LGDNet: local feature coupling global representations network for pulmonary nodules detection.

Detection of suspicious pulmonary nodules from lung CT scans is a crucial task in computer-aided diagnosis (CAD) systems. In recent years, various deep learning-based approaches have been proposed and demonstrated significant potential for addressing this task. However, existing deep convolutional neural networks exhibit limited long-range dependency capabilities and neglect crucial contextual information, resulting in reduced performance on detecting small-size nodules in CT scans. In this work, we propose a novel end-to-end framework called LGDNet for the detection of suspicious pulmonary nodules in lung CT scans by fusing local features and global representations. To overcome the limited long-range dependency capabilities inherent in convolutional operations, a dual-branch module is designed to integrate the convolutional neural network (CNN) branch that extracts local features with the transformer branch that captures global representations. To further address the issue of misalignment between local features and global representations, an attention gate module is proposed in the up-sampling stage to selectively combine misaligned semantic data from both branches, resulting in more accurate detection of small-size nodules. Our experiments on the large-scale LIDC dataset demonstrate that the proposed LGDNet with the dual-branch module and attention gate module could significantly improve the nodule detection sensitivity by achieving a final competition performance metric (CPM) score of 89.49%, outperforming the state-of-the-art nodule detection methods, indicating its potential for clinical applications in the early diagnosis of lung diseases.

PILN: A posterior information learning network for blind reconstruction of lung CT images.

BACKGROUND AND OBJECTIVE: Computer tomography (CT) imaging technology has played significant roles in the diagnosis and treatment of various lung diseases, but the degradations in CT images usually cause the loss of detailed structural information and interrupt the judgement from clinicians. Therefore, reconstructing noise-free, high-resolution CT images with sharp details from degraded ones is of great importance for the computer-assisted diagnosis (CAD) system. However, current image reconstruction methods suffer from unknown parameters of multiple degradations in actual clinical images. METHODS: To solve these problems, we propose a unified framework, so called Posterior Information Learning Network (PILN), for blind reconstruction of lung CT images. The framework consists of two stages: Firstly, a noise level learning (NLL) network is proposed to quantify the Gaussian and artifact noise degradations into different levels. Inception-residual modules are designed to extract multi-scale deep features from the noisy image, and residual self-attention structures are proposed to refine deep features to essential representations of noise. Secondly, by taking the estimated noise levels as prior information, a cyclic collaborative super-resolution (CyCoSR) network is proposed to iteratively reconstruct the high-resolution CT image and estimate the blur kernel. Two convolutional modules are designed based on cross-attention transformer structure, named as Reconstructor and Parser. The high-resolution image is restored from the degraded image by the Reconstructor under the guidance of the predicted blur kernel, while the blur kernel is estimated by the Parser according to the reconstructed image and the degraded one. The NLL and CyCoSR networks are formulated as an end-to-end framework to handle multiple degradations simultaneously. RESULTS: The proposed PILN is applied to the Cancer Imaging Archive (TCIA) dataset and the Lung Nodule Analysis 2016 Challenge (LUNA16) dataset to evaluate its ability in reconstructing lung CT images. Compared with the state-of-the-art image reconstruction algorithms, it can provide high-resolution images with less noise and sharper details with respect to quantitative benchmarks. CONCLUSIONS: Extensive experimental results demonstrate that our proposed PILN can achieve better performance on blind reconstruction of lung CT images, providing noise-free, detail-sharp and high-resolution images without knowing the parameters of multiple degradation sources.

Inclination of the small laminar slope angle leads to lumbar spinal stenosis due to hypertrophy of the ligamentum flavum.

OBJECTIVE: This study was designed to investigate the relationship between the laminar slope angle (LSA) and the lumbar disc degenerative grade, the cross-section area (CSA) of multifidus muscle, the muscle-fat index, and the thickness of the ligamentum flavum. METHODS: Retrospective analysis of 122 patients who were scheduled to undergo a lumbar operation for diagnoses associated with degenerative lumbar disease between January and December 2017. The L4-L5 disc grade was evaluated from preoperative sagittal T2-weighed magnetic resonance imaging of the lumber region; the CSA of the multifidus and muscle-fat index were measured at the L4 level, while the thickness of the ligamentum flavum was measured at the L4-L5 facet level from axis T2-weighed magnetic resonance imaging. The slope of the laminar was evaluated from preoperative three-dimensional computer tomography at the tip level of the facet joints and selected by the axis plane. Independent-sample T-tests were used to assess the association between age and measurement indices. RESULTS: Our results showed that age was positively connected with the LSA of L4 and L5 in different patients, although there was no significant difference between age and the difference of the two segment LSA. Partial correlation analysis, excluding the interference of age, revealed a strong negative relationship between the LSA of L4 and the thickness of the ligamentum flavum, irrespective of whether we considered the left or right. However, there was no correlation with lumbar disc degenerative grade, the CSA of the multifidus, and the muscle-fat index. CONCLUSION: The thickness of the ligamentum flavum showed changes with anatomical differences in the LSA, but not the lumbar disc degenerative grade, the CSA of the multifidus, and the muscle-fat index. A small change in LSA may cause large mechanical stress; this may be one of the causative factors responsible for lumbar spinal stenosis.

Circular RNA Expression Profile in Patients with Lumbar Spinal Stenosis Associated with Hypertrophied Ligamentum Flavum.

STUDY DESIGN: Sequencing and experimental analysis of the expression profile of circular RNAs (circRNAs) in hypertrophic ligamentum flavum (LFH). OBJECTIVES: The aim of this study was to identify differentially expressed circRNAs between LFH and nonhypertrophic ligamentum flavum tissues from lumbar spinal stenosis (LSS) patients. SUMMARY OF BACKGROUND DATA: Hypertrophy of the ligamentum flavum (LF) can cause LSS. circRNAs are important in various diseases. However, no circRNA expression patterns related to LF hypertrophy have been reported. METHODS: A total of 33 patients with LSS participated in this study. LF tissue samples were obtained when patients underwent decompressive laminectomy during surgery. The expression profile of circRNAs was analyzed by transcriptome high-throughput sequencing and validated with quantitative real-time polymerase chain reaction (PCR). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed for the differentially expressed circRNA-associated genes and related pathways. The connections between circRNAs and microRNAs were explored using Cytoscape. The role of hsa_circ_0052318 on LF cell fibrosis was assessed by analyzing the expression of collagen I and collagen III. RESULTS: The results showed that 2439 circRNAs of 4025 were differentially expressed between the LFH and nonhypertrophic ligamentum flavum tissues, including 1276 upregulated and 1163 downregulated circRNAs. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that these differentially expressed circRNAs functioned in biological processes, cellular components, and molecular functions. Autophagy and mammalian target of rapamycin were the top two signaling pathways affected by these circRNAs. Five circRNAs (hsa_circ_0021604, hsa_circ_0025489, hsa_circ_0002599, hsa_circ_0052318, and hsa_circ_0003609) were confirmed by quantitative real-time PCR. The network indicated a strong relationship between circRNAs and miRNAs. Furthermore, hsa_circ_0052318 overexpression decreased mRNA and protein expression of collagen I and III in LF cells from LFH tissues. CONCLUSION: This study identified circRNA expression profiles characteristic of hypertrophied LF in LSS patients, and demonstrated that hsa_circ_0052318 may play an important role in the pathogenesis of LF hypertrophy.Level of Evidence: N/A.

A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks.

Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the "suspicious nodules" in the image. The dilated convolution can increase the receptive fields to improve the ability of the network in learning global information of the image. Thirdly, a modified U-net adapting multi-scale pooling and multi-resolution convolution connection is proposed to find the true pulmonary nodule in the image with multiple candidate regions. During the detection, the result of the former step is used as the input of the latter step to follow the "coarse-to-fine" detection process. Moreover, the focal loss, perceptual loss and dice loss were used together to replace the cross-entropy loss to solve the problem of imbalance distribution of positive and negative samples. We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection. Experimental results illustrate that the proposed method outperform the state-of-the-art methods with respect to accuracy, sensitivity and specificity.

Robot-assisted orthopedic surgery in the treatment of adult degenerative scoliosis: a preliminary clinical report.

STUDY DESIGN: A single-institution, retrospective cohort study. OBJECTIVE: To compare the accuracy and short-term clinical outcomes of pedicle screw placement between robot-assisted (RA) and freehand (FH) technique in the treatment of adult degenerative scoliosis (ADS). METHODS: From February 2018 to October 2019, 97 adult patients with degenerative scoliosis admitted to our department were retrospectively reviewed. Thirty-one patients received robot-assisted pedicle screw placement (RA group), and 66 patients underwent freehand pedicle screw placement (FH group). Patient demographics and short-term clinical outcomes were recorded and compared between two groups. Gertzbein-Robbins grading system was adopted to evaluate the accuracy of pedicle screw placement by means of postoperative CT scan. Short-term clinical outcomes consist of operative time, intraoperative blood loss, length of hospital stay (LOS), radiological parameters, Scoliosis Research Society-22 (SRS-22) scores before the operation, 6 months after operation, adverse events, and revisions. RESULTS: The accuracy of screw placement was higher than that of the FH group (clinically acceptable 98.7% vs. 92.2%; P< 0.001). Intraoperative blood loss of the RA group was less than those in the FH group (499 vs. 573 ml; P < 0.001). Operative time (283.1 vs. 291.9 min; P = 0.31) and length of stay (12.8 vs. 13.7 days; P = 0.36) were compared between RA and FH groups. In terms of radiological parameters, both of groups were improved postoperatively. The SRS-22 scores at 6 months after operation from both groups were better than those before operation. For surgery-related complication, one case had pressure sores in the RA group while two cases developed dural tears in the FH group. No revision was required in both groups. CONCLUSION: Combined with other surgical correction modalities, robot-assisted pedicle screw fixation is an effective and safe method of treating degenerative scoliosis. Due to its satisfactory surgical outcomes such as higher accuracy and less trauma, it provides a good alternative for clinical practice. LEVEL OF EVIDENCE: 3.

Computed Tomography (CT) Image Quality Enhancement via a Uniform Framework Integrating Noise Estimation and Super-Resolution Networks.

Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image quality, resulting in unreliable performance of the post-processing steps in the computer assisted diagnosis system (CADs), such as medical image segmentation, feature extraction, and medical image classification. Since the degradation of medical images typically appears as noise and low-resolution blurring, in this paper, we propose a uniform deep convolutional neural network (DCNN) framework to handle the de-noising and super-resolution of the CT image at the same time. The framework consists of two steps: Firstly, a dense-inception network integrating an inception structure and dense skip connection is proposed to estimate the noise level. The inception structure is used to extract the noise and blurring features with respect to multiple receptive fields, while the dense skip connection can reuse those extracted features and transfer them across the network. Secondly, a modified residual-dense network combined with joint loss is proposed to reconstruct the high-resolution image with low noise. The inception block is applied on each skip connection of the dense-residual network so that the structure features of the image are transferred through the network more than the noise and blurring features. Moreover, both the perceptual loss and the mean square error (MSE) loss are used to restrain the network, leading to better performance in the reconstruction of image edges and details. Our proposed network integrates the degradation estimation, noise removal, and image super-resolution in one uniform framework to enhance medical image quality. We apply our method to the Cancer Imaging Archive (TCIA) public dataset to evaluate its ability in medical image quality enhancement. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on de-noising and super-resolution by providing higher peak signal to noise ratio (PSNR) and structure similarity index (SSIM) values.

LncRNA SNHG4 promotes tumour growth by sponging miR-224-3p and predicts poor survival and recurrence in human osteosarcoma.

OBJECTIVE: Accumulating data show that dysregulation of long noncoding RNAs (lncRNAs) acts a critical role in a variety of malignancies. Among these lncRNAs, small nucleolar RNA host genes (SNHGs) are associated with tumour growth and progression. But, the molecular mechanisms by which SNHG4 contributes to osteosarcoma remain undocumented. METHODS: The association between lncRNA SNHG4 expression and clinicopathologic characteristics and prognosis in patients with osteosarcoma was analysed by TCGA RNA-sequencing data. Cell viability and colony formation abilities were respectively assessed by MTT and colony formation assays. LncRNA SNHG4-specific binding with miR-224-3p was verified by bioinformatic analysis, luciferase gene report, and RNA immunoprecipitation assays. Regulation relationship between SNHG4 and miR-224-3p expression was further evaluated by the rescue experiments. RESULTS: The expression level of lncRNA SNHG4 was significantly elevated in osteosarcoma samples and cell lines as compared with the adjacent normal tissues, and SNHG4 high expression was associated with tumour size (TS) and poor prognosis in patients with osteosarcoma. Knockdown of SNHG4 suppressed cell viability and invasive potential, whereas ectopic SNHG4 expression displayed the opposite effects. Moreover, we found that lncRNA SNHG4 acted as a sponge of miR-224-3p, and miR-224-3p mimic reversed SNHG4 induced tumour-promoting effects in osteosarcoma cells. The expression of miR-224-3p depicted a negative correlation with SNHG4 in osteosarcoma samples and miR-224-3p low expression was associated with TS and poor survival in patients with osteosarcoma. CONCLUSION: Our findings demonstrated that LncRNA SNHG4 promoted tumour growth by sponging miR-224-3p and represented a poor prognostic factor in patients with osteosarcoma.

miR-149-5p inhibits cell growth by regulating TWEAK/Fn14/PI3K/AKT pathway and predicts favorable survival in human osteosarcoma.

MicroRNAs (miRNAs) as small non-coding RNAs act as either tumor suppressors or oncogenes in human cancers, of which miR-149-5p (miR-149) is involved in tumor growth and metastasis, but its role and molecular mechanisms underlying osteosarcoma growth are poorly understood. The correlation of miR-149 expression with clinicopathological characteristics and prognosis in patients with sarcoma was analyzed by The Cancer Genome Atlas (TCGA) RNA-sequencing data. Osteosarcoma cell growth affected by miR-149 was evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and colony formation assays. As a result, we found that the expression level of miR-149 was markedly downregulated in human sarcoma samples and were negatively associated with tumor size, acting as an independent prognostic factor for overall survival of the sarcoma patients. Restoration of miR-149 expression suppressed osteosarcoma cell growth, while its knockdown reversed these effects. Furthermore, we identified TNFRSF12A (TNF receptor superfamily member 12A), also called fibroblast growth factor-inducible 14 (Fn14) as a direct target of miR-149, and TNFRSF12A and its ligand TNFSF12 (TNF superfamily member 12), also called tumor necrosis factor-related weak inducer of apoptosis (TWEAK), were both negatively correlated with miR-149 expression in sarcoma samples. Knockdown of TNFRSF12A suppressed cell growth, but its overexpression weakened the antiproliferative effects of miR-149 via the PI3K/AKT (AKT serine/threonine kinase) signaling pathway. Altogether, our findings show that miR-149 functions as a tumor suppressor in osteosarcoma via inhibition of the TWEAK-Fn14 axis and represents a potential therapeutic target in patients with osteosarcoma.