Damage Evolution Mechanism of Railway Wagon Bogie Adapter 1035 Steel and Damage Parameter Calibration Based on Gursone-Tvergaarde-Needleman Model.

As a critical component of a train, the railway wagon bogie adapter has higher quality requirements. During the forging process, external loads can induce voids, cracks, and other defects in the forging, thereby reducing its service life. Hence, studying the damage behavior of the forging material, specifically AISI 1035 steel, becomes crucial. This study involved obtaining stress-strain curves for AISI 1035 steel through uniaxial tensile tests at temperatures of 900 °C, 1000 °C, and 1100 °C, with strain rates of 0.1 s-1, 1 s-1, and 10 s-1. Subsequently, SEM was used to observe samples at various deformation stages. The damage parameters, q1, q2 and q3 in the GTN model "a computational model used to analyze and simulate material damage which can effectively capture the damage behavior of materials under different loading conditions" were then calibrated using the Ramberg-Osgood model and stress-strain curve fitting. Image Pro Plus software v11.1 quantified the sample porosity as f0, fn, fc and fF. A finite element model was established to simulate the tensile behavior of the AISI 1035 steel samples. By comparing the damage parameters of f0, fn, fc and fF obtained by the finite element method and experimental method, the validity of the damage parameters obtained by the finite element inverse method could be verified.

A 3D hierarchical cross-modality interaction network using transformers and convolutions for brain glioma segmentation in MR images.

BACKGROUND: Precise glioma segmentation from multi-parametric magnetic resonance (MR) images is essential for brain glioma diagnosis. However, due to the indistinct boundaries between tumor sub-regions and the heterogeneous appearances of gliomas in volumetric MR scans, designing a reliable and automated glioma segmentation method is still challenging. Although existing 3D Transformer-based or convolution-based segmentation networks have obtained promising results via multi-modal feature fusion strategies or contextual learning methods, they widely lack the capability of hierarchical interactions between different modalities and cannot effectively learn comprehensive feature representations related to all glioma sub-regions. PURPOSE: To overcome these problems, in this paper, we propose a 3D hierarchical cross-modality interaction network (HCMINet) using Transformers and convolutions for accurate multi-modal glioma segmentation, which leverages an effective hierarchical cross-modality interaction strategy to sufficiently learn modality-specific and modality-shared knowledge correlated to glioma sub-region segmentation from multi-parametric MR images. METHODS: In the HCMINet, we first design a hierarchical cross-modality interaction Transformer (HCMITrans) encoder to hierarchically encode and fuse heterogeneous multi-modal features by Transformer-based intra-modal embeddings and inter-modal interactions in multiple encoding stages, which effectively captures complex cross-modality correlations while modeling global contexts. Then, we collaborate an HCMITrans encoder with a modality-shared convolutional encoder to construct the dual-encoder architecture in the encoding stage, which can learn the abundant contextual information from global and local perspectives. Finally, in the decoding stage, we present a progressive hybrid context fusion (PHCF) decoder to progressively fuse local and global features extracted by the dual-encoder architecture, which utilizes the local-global context fusion (LGCF) module to efficiently alleviate the contextual discrepancy among the decoding features. RESULTS: Extensive experiments are conducted on two public and competitive glioma benchmark datasets, including the BraTS2020 dataset with 494 patients and the BraTS2021 dataset with 1251 patients. Results show that our proposed method outperforms existing Transformer-based and CNN-based methods using other multi-modal fusion strategies in our experiments. Specifically, the proposed HCMINet achieves state-of-the-art mean DSC values of 85.33% and 91.09% on the BraTS2020 online validation dataset and the BraTS2021 local testing dataset, respectively. CONCLUSIONS: Our proposed method can accurately and automatically segment glioma regions from multi-parametric MR images, which is beneficial for the quantitative analysis of brain gliomas and helpful for reducing the annotation burden of neuroradiologists.

Prostate lesion segmentation based on a 3D end-to-end convolution neural network with deep multi-scale attention.

Prostate cancer is one of the deadest cancers among human beings. To better diagnose the prostate cancer, prostate lesion segmentation becomes a very important work, but its progress is very slow due to the prostate lesions small in size, irregular in shape, and blurred in contour. Therefore, automatic prostate lesion segmentation from mp-MRI is a great significant work and a challenging task. However, the most existing multi-step segmentation methods based on voxel-level classification are time-consuming, may introduce errors in different steps and lead to error accumulation. To decrease the computation time, harness richer 3D spatial features, and fuse the multi-level contextual information of mp-MRI, we present an automatic segmentation method in which all steps are optimized conjointly as one step to form our end-to-end convolutional neural network. The proposed end-to-end network DMSA-V-Net consists of two parts: (1) a 3D V-Net is used as the backbone network, it is the first attempt in employing 3D convolutional neural network for CS prostate lesion segmentation, (2) a deep multi-scale attention mechanism is introduced into the 3D V-Net which can highly focus on the ROI while suppressing the redundant background. As a merit, the attention can adaptively re-align the context information between the feature maps at different scales and the saliency maps in high-levels. We performed experiments based on five cross-fold validation with data including 97 patients. The results show that the Dice and sensitivity are 0.7014 and 0.8652 respectively, which demonstrates that our segmentation approach is more significant and accurate compared to other methods.

Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour.

PURPOSE: Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learning methods. METHODS: The proposed method first constructs a slice-indexed-histogram to localize the volume of interest (VOI) and estimate the probability that a voxel belongs to the liver according its intensity. The probabilistic image would be used to initialize the 3D PAC model. Secondly, a new contour indicator function, which is a component of the model, is produced by combining the gradient-based edge detection and Hessian-matrix-based surface detection. Then, a fast numerical scheme derived for the 3D PAC model is performed to evolve the initial probabilistic image into the global minimizer of the model, which is a smoothed probabilistic image showing a distinctly highlighted liver. Next, a simple region-growing strategy is applied to extract the whole liver mask from the smoothed probabilistic image. Finally, a B-spline surface is constructed to fit the patch of the rib cage to prevent possible leakage into adjacent intercostal tissues. RESULTS: The proposed method is evaluated on two public datasets. The average Dice score, volume overlap error, volume difference, symmetric surface distance and volume processing time are 0.96, 7.35%, 0.02%, 1.17 mm and 19.8 s for the Sliver07 dataset, and 0.95, 8.89%, - 0.02 % $-0.02\%$ , 1.45 mm and 23.08 s for the 3Dircadb dataset, respectively. CONCLUSIONS: The proposed fully-automatic approach can effectively segment the liver from low-contrast and complex backgrounds. The quantitative and qualitative results demonstrate that the proposed segmentation method outperforms state-of-the-art traditional automatic liver segmentation algorithms and achieves very competitive performance compared with recent deep leaning-based methods.

The Prognostic Value of Periostin Expression in Non-Small Cell Lung Cancer: A Meta-Analysis.

BACKGROUND Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related death in the world and its poor prognosis is a major concern. Periostin was found to be associated with the prognosis of NSCLC. However, the research results were inconsistent. This meta-analysis evaluated the correlation between periostin expression and the prognosis of NSCLC. MATERIAL AND METHODS A meta-analysis was performed on data acquired from PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang Database from inception to 18 June 2022. Published and unpublished studies investigating the correlation between periostin expression and the prognosis of NSCLC were included in this meta-analysis. Eligible studies reported at least 1 of the following clinical outcome measures: overall survival, progression-free survival, cancer-specific survival, relapse-free survival, disease-free survival, or other clinical parameters of prognosis. Pooled hazard ratios (HR) with 95% confidence interval (CI) were calculated using the random-effects model. Sensitivity and subgroup analyses and assessment of publication bias were also conducted. RESULTS This meta-analysis enrolled 2504 NSCLC cases from 12 eligible studies. The hazard ratio for the overall survival was 1.761 (95% CI: 1.022-3.033, P=0.041). Heterogeneity was significant among the studies, but publication bias was lacking. Subgroup analyses were performed based on different issues, such as districts, antibodies and methods for periostin detection. CONCLUSIONS Overexpression of periostin is a negative prognostic factor and is associated with worse overall survival (OS) in NSCLC patients. Periostin may serve as a prognostic biomarker for NSCLC patients.

Therapeutic efficacy of the novel SHP2 degrader SHP2-D26, alone or in combination, against lung cancer is associated with modulation of p70S6K/S6, Bim and Mcl-1.

SHP2, a protein tyrosine phosphatase, plays a critical role in fully activating oncogenic signaling pathways such as Ras/MAPK downstream of cell surface tyrosine receptors (e.g., EGFR), which are often activated in human cancers, and thus has emerged as an attractive cancer therapeutic target. This study focused on evaluating the therapeutic potential of the novel SHP2 degrader, SHP2-D26 (D26), either alone or in combination, against non-small cell lung cancer (NSCLC) cells. While all tested NSCLC cell lines responded to D26 with IC50s of < 8 µM, a few cell lines (4/14) were much more sensitive than others with IC50s of ≤ 4 µM. There was no clear association between basal levels of SHP2 and cell sensitivities to D26. Moreover, D26 rapidly and potently decreased SHP2 levels in different NSCLC cell lines in a sustained way regardless of cell sensitivities to D26, suggesting that additional factors may impact cell response to D26. We noted that suppression of p70S6K/S6, but not ERK1/2, was associated with cell responses to D26. In the sensitive cell lines, D26 effectively increased Bim levels while decreasing Mcl-1 levels accompanied with the induction of apoptosis. When combined with the third generation EGFR inhibitor, osimertinib (AZD9291), synergistic effects on decreasing the survival of different osimertinib-resistant cell lines were observed with enhanced induction of apoptosis. Although D26 alone exerted moderate inhibition of the growth of NSCLC xenografts, the combination of osimertinib and D26 effectively inhibited the growth of osimertinib-resistant xenografts, suggesting promising efficacy in overcoming acquired resistance to osimertinib.

The novel BET degrader, QCA570, is highly active against the growth of human NSCLC cells and synergizes with osimertinib in suppressing osimertinib-resistant EGFR-mutant NSCLC cells.

Lung cancer remains the leading cause of cancer deaths worldwide despite advances in knowledge in cancer biology and options of various targeted therapies. Efforts in identifying innovative and effective therapies are still highly appreciated. Targeting bromodomain and extra terminal (BET) proteins that function as epigenetic readers and master transcription coactivators is now a potential cancer therapeutic strategy. The current study evaluates the therapeutic efficacies of the novel BET degrader, QCA570, in lung cancer and explores its underlying mechanisms. QCA570 at low nanomolar ranges effectively decreased the survival of a panel of human lung cancer cell lines with induction of apoptosis in vitro. As expected, it potently induced degradation of BET proteins including BRD4, BRD3 and BRD2. Moreover, it potently decreased Mcl-1 levels due to transcriptional suppression and protein degradation; this event is critical for mediating apoptosis induced by QCA570. Moreover, QCA570 synergized with osimertinib in suppressing the growth of osimertinib-resistant cells in vitro and in vivo, suggesting potential in overcoming acquired resistance to osimertinib. These preclinical findings support the potential of QCA570 in treatment of lung cancer either as a single agent or in combination with others.

Overcoming acquired resistance to third-generation EGFR inhibitors by targeting activation of intrinsic apoptotic pathway through Mcl-1 inhibition, Bax activation, or both.

Treatment of EGFR-mutant non-small cell lung cancer (NSCLC) with mutation-selective third-generation EGFR-tyrosine kinase inhibitors (EGFR-TKIs) such as osimertinib has achieved remarkable success in the clinic. However, the immediate challenge is the emergence of acquired resistance, limiting the long-term remission of patients. This study suggests a novel strategy to overcome acquired resistance to osimertinib and other third-generation EGFR-TKIs through directly targeting the intrinsic apoptotic pathway. We found that osimertinib, when combined with Mcl-1 inhibition or Bax activation, synergistically decreased the survival of different osimertinib-resistant cell lines, enhanced the induction of intrinsic apoptosis, and inhibited the growth of osimertinib-resistant tumor in vivo. Interestingly, the triple-combination of osimertinib with Mcl-1 inhibition and Bax activation exhibited the most potent activity in decreasing the survival and inducing apoptosis of osimertinib-resistant cells and in suppressing the growth of osimertinib-resistant tumors. These effects were associated with increased activation of the intrinsic apoptotic pathway evidenced by augmented mitochondrial cytochrome C and Smac release. Hence, this study convincingly demonstrates a novel strategy for overcoming acquired resistance to osimertinib and other 3rd generation EGFR-TKIs by targeting activation of the intrinsic apoptotic pathway through Mcl-1 inhibition, Bax activation or both, warranting further clinical validation of this strategy.

Identification of tumor microenvironment-based genes associated with acquired resistance to EGFR Tyrosine Kinase Inhibitor in Lung Adenocarcinoma.

Background: The tumor microenvironment evidently affects treatment response and clinical outcome. This study aims to construct a tumor microenvironment-based crosstalk between immunotherapy and epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in lung adenocarcinoma. Methods: We used ESTIMATE algorithm to calculate stromal and immune scores. Differentially expressed genes (DEGs) were extracted based on the comprehensive analysis of immune score groups and EGFR-TKI resistance samples. The independent prognostic value of the five selected genes was assessed by univariate/multivariate Cox regression analysis, survival analysis and the receiver operating characteristic (ROC) curve. Correlation analysis was performed using Spearman's rho value through TIMER 2.0. Results: The Kaplan-Meier survival curve show that patients with higher immune scores have significantly better overall survival. We identified 1328 DEGs from immune score groups and 806 DEGs from the EGFR-TKI resistance cohort GSE123066. A total of 19 co-regulated genes were found, and the Cox regression model produced a significant statistical prognosis for five genes (CENPF, CYSLTR1, GLDN, PIGR and SCGB3A1). Multivariate Cox regression analysis showed that the selected five gene signatures could be used as independent prognostic indicators. Furthermore, GSEA and correlation analysis demonstrated that CENPF was positively correlated to the signalling pathway which related to EGFR-TKI resistance and the well-known bypass gene. Conclusion: Our findings indicate that CENPF, CYSLTR1, GLDN, PIGR and SCGB3A1 are independent prognostic biomarkers associated with acquired EGFR-TKI resistance and tumor immune cell infiltration in lung adenocarcinoma, and CENPF may be a potential target that can improve immunotherapy efficacy and overcome the acquired EGFR-TKI resistance.

Systemic Immune-Inflammation Index Is a Prognostic Factor for Breast Cancer Patients After Curative Resection.

BACKGROUND: The preoperative systemic immune-inflammation index (SII) is correlated with prognosis in several malignancies. The aim of this study was to investigate the prognosis value of SII in patients with resected breast cancer. MATERIALS AND METHODS: A total of 784 breast cancer patients who underwent surgical resection were consecutively investigated. The optimal cutoff value of SII was evaluated using the receiver operating characteristic (ROC) curve. The collection of SII with clinicopathological characteristic and prognosis was further evaluated. RESULTS: The optimal cutoff value for SII in the prediction of survival was 514 according to ROC curve analysis. A high SII was significantly correlated with younger age (P = 0.037), PR status (P < 0.001), and HER2 status (P = 0.035). Univariate analysis revealed that SII (P < 0.001), T-stage (P < 0.001), lymph node involvement post-surgery (P = 0.024), and histological grade (P < 0.001) were significantly related to DFS, and SII (P < 0.001), T-stage (P = 0.003), lymph node involvement post-surgery (P = 0.006), and histological grade (P < 0.001) were significantly associated with OS. In multivariate analysis, a high SII was an independent worse prognostic factor for DFS (HR, 4.530; 95% CI, 3.279-6.258; P < 0.001) and OS (HR, 3.825; 95% CI, 2.594-5.640; P < 0.001) in all the enrolled patients. Furthermore, subgroup analysis of molecular subtype revealed that SII was significantly associated with prognosis in all subtypes. CONCLUSION: Preoperative SII is a simple and useful prognostic factor for predicting long-term outcomes for breast cancer patients undergoing surgery.

Lung-CRNet: A convolutional recurrent neural network for lung 4DCT image registration.

PURPOSE: Deformable image registration (DIR) of lung four-dimensional computed tomography (4DCT) plays a vital role in a wide range of clinical applications. Most of the existing deep learning-based lung 4DCT DIR methods focus on pairwise registration which aims to register two images with large deformation. However, the temporal continuities of deformation fields between phases are ignored. This paper proposes a fast and accurate deep learning-based lung 4DCT DIR approach that leverages the temporal component of 4DCT images. METHODS: We present Lung-CRNet, an end-to-end convolutional recurrent registration neural network for lung 4DCT images and reformulate 4DCT DIR as a spatiotemporal sequence predicting problem in which the input is a sequence of three-dimensional computed tomography images from the inspiratory phase to the expiratory phase in a respiratory cycle. The first phase in the sequence is selected as the only reference image and the rest as moving images. Multiple convolutional gated recurrent units (ConvGRUs) are stacked to capture the temporal clues between images. The proposed network is trained in an unsupervised way using a spatial transformer layer. During inference, Lung-CRNet is able to yield the respective displacement field for each reference-moving image pair in the input sequence. RESULTS: We have trained the proposed network using a publicly available lung 4DCT dataset and evaluated performance on the widely used the DIR-Lab dataset. The mean and standard deviation of target registration error are 1.56 ± 1.05 mm on the DIR-Lab dataset. The computation time for each forward prediction is less than 1 s on average. CONCLUSIONS: The proposed Lung-CRNet is comparable to the existing state-of-the-art deep learning-based 4DCT DIR methods in both accuracy and speed. Additionally, the architecture of Lung-CRNet can be generalized to suit other groupwise registration tasks which align multiple images simultaneously.

Ureteral metastasis from pulmonary adenocarcinoma: A case report and literature review.

The occurrence of ureteral metastasis from distant primary tumors is uncommon, and appears to be especially rare when it originates from the lungs. In the case presented here, a patient with lumbago and left hydronephrosis was diagnosed with left ureteral metastasis of pulmonary adenocarcinoma after a CT-guided percutaneous transthoracic needle biopsy of the lung and retroperitoneal laparoscopic left nephroureterectomy. He accepted the targeted therapy because the lung tumor epidermal growth factor receptor mutation (exon19 deletion) was positive, and preoperative staging of lung adenocarcinoma was stage IVA. After an 8-month follow-up, he is still alive and well, with no local recurrence or distant metastases. The therapy outcome assessment is stable disease. Although rare, our case has demonstrated that pulmonary adenocarcinoma has the possibility of metastasizing to the ureter, a risk that should be considered in some lung cancer patients.

Comprehensive analysis of competitive endogenous RNA associated with immune infiltration in lung adenocarcinoma.

To identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase. In addition, the Kaplan-Meier survival and Cox regression model analysis were used to identify the prognostic ceRNA network and TIICs. Correlation analysis was performed to analysis the correlation between the ceRNA network and TIICs. In the differently expressed RNAs between tumor and normal tissue, a total of 190 miRNAs, 224 lncRNAs and 3024 mRNAs were detected, and the constructed ceRNA network contained 5 lncRNAs, 92 mRNAs and 10 miRNAs. Then, six prognostic RNAs (FKBP3, GPI, LOXL2, IL22RA1, GPR37, and has-miR-148a-3p) were viewed as the key members for constructing the prognostic prediction model in the ceRNA network, and three kinds of TIICs (Monocytes, Macrophages M1, activated mast cells) were identified to be significantly related with the prognosis in lung adenocarcinoma. Correlation analysis suggested that the FKBP3 was associated with Monocytes and Macrophages M1, and the GPI was obviously related with Monocytes and Macrophages M1. Besides, the LOXL2 was associated with Monocytes and Activated mast cells, and the IL22RA1 was significantly associated with Monocytes and Macrophages M1, while the GPR37 and Macrophages M1 was closely related. The constructed ceRNA network and identified Monocytes, Macrophages M1 and activated Mast cells are all prognostic factors for lung adenocarcinoma. Moreover, the crosstalk between the ceRNA network and TIICs might be a potential molecular mechanism involved.

Effects of clinicopathological factors on prognosis of young patients with resected breast cancer.

ABSTRACT: This study aims to analyze the relationship between clinicopathological characteristics and survival in young patients (≤35 years old) with resected breast cancer.A total of 173 cases were included in this study. The clinicopathological factors potentially associated with prognosis were evaluated. Furthermore, we categorized patients into different groups to evaluate the prognosis according to hormone receptor status or important risk factors.Younger age (≤30 years) was an independent predictor for poor disease-free survival (DFS) and overall survival (OS). Besides, PR negative status, tumor grade, and advanced lymph nodes postsurgery were independent prognostic factors of DFS, while PR negative status and advanced lymph nodes postsurgery were independent prognostic factors of OS. For hormone receptor-positive patients, people with ER+ or PR+ and HER2-/+ showed poorer prognosis than the other 2 levels. Risk factor grouping based on the ER, PR, HER2, Ki-67 status, tumor grade, and lymph nodes postsurgery showed that patients in highest score group received the poorest prognosis. Grading system based on the hormone status or the risk factor grouping may offer a useful approach to assess which subgroups of young breast cancer present poorer prognosis.

Artesunate exhibits synergistic anti-cancer effects with cisplatin on lung cancer A549 cells by inhibiting MAPK pathway.

BACKGROUND: Artesunate (ART) has been used extensively as anti-malarial drugs worldwide. Besides, it has also been reported to have anti-cancer activities. This study was aimed to explore the anti-cancer activity of ART in combination with cisplatin (CIS) on A549 cells. METHODS: Cells were cultured with different concentrations of ART and/or CIS for 24, 48, or 72 h to test the anti-proliferative effects by CCK-8 assay. Colony formation assay and EdU staining were also performed. TUNEL staining was used to illustrate the morphologic changes. Cell cycle and apoptosis were determined by flow cytometry assay, and Western blot analysis was conducted to detect the expression of apoptosis- and proliferation-related proteins. Caspase activities were determined by colorimetric assay kit. Moreover, the synergistic effect of ART with CIS in A549 cell xenograft model was also determined. RESULTS: ART significantly inhibited cell proliferation in dose- and time-dependent manners. Collectively, the combination treatment remarkably decreased colony formation rates and increased the rates of TUNEL-positive cells compared with mono-treatment. Mechanistically, the combination treatment influenced the expression of Bcl-2, Bax, p-P53, Caspase-3/7, Caspase-9, CyclinB1, P34, P21, and synergistically regulated the activity of P38/JNK/ERK1/2 MAPK pathway. In mice A549 xenograft tumors, the combination strategy significantly increased the anti-cancer efficacy of ART and CIS alone, consistent with the in vitro observations. CONCLUSIONS: ART exhibited significant anti-tumor effect on A549 cells and this efficiency could be enhanced by combination with CIS.

A new data augmentation method based on local image warping for medical image segmentation.

PURPOSE: The segmentation accuracy of medical images was improved by increasing the number of training samples using a local image warping technique. The performance of the proposed method was evaluated in the segmentation of breast masses, prostate and brain tumors, and lung nodules. METHODS: We propose a simple data augmentation method which is called stochastic evolution (SE). Specifically, the idea of SE stems from our thinking about the deterioration of the diseased tissue and the healing process. In order to simulate this natural process, we implement it according to the local distortion algorithm in image warping. In other words, the irregular deterioration and healing processes of the diseased tissue is simulated according to the direction of the local distortion, thereby producing a natural sample that is indistinguishable by humans. RESULTS: The proposed method is evaluated on four segmentation tasks of breast masses, prostate, brain tumors, and lung nodules. Comparing the experimental results of four segmentation methods based on the UNet segmentation architecture without adding any expanded data during training, the accuracy and the Hausdorff distance obtained in our approach remain almost the same as other methods. However, the dice similarity coefficient (DSC) and sensitivity (SEN) have both improved to some extent. Among them, DSC is increased by 5.2%, 2.8%, 1.0%, and 3.2%, respectively; SEN is increased by 6.9%, 4.3%, 1.2%, and 4.5%, respectively. CONCLUSIONS: Experimental results show that the proposed SE data augmentation method could improve the segmentation accuracy of breast masses, prostate, brain tumors, and lung nodules. The method also shows the robustness with different image datasets and imaging modalities.

Prognostic Value of Microvessel Density in Head and Neck Squamous Cell Carcinoma: A Meta-Analysis.

The prognostic value of microvessel density (MVD) in head and neck squamous cell carcinoma (HNSCC) remains disputable. The purpose of this study was to comprehensively determine the prognostic value of MVD in HNSCC. Relevant literatures were identified using PubMed, Embase, and Cochrane Library. A meta-analysis was performed to clarify the prognostic role of MVD in HNSCC patients and different subgroups. A total of 14 eligible articles were included in this meta-analysis. The combined hazard ratio (HR) and 95% confidence interval (95% CI) for overall survival (OS) of 11 studies was 1.663 (1.236-2.237, P = 0.001), and the pooled HR and 95% CI for progression-free survival (PFS) of 7 studies was 2.069 (1.281-3.343, P = 0.003). Subgroup analyses were also performed on different issues, such as regional distribution of patients, age, tumor location, antibody, and treatment strategy. To conclude, high MVD is associated with worse OS and PFS in patients with HNSCC.

Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification.

Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients' survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning-based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. (3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results.