Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics.

OBJECTIVES: Musculoskeletal (MSK) tumors, given their high mortality rate and heterogeneity, necessitate precise examination and diagnosis to guide clinical treatment effectively. Magnetic resonance imaging (MRI) is pivotal in detecting MSK tumors, as it offers exceptional image contrast between bone and soft tissue. This study aims to enhance the speed of detection and the diagnostic accuracy of MSK tumors through automated segmentation and grading utilizing MRI. MATERIALS AND METHODS: The research included 170 patients (mean age, 58 years ±12 (standard deviation), 84 men) with MSK lesions, who underwent MRI scans from April 2021 to May 2023. We proposed a deep learning (DL) segmentation model MSAPN based on multi-scale attention and pixel-level reconstruction, and compared it with existing algorithms. Using MSAPN-segmented lesions to extract their radiomic features for the benign and malignant classification of tumors. RESULTS: Compared to the most advanced segmentation algorithms, MSAPN demonstrates better performance. The Dice similarity coefficients (DSC) are 0.871 and 0.815 in the testing set and independent validation set, respectively. The radiomics model for classifying benign and malignant lesions achieves an accuracy of 0.890. Moreover, there is no statistically significant difference between the radiomics model based on manual segmentation and MSAPN segmentation. CONCLUSION: This research contributes to the advancement of MSK tumor diagnosis through automated segmentation and predictive classification. The integration of DL algorithms and radiomics shows promising results, and the visualization analysis of feature maps enhances clinical interpretability.

Coronary vessel segmentation in coronary angiography with a multi-scale U-shaped transformer incorporating boundary aggregation and topology preservation.

Coronary vessel segmentation plays a pivotal role in automating the auxiliary diagnosis of coronary heart disease. The continuity and boundary accuracy of the segmented vessels directly affect the subsequent processing. Notably, during segmentation, vessels with severe stenosis can easily cause boundary errors and breakage, resulting in isolated islands. To address these issues, we propose a novel multi-scale U-shaped transformer with boundary aggregation and topology preservation (UT-BTNet) for coronary vessel segmentation in coronary angiography. Specifically, considering the characteristics of coronary vessels, we first develop the UT-BTNet for coronary vessels segmentation, which combines the advantages of a convolutional neural networks (CNN) and a transformer, and is able to effectively extract the local and global features of angiographic images. Secondly, we innovatively employ boundary loss and topological loss in two stages, in addition to the traditional losses. In the first stage, boundary loss is adopted, which has the effect of boundary aggregation. In the second stage, topological loss is applied to preserve the topology of the vessels, after the network converges. In the experiment, in addition to the two metrics of Dice and intersection over union (IoU), we specifically propose two metrics of boundary intersection over union (BIoU) and Betti error to evaluate boundary accuracy and the continuity of segmentation results. The results show that the Dice is 0.9291, the IoU is 0.8687, the BIoU is 0.5094, and the Betti error is 0.3400. Compared with the other state-of-the-art methods, UT-BTNet achieves better segmentation results, while ensuring the continuity and boundary accuracy of the vessels, indicating its potential clinical value.

A framework of myocardial bridge detection with x-ray angiography sequence.

BACKGROUND: Myocardial bridges are congenital anatomical abnormalities in which myocardium covers a segment of coronary arteries, leading to stenocardia, myocardial ischemia, and sudden cardiac death in severe cases. However, automatic diagnosis of myocardial bridge presents significant challenges. METHOD: A novel framework of myocardial bridge detection with x-ray angiography sequence is proposed, which can realize automatic detection of vessel stenosis and myocardial bridge. Firstly, we employ a novel neural network model for coronary vessel segmentation, which consists of both CNNs and transformer structures to effectively extract both local and global information of the vessels. Secondly, we describe the vessel segment information, establish the vessel tree in the image, and fuse the vessel tree information between sequences. Finally, based on vessel stenosis detection, we realize automatic detection of the myocardial bridge by querying the blood vessels between the image sequence information. RESULTS: In experiment, we evaluate the segmentation results using two metrics, Dice and ASD, and achieve scores of 0.917 and 1.39, respectively. In the stenosis detection, we achieve an average accuracy rate of 92.7% in stenosis detection among 262 stenoses. In multi-frame image processing, vessels in different frames can be well-matched, and the accuracy of myocardial bridge detection achieves 75%. CONCLUSIONS: Our experimental results demonstrate that the algorithm can automatically detect stenosis and myocardial bridge, providing a new idea for subsequent automatic diagnosis of coronary vessels.

LN-Net: Perfusion Pattern-Guided Deep Learning for Lymph Node Metastasis Diagnosis Based on Contrast-Enhanced Ultrasound Videos.

OBJECTIVE: The blood flow in lymph nodes reflects important pathological features. However, most intelligent diagnosis based on contrast-enhanced ultrasound (CEUS) video focuses only on CEUS images, ignoring the process of extracting blood flow information. In the work described here, a parametric imaging method for describing blood perfusion pattern was proposed and a multimodal network (LN-Net) to predict lymph node metastasis was designed. METHODS: First, the commercially available artificial intelligence object detection model YOLOv5 was improved to detect the lymph node region. Then the correlation and inflection point matching algorithms were combined to calculate the parameters of the perfusion pattern. Finally, the Inception-V3 architecture was used to extract the image features of each modality, with the blood perfusion pattern taken as the guiding factor in fusing the features with CEUS by sub-network weighting. DISCUSSION: The average precision of the improved YOLOv5s algorithm compared with baseline was improved by 5.8%. LN-Net predicted lymph node metastasis with 84.9% accuracy, 83.7% precision and 80.3% recall. Compared with the model without blood flow feature guidance, accuracy was improved by 2.6%. The intelligent diagnosis method has good clinical interpretability. CONCLUSION: A static parametric imaging map could describe a dynamic blood flow perfusion pattern, and as a guiding factor, it could improve the classification ability of the model with respect to lymph node metastasis.

Molecular dynamics study on the behavior and binding mechanism of target protein Transgelin-2 with its agonist TSG12 for anti-asthma drug discovery.

Transgelin-2 (TG2) is a novel promising therapeutic target for the treatment of asthma as it plays an important role in relaxing airway smooth muscles and reducing pulmonary resistance in asthma. The compound TSG12 is the only reported TG2 agonist with in vivo anti-asthma activity. However, the dynamic behavior and ligand binding sites of TG2 and its binding mechanism with TSG12 remain unclear. In this study, we performed 12.6 µs molecular dynamics (MD) simulations for apo-TG2 and TG2-TSG12 complex, respectively. The results suggested that the apo-TG2 has 4 most populated conformations, and that its binding of the agonist could expand the conformation distribution space of the protein. The simulations revealed 3 potential binding sites in 3 most populated conformations, one of which is induced by the agonist binding. Free energy decomposition uncovered 8 important residues with contributions stronger than -1 kcal/mol. Computational alanine scanning for the important residues by 100 ns conventional MD simulation for each mutated TG2-TSG12 complexes demonstrated that E27, R49 and F52 are essential residues for the agonist binding. These results should be helpful to understand the dynamic behavior of TG2 and its binding mechanism with the agonist TSG12, which could provide some structural insights into the novel mechanism for anti-asthma drug development.

Establishment and Evaluation of a Rat Model of Medial Malleolar Fracture with Vascular Injury.

OBJECTIVE: A stable animal model was needed to study bone non-union caused by insufficient blood supply, the main object of this paper is to develop a medial malleolar fracture model with controllable arterial vascular injuries in rats for revealing the biochemical mechanism of non-union by insufficient blood supply. METHODS: A total of 18 rats were randomly divided into three equal groups: the Sham group, the Fracture group, and the Fracture + Vascular group. The animals were subjected to unilateral medial malleolar bone fracture and vascular injury using customized molding equipment. The fracture site was scanned by micro-CT, and vascular injury was evaluated by laser Doppler flowmetry (LDF) 24 h after modeling. Histological examination (HE), alkaline phosphatase (ALP) and tartrate-resistant acid phosphatase (TRAP) staining, immunohistochemistry and immunofluorescence were conducted on the medial malleolar fracture tissues of three rats randomly selected from each group 24 h after modeling. Subsequently, to further confirm the success of fracture modeling, the fracture sites of three other rats in each group underwent micro-CT scanning again 6 weeks after surgery. RESULTS: The results of a 24 h micro-CT showed that all rats used to create the fracture models showed controlled injury of the medial malleolus. The model was stable, and the satisfaction of the homemade equipment agreed with the expectation. LDF showed that the blood flow of rats in the Fracture + Vascular group decreased significantly 24 h after fracture injury, while collateral blood flow perfusion increased by 50% on average. The results of HE, ALP and TRAP staining in the medial malleolus showed that the number of osteoblasts (OBs) and osteoclasts (OCs) in the Fracture group increased significantly, but the number of OBs and OCs in the Fracture + Vascular group decreased sharply relative to the number in the Sham group 24 h later. Furthermore, immunohistochemistry and immunofluorescence results showed that the number of neovessels in the Fracture group was significantly increased, while the number of neovessels in the Fracture + Vascular group was significantly decreased, which was consistent with the above results. After 6 weeks of modeling, the micro-CT results showed that the fractures in the Fracture group had healed substantially, while those in the Fracture + Vascular group had not. CONCLUSION: This study provided a reproducible and stable experimental animal model for medial malleolar fractures with arterial injury.