriminative results and hold promise in the CADx application domain, which could help doctors work efficiently and facilitate the application of precision medicine.Significance: Machine learning tools are beginning to be implemented for clinical applications. This study represents an important milestone for this emerging technology, which could improve therapy selection for patients with lung cancer.

Objectives To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed. Results Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model. Conclusions The proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs. Key Points • CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.

Abdominal aortic aneurysm (AAA) is accepted as a chronic vascular inflammatory disease. However, how the inflammatory response is regulated during AAA formation is not fully understood. This study was undertaken to determine whether the long noncoding RNA (lncRNA) H19 (H19) promotes AAA formation by enhancing aortic inflammation. qRT-PCR detected the upregulation of H19 in human and mouse AAA tissue samples. Co-staining for H19 and the macrophage marker MAC-2 showed that H19 was located in vascular smooth muscle cells (VSMCs) and infiltrating aortic macrophages. In vivo overexpression of H19 increased vascular inflammation and induced AAA formation, which was supported by exacerbated aortic morphology, maximum aortic diameter values, elastin degradation, expression of interleukin-6 (IL-6) and macrophage chemoattractant protein-1 (MCP-1), and macrophage infiltration. H19 suppression resulted in the opposite effects. A rescue experiment indicated that IL-6 neutralization significantly mitigated the aortic inflammation and AAA formation evoked by H19 overexpression. Luciferase reporter assays and ex vivo experiments using VSMCs and macrophages confirmed that H19 induced aneurysm formation in part via endogenous competition with the let-7a microRNA to induce the transcription of its target gene, IL-6. This mechanism was further validated by in vivo experiments using a mutant H19 that could not effectively bind let-7a. Collectively, our study revealed a pathogenic H19/let-7a/IL-6 inflammatory pathway in AAA formation, which offers a new potential therapeutic strategy for AAA.

Cyclic RGD-modified MBs targeted to GP IIb/IIIa with CEU are capable of detecting inflammation-activated platelets and thrombosis in large arteries, thus providing a potential tool for identification of vulnerable atherosclerotic plaques.

In the present study, multi-slice CT results of patients with Behçet's disease (BD) and vascular complications were retrospectively evaluated. From January 2016 to May 2018, 45 of 361 patients with BD were diagnosed with vascular involvement. The clinical background, laboratory parameters and response to therapy of those patients were assessed. The following characteristics of vascular aneurysms were analyzed: Maximum diameter, length, wall thickness, borders, luminal changes, mural thrombus, cystic change of the vessel walls, asymmetric bulging of the right part of the aortic wall (RP type) and calcific plaques. The 45 BD patients analyzed included 37 males and 8 females with a median age of 40 years (30–49 years). Significant differences were observed among genders regarding age, ocular disorders and digestive-tract ulceration. A total of 42 aneurysms were identified with a mean diameter of 43 mm. Most aneurysmal walls (88%) were homogeneously enhanced on contrast-enhanced CT. Comparison of groups classified by aortic and larger arterial aneurysms indicated that aneurysms occurring in the aorta were more likely to form a mural thrombus, have a thicker wall (P<0.001) and unclear borders (P=0.036), to be of the RP type (P=0.003) and have a longer extension (P=0.001) compared with those in larger arteries. Unclear border of the aneurysmal wall was the only radiologic predictor correlated with an elevated erythrocyte sedimentation rate (P<0.001). In conclusion, characteristic CT imaging features of aneurysms may help to diagnose vascular involvement of BD and assess its severity, particularly in the absence of the classical clinical manifestations.

Background Magnetic targeting may help microbubbles (MBs) reach obstructive thrombi and improve the efficacy of MB-mediated sonothrombolysis, but the role of magnetic targeting in MB-mediated sonothrombolysis remains elusive. Objectives We investigate the feasibility and efficacy of magnetically targeted MB-mediated sonothrombolysis for the treatment of obstructive thrombi. Materials and Methods Red and white thromboembolic models were established in vitro and in vivo. The models were randomly assigned to the control, ultrasound plus control MB (US + C-MB), ultrasound plus magnetic MB (US + M-MB), or US + M-MB + recombinant tissue-type plasminogen activator (r-tPA) groups and treated for 30 minutes. The recanalization rate, average blood flow velocity, hindlimb perfusion, and skeletal muscle injury marker levels were recorded. Results The recanalization rate, average blood flow velocity, and hindlimb perfusion in the red and white thromboembolic models were all significantly higher in the US + M-MB and US + M-MB + r-tPA groups than in the control and US + C-MB groups both in vitro and in vivo. Moreover, the levels of the skeletal muscle injury markers were all significantly lower in the US + M-MB and US + M-MB + r-tPA groups than in the other two groups in vivo for both thromboembolic models. However, the thrombolytic effects of red thrombi performed better than those of white thrombi in the US + M-MB + r-tPA group. Conclusion M-MB-mediated sonothrombolysis improves the efficacy of thrombolysis both in vitro and in vivo, and reduces tissue damage in clogging model; thus, this method may serve as a promising approach for treating thrombus-occlusive diseases.

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MBM may be a better molecular probe than MB for detection of P-selectin on aorta with CEU, likely due to the shifting of axial distribution. Thus, it may improve the detection of the inflammatory profile on large arteries by UMI.