Regulatory mechanism of FCGR2A in macrophage polarization and its effects on intervertebral disc degeneration.

Intervertebral disc degeneration (IDD) poses a significant health burden, necessitating a deeper understanding of its molecular underpinnings. Transcriptomic analysis reveals 485 differentially expressed genes (DEGs) associated with IDD, underscoring the importance of immune regulation. Weighted gene co-expression network analysis (WGCNA) identifies a yellow module strongly correlated with IDD, intersecting with 197 DEGs. Protein-protein interaction (PPI) analysis identifies ITGAX, MMP9 and FCGR2A as hub genes, predominantly expressed in macrophages. Functional validation through in vitro and in vivo experiments demonstrates the pivotal role of FCGR2A in macrophage polarization and IDD progression. Mechanistically, FCGR2A knockdown suppresses M1 macrophage polarization and NF-κB phosphorylation while enhancing M2 polarization and STAT3 activation, leading to ameliorated IDD in animal models. This study sheds light on the regulatory function of FCGR2A in macrophage polarization, offering novel insights for IDD intervention strategies. KEY POINTS: This study unveils the role of FCGR2A in intervertebral disc (IVD) degeneration (IDD). FCGR2A knockdown mitigates IDD in cellular and animal models. Single-cell RNA-sequencing uncovers diverse macrophage subpopulations in degenerated IVDs. This study reveals the molecular mechanism of FCGR2A in regulating macrophage polarization. This study confirms the role of the NF-κB/STAT3 pathway in regulating macrophage polarization in IDD.

Hybrid transformer convolutional neural network-based radiomics models for osteoporosis screening in routine CT.

OBJECTIVE: Early diagnosis of osteoporosis is crucial to prevent osteoporotic vertebral fracture and complications of spine surgery. We aimed to conduct a hybrid transformer convolutional neural network (HTCNN)-based radiomics model for osteoporosis screening in routine CT. METHODS: To investigate the HTCNN algorithm for vertebrae and trabecular segmentation, 92 training subjects and 45 test subjects were employed. Furthermore, we included 283 vertebral bodies and randomly divided them into the training cohort (n = 204) and test cohort (n = 79) for radiomics analysis. Area receiver operating characteristic curves (AUCs) and decision curve analysis (DCA) were applied to compare the performance and clinical value between radiomics models and Hounsfield Unit (HU) values to detect dual-energy X-ray absorptiometry (DXA) based osteoporosis. RESULTS: HTCNN algorithm revealed high precision for the segmentation of the vertebral body and trabecular compartment. In test sets, the mean dice scores reach 0.968 and 0.961. 12 features from the trabecular compartment and 15 features from the entire vertebral body were used to calculate the radiomics score (rad score). Compared with HU values and trabecular rad-score, the vertebrae rad-score suggested the best efficacy for osteoporosis and non-osteoporosis discrimination (training group: AUC = 0.95, 95%CI 0.91-0.99; test group: AUC = 0.97, 95%CI 0.93-1.00) and the differences were significant in test group according to the DeLong test (p < 0.05). CONCLUSIONS: This retrospective study demonstrated the superiority of the HTCNN-based vertebrae radiomics model for osteoporosis discrimination in routine CT.

LumVertCancNet: A novel 3D lumbar vertebral body cancellous bone location and segmentation method based on hybrid Swin-transformer.

Lumbar vertebral body cancellous bone location and segmentation is crucial in an automated lumbar spine processing pipeline. Accurate and reliable analysis of lumbar spine image is expected to advantage practical medical diagnosis and population-based analysis of bone strength. However, the design of automated algorithms for lumbar spine processing is demanding due to significant anatomical variations and scarcity of publicly available data. In recent years, convolutional neural network (CNN) and vision transformers (Vits) have been the de facto standard in medical image segmentation. Although adept at capturing global features, the inherent bias of locality and weight sharing of CNN constrains its capacity to model long-range dependency. In contrast, Vits excel at long-range dependency modeling, but they may not generalize well with limited datasets due to the lack of inductive biases inherent to CNN. In this paper, we propose a deep learning-based two-stage coarse-to-fine solution to address the problem of automatic location and segmentation of lumbar vertebral body cancellous bone. Specifically, in the first stage, a Swin-transformer based model is applied to predict the heatmap of lumbar vertebral body centroids. Considering the characteristic anatomical structure of lumbar spine, we propose a novel loss function called LumAnatomy loss, which enforces the order and bend of the predicted vertebral body centroids. To inherit the excellence of CNN and Vits while preventing their respective limitations, in the second stage, we propose an encoder-decoder network to segment the identified lumbar vertebral body cancellous bone, which consists of two parallel encoders, i.e., a Swin-transformer encoder and a CNN encoder. To enhance the combination of CNNs and Vits, we propose a novel multi-scale attention feature fusion module (MSA-FFM), which address issues that arise when fusing features given at different encoders. To tackle the issue of lack of data, we raise the first large-scale lumbar vertebral body cancellous bone segmentation dataset called LumVBCanSeg containing a total of 185 CT scans annotated at voxel level by 3 physicians. Extensive experimental results on the LumVBCanSeg dataset demonstrate the proposed algorithm outperform other state-of-the-art medical image segmentation methods. The data is publicly available at: https://zenodo.org/record/8181250. The implementation of the proposed method is available at: https://github.com/sia405yd/LumVertCancNet.