Search

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xue et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks., (© 2024. The Author(s).)

Objective: To compare the therapeutic effects and adverse reactions of sterilizing rare earth carbonate combined with concurrent chemoradiotherapy and simple concurrent chemoradiotherapy in the treatment of late-stage non-small cell lung cancer (NSCLC), and to analyze the reasons for the differences., Method: A total of 817 patients with pathologically diagnosed late-stage NSCLC from June 1, 2021 to December 30, 2022, in the affiliated hospital of Kunming University of Science and Technology, were selected. They were randomly divided into a control group of 394 people and an experimental group of 423 people. The control group was given concurrent chemoradiotherapy (cisplatin + etoposide), while the experimental group simultaneously took a low dose of sterilized rare earth carbonate (0.05mg/Kg). The χ² test and Fisher's test were used to compare the clinical pathological features, objective response rate (ORR), ECOG score, and adverse reactions of the two groups of patients, while survival analysis was used to compare the progression-free survival (PFS) of the two groups. Cox regression analysis was used to test factors related to prognosis., Results: The differences in clinical pathological features between the two groups of patients were not statistically significant, with all P>0.05. The ORR of the control group was 45.18% (178/394), and the experimental group was 89.83% (380/423), with a statistically significant difference (P=0.001). After treatment, the ECOG score of the experimental group was lower than that of the control group, P<0.001. The adverse reaction grading of patients in both groups was below level 3 after treatment, and no treatment-related fatalities occurred. The risk of pulmonary infection and bone marrow suppression in the experimental group was lower than that in the control group., Conclusion: In late-stage NSCLC patients, compared with simple concurrent chemoradiotherapy, the combination of concurrent chemoradiotherapy and sterilizing rare earth carbonate can significantly improve the short-term therapeutic effect and prognosis of patients, with good safety., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Cao, Ye, Wu, Zhang, Gong, Chen, You, Shen, Qiang and Cao.)

The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings. Here, we examine a graph-based model to facilitate end-to-end learning and sample suitable patches using a tile-based approach. We propose MIL-GNN to employ a graph-based Variational Auto-encoder with a Gaussian mixture model to discover relations between sample patches for the purposes to aggregate patch details into an individual vector representation. Using the classical MIL dataset MUSK and distinguishing two lung cancer sub-types, lung cancer called adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we exhibit the efficacy of our technique. We achieved a 97.42% accuracy on the MUSK dataset and a 94.3% AUC on the classification of lung cancer sub-types utilizing features., (© 2023. The Author(s).)

Purpose: Identifying the stage of lung cancer accurately from histopathology images and gene is very important for the diagnosis and treatment of lung cancer. Despite the substantial progress achieved by existing methods, it remains challenging due to large intra-class variances, and a high degree of inter-class similarities., Methods: In this paper, we propose a phased Multimodal Multi-scale Attention Model (MMAM) that predicts lung cancer stages using histopathology image data and gene data. The model consists of two phases. In Phase1, we propose a Staining Difference Elimination Network (SDEN) to eliminate staining differences between different histopathology images, In Phase2, it utilizes the image feature extractor provided by Phase1 to extract image features, and sends the multi-scale image features together with gene features into our Adaptive Enhanced Attention Fusion (AEAF) module for multimodal multi-scale features fusion to enable prediction of lung cancer staging., Results: We evaluated the proposed MMAM on the TCGA lung cancer dataset, and achieved 88.51% AUC and 88.17% accuracy on classification prediction of lung cancer stages I, II, III, and IV., Conclusion: The method can help doctors diagnose the stage of lung cancer patients and can benefit from multimodal data., (© 2023. CARS.)

Objective: To find out how bacterial lung infections (BLI) affect the effectiveness of therapy and the rate of pneumonia caused by pneumonia related to checkpoint inhibitors (CIP) in patients with non-small cell lung cancer (NSCLC) who are getting immunotherapy with checkpoint inhibitors (ICIs)., Patients and Methods: 507 NSCLC patients who received at least two ICI treatments between June 2020 and December 2022 at the Affiliated Hospital of Kunming University of Science and Technology(AHKUST) were included in a retrospective cohort study. Based on whether there was a concurrent BLI diagnosis from high-resolution CT scans of the chest, the patients were divided into two groups: 238 in the NSCLC with BLI group (NSCLC-BLI group), and 269 in the NSCLC alone group. The collected therapeutic outcome measures included the objective response rate (ORR), progression-free survival (PFS), overall survival (OS), and the incidence rate of CIP. We analyzed the effect of BLI on the therapeutic efficacy of ICI treatment and the incidence rate of CIP in NSCLC patients.Inclusion criteria based on NSCLC patients staged I to IV according to the 8th edition of the International Association for Lung Cancer Research (IASLC)., Results: The NSCLC-BLI group showed superior ORR to the NSCLC group when treated with ICIs. Multifactorial logistic regression and Cox analyses, adjusted for confounders, identified BLI as an independent positive prognostic factor for ORR (HR=0.482, 95%CI: 0.391-0.550; P<0.001) and PFS (HR=0.619; 95%CI: 0.551-0.771; P<0.001). No correlation between BLI and OS was found. Out of 26 cases of CIP, 12 were in the NSCLC-BLI group and 14 in the NSCLC group, with no significant difference in incidence (P=0.145)., Conclusion: NSCLC patients with BLI receiving ICI treatment show superior ORR and PFS compared to NSCLC alone without an increased CIP risk, positioning BLI as a predictive factor for improved outcomes in NSCLC patients receiving ICIs. However, the study has limitations including its retrospective nature and lacking data on BLI bacteria types and levels, which could influence therapy outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Cao, Wu, Chen, Wei, You, Qiang and Cao.)

As a critical indicator of how easily the human immune system recognizes tumour cells, tumour mutational burden (TMB) is widely used to identify the potential effectiveness of immune checkpoint inhibitor therapy. However, the difficulties associated with the whole exome sequencing (WES) process, such as high tissue sampling requirements, high costs, and long turnaround times, have hindered the widespread clinical use of WES. Furthermore, the mutation landscape varies across cancer types, and the distribution of TMBs varies across cancer subtypes. Therefore, there is an urgent clinical need to develop a small cancer-specific panel to estimate TMB accurately, predict immunotherapy response cost-effectively and assist physicians in precise decision-making. This paper uses a graph neural network framework (Graph-ETMB) to address the cancer specificity problem in TMB. The correlation and tractability between mutated genes are described through message-passing and aggregation algorithms between graph networks. Then the graph neural network is trained in the lung adenocarcinoma data through a semi-supervised approach, resulting in a mutation panel containing 20 genes with a length of only 0.16 Mb. The number of genes to be detected is smaller than most commercial panels currently in clinical use. In addition, the efficacy of the designed panel in predicting immunotherapy response was further determined in an independent validation dataset, exploring the association between TMB and immunotherapy efficacy., Competing Interests: Declaration of Competing Interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Accurate quantification of pulmonary nodules helps physicians to accurately diagnose and treat lung cancer. We try to improve the segmentation efficiency of irregular nodules while maintaining the segmentation accuracy of simple types of nodules., Methods: In this paper, we obtain the unique edge part of pulmonary nodules and process it as a single branch stream, i.e., border stream, to explicitly model the nodule edge information. We propose a multi-scale dense selective network based on border modeling (BorDenNet). Its overall framework consists of a dual-branch encoder-decoder, which achieves parallel processing of classical image stream and border stream. We design a dense attention module to facilitate a strongly coupled status of feature images to focus on key regions of pulmonary nodules. Then, during the process of model decoding, the multi-scale selective attention module is proposed to establish long-range correlation relationships between different scale features, which further achieves finer feature discrimination and spatial recovery. We introduce border context enhancement module to mutually fuse and enhance the edge-related voxel features contained in the image stream and border stream and finally achieve the accurate segmentation of pulmonary nodules., Results: We evaluate the BorDenNet rigorously on the lung public dataset LIDC-IDRI. For the segmentation of the target nodules, the average Dice score is 92.78[Formula: see text], the average sensitivity is 91.37[Formula: see text], and the average Hausdorff distance is 3.06 mm. We further test on a private dataset from Shanxi Provincial People's Hospital, which verifies the excellent generalization of BorDenNet. Our BorDenNet relatively improves the segmentation efficiency for multi-type nodules such as adherent pulmonary nodules and ground-glass pulmonary nodules., Conclusion: Accurate segmentation of irregular pulmonary nodules can obtain important clinical parameters, which can be used as a guide for clinicians and improve clinical efficiency., (© 2022. CARS.)

Background and Purpose: Accurate identification of lung cancer subtypes in medical images is of great significance for the diagnosis and treatment of lung cancer. Despite substantial progress in existing methods, they remain challenging due to limited annotated datasets, large intra-class differences, and high inter-class similarities., Methods: To address these challenges, we propose a Frequency Domain Transformer Model (FDTrans) to identify patients' lung cancer subtypes using the TCGA lung cancer dataset. We add a pre-processing process to transfer histopathological images to the frequency domain using a block-based discrete cosine transform and design a coordinate Coordinate-Spatial Attention Module (CSAM) to obtain critical detail information by reassigning weights to the location information and channel information of different frequency vectors. Then, a Cross-Domain Transformer Block (CDTB) is designed for Y, Cb, and Cr channel features, capturing the long-term dependencies and global contextual connections between different component features. At the same time, feature extraction is performed on the genomic data to obtain specific features. Finally, the image branch and the gene branch are fused, and the classification result is output through the fully connected layer., Results: In 10-fold cross-validation, the method achieves an AUC of 93.16% and overall accuracy of 92.33%, which is better than similar current lung cancer subtypes classification detection methods., Conclusion: This method can help physicians diagnose the subtypes classification of lung cancer in patients and can benefit from both spatial and frequency domain information., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Purpose: Longitudinal CT images contain the law of lesion growth and evolution over time. Therefore, our purpose is to explore the growth and evolution law of pulmonary lesions in the time dimension to improve the performance of predicting the malignant evolution of pulmonary nodules., Methods: In this paper, we propose a Multi-task Spatial-Temporal Self-attention network (MSTS-Net) to predict the malignancy growth trend of pulmonary nodules from different periods. More specifically, the model achieves lesion segmentation task and lesion prediction task by sharing the same encoder. Segmentation task boosts the performance of the prediction task. In addition, a Static Context Spatial Self-attention Module and a Dynamic Adaptive Temporal Self-Attention Module are introduced to capture both static spatial coherence patterns between consecutive slices of lesions in the same period and temporal dynamics across different time points., Results: We repeatedly evaluated the proposed method on the National Lung Screening Trial dataset and the Shanxi Cancer Hospital dataset. The final experimental results show that our MSTS-Net has an area under the ROC curve score of 0.919., Conclusion: In the computer-aided prediction of the malignant evolution of pulmonary nodules, combining the characteristics of the temporal dimension of pulmonary nodules with CT data can effectively improve the accuracy of prediction. The MSTS-Net we developed has high predictive value and broad prospects for clinical application., (© 2022. CARS.)

Aiming at the problems of long time, high cost, invasive sampling damage, and easy emergence of drug resistance in lung cancer gene detection, a reliable and non-invasive prognostic method is proposed. Under the guidance of weakly supervised learning, deep metric learning and graph clustering methods are used to learn higher-level abstract features in CT imaging features. The unlabeled data is dynamically updated through the k-nearest label update strategy, and the unlabeled data is transformed into weak label data and continue to update the process of strong label data to optimize the clustering results and establish a classification model for predicting new subtypes of lung cancer imaging. Five imaging subtypes are confirmed on the lung cancer dataset containing CT, clinical and genetic information downloaded from the TCIA lung cancer database. The successful establishment of the new model has a significant accuracy rate for subtype classification (ACC = 0.9793), and the use of CT sequence images, gene expression, DNA methylation and gene mutation data from the cooperative hospital in Shanxi Province proves the biomedical value of this method. The proposed method also can comprehensively evaluate intratumoral heterogeneity based on the correlation between the final lung CT imaging features and specific molecular subtypes., (© 2023. The Author(s).)

Objective . Recently, imaging genomics has increasingly shown great potential for predicting postoperative recurrence of lung cancer patients. However, prediction methods based on imaging genomics have some disadvantages such as small sample size, high-dimensional information redundancy and poor multimodal fusion efficiency. This study aim to develop a new fusion model to overcome these challenges. Approach . In this study, a dynamic adaptive deep fusion network (DADFN) model based on imaging genomics is proposed for predicting recurrence of lung cancer. In this model, the 3D spiral transformation is used to augment the dataset, which better retains the 3D spatial information of the tumor for deep feature extraction. The intersection of genes screened by LASSO, F-test and CHI-2 selection methods is used to eliminate redundant data and retain the most relevant gene features for the gene feature extraction. A dynamic adaptive fusion mechanism based on the cascade idea is proposed, and multiple different types of base classifiers are integrated in each layer, which can fully utilize the correlation and diversity between multimodal information to better fuse deep features, handcrafted features and gene features. Main results . The experimental results show that the DADFN model achieves good performance, and its accuracy and AUC are 0.884 and 0.863, respectively. This indicates that the model is effective in predicting lung cancer recurrence. Significance . The proposed model has the potential to help physicians to stratify the risk of lung cancer patients and can be used to identify patients who may benefit from a personalized treatment option., (© 2023 Institute of Physics and Engineering in Medicine.)

Objective: Yunnan, China, is a central tobacco-producing region with a large smoking population and an increasing incidence of lung cancer in recent years. This study aimed to understand the incidence of lung cancer and the characteristics of lung nodules on low-dose computed tomography (LDCT) scans of the chest in a long-term smoking population in Kunming., Patients and Methods: Long-term smokers in Kunming who were not at risk of evident lung disease symptoms were recruited through recommendation and publicity by the Kunming University of Science and Technology., Results: Among 375 cases eligible for inclusion,14 cases of lung cancer were detected with a detection rate of 3.73% (95% CI: 2.55%-4.27%), including one case of squamous carcinoma, one case of small cell lung cancer, seven cases of adenocarcinoma of the lung and five cases of early-stage lung cancer (35.71%). In the group of < 6 mm solid nodules and < 5 mm non-solid nodules, no lung cancer was detected in 201 cases; lung cancer was detected in 14 cases in 61 cases, and there was a statistical difference between the two groups (p < 0.05)., Conclusions: The lung cancer detection rate in long-term smokers was high, with the type predominantly adenocarcinoma and a high incidence of lung nodules, and increased when solid nodules≥6 mm or non-solid nodules ≥ 5 mm were present. It is recommended that screening for lung cancer by LDCT of the chest be introduced in the male smoking population who meet the risk factors and that screening for lung cancer in women should be redefined as a high-risk factor.

Objective: The association between sleep status and lung adenocarcinoma risk was analyzed using long-term follow-up data from 60,443 patients over the period 2016-2022 to provide a reference for exploring the association between sleep status and lung adenocarcinoma development., Patients and Methods: Based on long-term follow-up data, a total of 60,443 people were included. Sleep data collected for the study included insomnia symptoms, lunch break habits, and sleep duration. A sleep score (0-3) was constructed based on difficulty falling asleep, premature awakening and sleep duration. Proportional risk regression models were used to analyze the association between each sleep factor, sleep score and lung cancer risk., Results: The study population was followed up for 9.9 ± 4.8 years and a total of 307 cases of lung adenocarcinoma were first recorded during the follow-up period. After controlling for potential confounders, the risk ratios (HR) for lung adenocarcinoma in those with difficulties going asleep or waking up too early were 1.12 (95% CI: 1.02-1.14) and 1.07 (95% CI: 1.01-1.11), respectively, compared to those without symptoms of insomnia. The HR for lung adenocarcinoma in those with less than 7 h of sleep [HR = 1.17 (95% CI: 1.05-1.21)] was compared to those with ≥ 7 h of sleep per day. Compared to those with a sleep score of 3 (highest quality sleep), those with a sleep score of 2, 1 and 0 corresponded to HR of 1.06 (95% CI: 1.01-1.12), 1.11 (95% CI: 1.09-1.18) and 1.15 (95% CI: 1.01-1.32) respectively., Conclusions: Patients who suffer from insomnia or have a short sleep schedule are at increased risk of developing lung cell cancer. Sleep has an important impact on health and improving sleep conditions can reduce the incidence of lung cancer.

Accurate prediction of the tumor's future imaging features can provide its complete growth evolution and more detailed clinical parameters. The existing longitudinal models tend to lose detailed growth information and make it difficult to model the complete tumor development process. In this paper, we propose the Static-Dynamic coordinated Transformer for Tumor Longitudinal Growth Prediction (SDC-Transformer). To extract the static high-level features of tumors in each period, and to further explore the dynamic growth associations and expansion trend of tumors between different periods. Aiming at the insensitivity to local pixel information of the Transformer, we propose the Local Adaptive Transformer Module to facilitate a strongly coupled status of feature images, which ensures the characterization of tumor complex growth trends. Faced with the dynamic changes brought about by tumor growth, we introduce the Dynamic Growth Estimation Module to predict the future growth trend of the tumor. As a core part of SDC-Transformer, we design the Enhanced Deformable Convolution to enrich the sampling space of tumor growth pixels. And a novel Cascade Self-Attention is performed under multi-growth imaging to obtain dynamic growth relationships between periods and use dual cascade operations to predict the tumor's future expansion trajectories and growth contours. Our SDC-Transformer is rigorously trained and tested on longitudinal tumor data composed of the National Lung Screening Trial (NLST) and collaborative Shanxi Provincial People's Hospital. The RMSE, Dice, Recall, and Specificity of the longitudinal prediction results reach 11.32, 89.31%, 90.57%, and 89.64%, respectively. This result shows that our proposed SDC-Transformer model can achieve accurate longitudinal prediction of tumors, which will help physicians to establish specific treatment plans and accurately diagnose lung cancer. The code will be released soon., (Copyright © 2022. Published by Elsevier Ltd.)

Purpose: Medical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly., Methods: In this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results., Results: The experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor., Conclusion: In computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy., (© 2022. CARS.)

Lung adenocarcinoma (LUAD) represents the major histological type of lung cancer with high mortality globally. Due to the heterogeneous nature, the same treatment strategy to various patients may result in different therapeutic responses. Hence, we aimed to elaborate an effective signature for predicting patient survival outcomes. The TCGA-LUAD cohort from the TCGA portal was used as a training dataset. The GSE26939 and GSE68465 cohorts from the GEO database were taken as validation datasets. All immunologically relevant genes were extracted from the ImmPort. The ESTIMATE algorithm was employed to explore LUAD microenvironment in the training dataset. Further, the DEGs were picked out based on the immune-associated genes reflecting different statuses in the immune context of TME. Univariate/multivariate Cox regression was performed to determine six prognosis- specific genes (PIK3CG, BTK, VEGFD, INHA, INSL4, and PTPRC) and established a risk predictive signature. The time-dependent ROC indicated that AUC values were all greater than 0.70 at 1-, 3-, and 5- year intervals. Corresponding RiskScore of each LUAD patient was calculated from the signature, and they were stratified into the high- and low-risk groups by the median value of RiskScore. K-M curves and Log-rank test demonstrated significant survival differences between the two groups ( P < 0.05). Similar results were exhibited in the validation datasets. The RiskScore was incredibly relevant to clinicopathological factors like gender, AJCC stage, and T stage. Also, it can mirror the distribution state of 15 kinds of TIICs and have some predictive value for the sensitivity of therapeutic drugs.

Background: Nowadays, controlling nutritional status (CONUT) has been used as a prognostic factor in variety of cancers. However, no consensus has been reached on the prognostic value of CONUT in lung cancer. In this study, we aim to investigate the role of CONUT in survival of patients with lung cancer., Methods: EMBASE, web of science, and Medline were used to search articles in English-language journals. The association between CONUT score and survival of patients with lung cancer was evaluated by using pooled HRs and their 95% CIs. Chi-square test and I-Square was used to test heterogeneity among studies. Analyses were all performed using Stata 13.0 (Stata Corporation, College Station, TX)., Results: Eight studies with 1,836 patients were eventually included in this meta-analysis. The pooled results showed that high CONUT score had an unfavorable impact on OS (HR =1.63, 95% CI: 1.30-2.04), DFS (HR =1.75, 95% CI: 1.35-2.26), CSS (HR =1.45, 95% CI: 1.01-2.07) and PFS (HR =1.67, 95% CI: 0.99-2.35), compared with those with low-CONUT., Conclusions: CONUT can be used as a predictor of prognosis in patients with lung cancer. High-CONUT score was significantly associated with poor OS, DFS, CSS and PFS.

Angiogenesis is a core hallmark of advanced cancers, especially in lung adenocarcinoma (LUAD). However, the underlying functions and mechanisms of lncRNAs in tumor angiogenesis remain largely unknown. Here we found that linc00665 depletion could markedly depressed proliferation and capillary tube formation of HUVECs in vitro. Mechanistically, linc00665 directly interacted with YB-1 protein, enhanced its stability through inhibiting ubiquitination-dependent proteolysis and stimulated its nuclear translocation in LUAD cells. The accumulated nuclear YB-1 activated expression of ANGPT4, ANGPTL3 and VEGFA by binding to their promoters, contributing to tumor-related angiogenesis in vitro and in vivo. Collectively, we conclude that linc00665 induces tumor-related angiogenesis in LUAD by directly interacting with YB-1 and activating YB-1-ANGPT4/ANGPTL3/VEGFA axis, which provides promising anti-angiogenic targets for cancer therapy., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

BACKGROUND This study aimed to use bioinformatics analysis to compare data from tissue microarrays from patients with lung adenocarcinoma (LUAD) and normal lung tissue, and human lung adenocarcinoma cells with normal lung epithelial cells in vitro to investigate the role of synaptotagmin 12 (SYT12) gene expression in LUAD. MATERIAL AND METHODS Human lung adenocarcinoma cell lines (A549, SPC-A-1, H1299, H1975, and PC9) and the normal HBE cell line were compared, and tumor xenografts were developed in mice. The Cancer Genome Atlas (TCGA) tissue microarray data were used to compare SYT12 expression and overall survival (OS). The in vivo and in vitro effects of down-regulation and upregulation of SYT12 were studied using short-interfering RNA (si-RNA) and overexpression plasmids, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway analysis, quantitative reverse transcription-polymerase chain reaction (qRT-PCR), and Western blot investigated the molecular mechanisms of SYT12 expression in LUAD. RESULTS SYT12 expression was increased in tissues from patients with LUAD from TCGA and was associated with advanced tumor stage and reduced prognosis. Knockdown of SYT12 suppressed the proliferation and migration of LUAD cells, and upregulation of SYT12 increased the proliferation and migration of LUAD cells in vitro. Phosphorylation of PIK3R3 activated the PI3K/AKT/mTOR pathway. In the mouse xenograft model, expression of SYT12 increased the volume and weight of the xenograft tumors. CONCLUSIONS Bioinformatics analysis, human LUAD cells, and mouse xenograft studies showed that SYT12 acted as a possible oncogene by phosphorylation of PIK3R3 to activate the PI3K/AKT/mTOR signaling pathway.

Non-small-cell lung cancer (NSCLC) accounts for nearly 85% of lung cancer cases. LukS-PV, one of the two components of Panton-Valentine leucocidin (PVL), is produced by Staphylococcus aureus. The present study showed that LukS-PV can induce apoptosis in human acute myeloid leukemia (AML) lines (THP-1 and HL-60). However, the role of LukS-PV in NSCLC is unclear. In this study, we treated NSCLC cell lines A549 and H460 and a normal lung cell line, 16HBE, with LukS-PV and investigated the biological roles of LukS-PV in NSCLC. Cells were treated with varying concentrations of LukS-PV and cell viability was evaluated by CCK8 and EdU assay. Flow cytometry was used to detect cell apoptosis and analyze the cell cycle, and the expression of apoptosis and cell cycle-associated proteins and genes were identified by western blotting analysis and qRT-polymerase chain reaction, respectively. We found that LukS-PV inhibited the proliferation of NSCLC cells but had little cytotoxicity in normal lung cells. LukS-PV induced NSCLC cell apoptosis and increased the BAX/BCL-2 ratio, triggering S-phase arrest in A549 and H460 cells while increasing P21 expression and decreasing CDK2, cyclin D1, and cyclin A2 expression. We also observed increased P-p38 and P-ERK in NSCLC cells treated with LukS-PV. Treatment of NSCLC with LukS-PV combined with p38 and ERK inhibitors reversed the pro-apoptotic and pro-cell cycle arrest effects of LukS-PV. Overall, these findings indicate that LukS-PV has anti-tumor effects in NSCLC and may contribute to the development of anti-cancer agents., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Lung cancer is the leading cause of cancer death worldwide. To overcome the toxic side effects and multidrug resistance (MDR) during doxorubicin (DOX) chemotherapy, a urokinase plasminogen activator receptor (uPAR) targeting U11 peptide decorated, pH-sensitive, dual drugs co-encapsulated nanoparticles (NPs) system is employed in this study. A U11 peptide conjugated, pH-sensitive DOX prodrug (U11-DOX) was synthesized and used as materials to produce NPs. A curcumin (CUR) and U11-DOX co-encapsulated NPs system (U11-DOX/CUR NPs) was constructed to treat lung cancer. After the characterization of biophysical properties of this NPs system, synergistic chemotherapeutic efficacy was evaluated in both cultured cancer cells and tumor-bearing animal model. U11-DOX/CUR NPs had a uniformly spherical shape with a core-shell structure. The mean particle size and zeta potential of the U11-DOX/CUR NPs was 121.3 nm and -33.5 mV, with a DOX and CUR EE of 81.7 and 90.5%, respectively. The DOX release from U11-DOX/CUR NPs was 83.5, 55.2, and 32.8% correspondence to the pH of 5.0, 6.0 and 7.4. Cellular uptake efficiency of U11-DOX/CUR NPs was significantly higher than non U11 peptide decorated DOX/CUR NPs. U11-DOX/CUR NPs displayed a pronounced synergy effects in vitro and an obvious tumor tissue accumulation efficiency in vivo. In vivo antitumor experiment showed that U11-DOX/CUR NPs could inhibit the tumor growth to a level of 85%.In vitro and in vivo studies demonstrated that U11-DOX/CUR NPs is a sustained released, pH responsive, synergistic antitumor system. This study suggests that the U11-DOX/CUR NPs have promising potential for combination treatment of lung cancer., (Copyright © 2019 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Long non-coding RNAs (lncRNAs) are frequently dysregulated in multiple malignancies, demonstrating their potential oncogenic or tumor-suppressive roles in tumorigenesis. Herein, we reported the identification of a novel lncRNA, linc00665 (ENST00000590622), which was markedly upregulated in lung adenocarcinoma (LUAD) tissues and might serve as an independent predictor for poor prognosis. Functional assays indicated that linc00665 reinforced LUAD cell proliferation and metastasis in vitro and in vivo. Mechanistically, transcription factor SP1 induced the transcription of linc00665 in LUAD cells, which exerted its oncogenic role by functioning as competing endogenous RNA (ceRNA) for miR-98 and subsequently activating downstream AKR1B10-ERK signaling pathway. Together, our study elucidates oncogenic roles of linc00665-miR98-AKR1B10 axis in LUAD tumorigenesis, which may serve as potential diagnostic biomarkers and therapeutic targets.

Lymph node metastasis (LNM) is one of the major causes of cancer‑associated morbidity and mortality in patients with lung cancer following radical pulmonary carcinoma resection. The present study aimed to investigate the relationship between the expression of collagen and calcium‑binding epidermal growth factor domain‑containing protein 1 (CCBE1) and lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1) in tumor tissue with the clinical prognosis of lung cancer. The present study included 40 patients with lung cancer that underwent pulmonary carcinoma resection, including 10 patients with LNM, and 10 control patients who underwent pulmonary bullae resection. CCBE1 and LYVE1 expression was assessed in samples from normal and tumor tissue using polymerase chain reaction, western blot analysis and immunohistochemistry. CCBE1 expression appeared to be decreased in lung tumor tissue and further downregulated in samples from patients with LNM, and was revealed to be correlated with poor clinical outcome. Conversely, LYVE1 expression appeared to be upregulated in lung cancer tissue. In conclusion, the present results suggested that CCBE1 and LYVE1 may have potential as biomarkers for the identification of lung cancer patients at a high risk of LNM.

The accurate segmentation of pulmonary nodules is an important preprocessing step in computer-aided diagnoses of lung cancers. However, the existing segmentation methods may cause the problem of edge leakage and cannot segment juxta-vascular pulmonary nodules accurately. To address this problem, a novel automatic segmentation method based on an LBF active contour model with information entropy and joint vector is proposed in this paper. Our method extracts the interest area of pulmonary nodules by a standard uptake value (SUV) in Positron Emission Tomography (PET) images, and automatic threshold iteration is used to construct an initial contour roughly. The SUV information entropy and the gray-value joint vector of Positron Emission Tomography-Computed Tomography (PET-CT) images are calculated to drive the evolution of contour curve. At the edge of pulmonary nodules, evolution will be stopped and accurate results of pulmonary nodule segmentation can be obtained. Experimental results show that our method can achieve 92.35% average dice similarity coefficient, 2.19 mm Hausdorff distance, and 3.33% false positive with the manual segmentation results. Compared with the existing methods, our proposed method that segments juxta-vascular pulmonary nodules in PET-CT images is more accurate and efficient.

Background: Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow processing speed, particularly for images in the top and bottom of the lung and the images that contain lung nodules., Method: Our proposed method first uses the position of the lung parenchyma image features to obtain lung parenchyma ROI image sequences. A gradient and sequential linear iterative clustering algorithm (GSLIC) for sequence image segmentation is then proposed to segment the ROI image sequences and obtain superpixel samples. The SGNF, which is optimized by a genetic algorithm (GA), is then utilized for superpixel clustering. Finally, the grey and geometric features of the superpixel samples are used to identify and segment all of the lung parenchyma image sequences., Results: Our proposed method achieves higher segmentation precision and greater accuracy in less time. It has an average processing time of 42.21 seconds for each dataset and an average volume pixel overlap ratio of 92.22 ± 4.02% for four types of lung parenchyma image sequences.

The similarity-based retrieval of lung nodule computed tomography (CT) images is an important task in the computer-aided diagnosis of lung lesions. It can provide similar clinical cases for physicians and help them make reliable clinical diagnostic decisions. However, when handling large-scale lung images with a general-purpose computer, traditional image retrieval methods may not be efficient. In this paper, a new retrieval framework based on a hashing method for lung nodule CT images is proposed. This method can translate high-dimensional image features into a compact hash code, so the retrieval time and required memory space can be reduced greatly. Moreover, a pruning algorithm is presented to further improve the retrieval speed, and a pruning-based decision rule is presented to improve the retrieval precision. Finally, the proposed retrieval method is validated on 2,450 lung nodule CT images selected from the public Lung Image Database Consortium (LIDC) database. The experimental results show that the proposed pruning algorithm effectively reduces the retrieval time of lung nodule CT images and improves the retrieval precision. In addition, the retrieval framework is evaluated by differentiating benign and malignant nodules, and the classification accuracy can reach 86.62%, outperforming other commonly used classification methods., Competing Interests: The authors declare that they have no competing interests.

Background: Integrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives., Method: Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method., Results: Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).

Non-small cell lung cancer (NSCLC) accounts for most lung cancer. To develop new therapy required the elucidation of NSCLC pathogenesis. The deubiquitinating enzymes USP 28 has been identified and studied in colon and breast carcinomas. However, the role of USP28 in NSCLC is unknown. The level mRNA or protein level of USP28 were measured by qRT-PCR or immunohistochemistry (IHC). The role of USP28 in patient survival was revealed by Kaplan-Meier plot of overall survival in NSCLC patients. USP28 was up or down regulated by overexpression plasmid or siRNA transfection. Cell proliferation and apoptosis was assayed by MTT and FACS separately. Potential microRNAs, which targeted USP28, were predicated by bioinformatic algorithm and confirmed by Dual Luciferase reporter assay system. High mRNA and protein level of USP28 in NSCLC were both correlated with low patient survival rate. Overexpression of USP28 promoted NSCLC cells growth and vice versa. Down-regulation of USP28 induced cell apoptosis. USP28 was targeted by miR-4295. Overexpression of USP28 promoted NSCLC cells proliferation, and was associated with poor prognosis in NSCLC patients. The expression of USP28 may be regulated by miR-4295. Our data suggested that USP28 was a tumour-promoting factor and a promising therapeutic target for NSCLC., (© 2015 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.)

Purpose: To aid a consistent segmentation of pulmonary nodules, the authors describe a novel computerized scheme that utilizes a freehand sketching technique and an improved break-and-repair strategy., Methods: This developed scheme consists of two primary parts. The first part is freehand sketch analysis, where the freehand sketching not only serves a natural way of specifying the location of a nodule, but also provides a mechanism for inferring adaptive information (e.g., the mass center, the density, and the size) in regard to the nodule. The second part is an improved break-and-repair strategy. The improvement avoids the time-consuming ray-triangle intersections using spherical bins and replaces the original global implicit surface reconstruction with a local implicit surface fitting and blending scheme. The performance of this scheme, including accuracy and consistence, was assessed using 50 CT examinations in the Lung Image Database Consortium (LIDC). For each of these examinations, a single nodule was selected under the aid of a publically available tool to assure these nodules were diverse in size, location, and density. Two radiologists were asked to use the developed tool to segment these nodules twice at different times (at least three months apart). A Hausdorff distance based method was used to assess the discrepancies (agreements) between the computerized results and the results by the four radiologists in the LIDC as well as the inter- and intrareader agreements in freehand sketching., Results: The maximum and mean discrepancies in boundary outlines between the computerized scheme and the radiologists were 2.73 ± 1.32 mm and 1.01 ± 0.47 mm, respectively. When the nodules were classified (binned) into different size ranges, the maximum errors ranged from 1.91 to 4.13 mm; but smaller nodules had larger percentage discrepancies in term of size. Under the aid of the developed scheme, the inter- and intrareader variability in averaged maximum discrepancy across all types of pulmonary nodules were consistently smaller than 0.15 ± 0.07 mm. The computational cost in time of segmenting a pulmonary nodule ranged from 0.4 to 2.3 s with an average of 1.1 s for a typical desktop computer., Conclusions: The experiments showed that this scheme could achieve a reasonable performance in nodule segmentation and demonstrated the merits of incorporating freehand sketching into pulmonary nodule segmentation., (© 2014 American Association of Physicists in Medicine.)

Background: Lung cancer is the lead cause of cancer-related mortality around the world. Non-small cell lung cancer (NSCLC) accounts for nearly 85% of all case of lung cancer. Accumulating evidence revealed the importance of miRNAs in the pathogenesis of NSCLC. Whether miR-4500 promotes NSCLC or not is still unknown. The potential targeted genes needed to be investigated., Methods: The level of miR-4500 was measured by qRT-PCR. The role of miR-4500 in patient survival was revealed by the Kaplan-Meier plot of overall survival of NSCLC patients. miR-4500 was up or down regulated by miRNAs mimics or ASO transfection. Cell proliferation and apoptosis were assayed by the MTT assay and FACS analysis separately. Targeted genes were predicted by a bioinformatic algorithm and confirmed by the Dual Luciferase reporter assay system., Results: NSCLC cell lines and tissues showed lower level of miR-4500. High miR-4500 expression was correlated with high patient survival rate. MiR-4500 overexpression inhibited NSCLC proliferation and induced apoptosis and vice versa. LIN28B and NRAS were targeted by miR-4500., Conclusions: Low expression of miR-4500 in NSCLC promoted tumor growth by targeting LIN28B and NRAS. MiR-4500 may be a prognosis predictor and potential target for NSCLC therapy., (© 2014 S. Karger AG, Basel.)