Prognosis of unresected versus resected early-stage pulmonary carcinoid tumors ≤3 cm in size: A population-based study.

PURPOSE: The observation-based prognosis, rather than resection, for small carcinoid tumors is still unclear. This lack of clarity has important implications for counseling elderly patients or patients for whom surgical resection poses a high risk. This study compared the outcomes of observation and surgical resection in patients with pulmonary carcinoid (PC) tumors ≤3 cm in size without metastasis. METHODS: Data of patients with PC tumors with ≤3 cm in diameter and without lymph node and distant metastases were retrieved from Surveillance, Epidemiology, and End Results (SEER) registry. To reduce the inherent bias of retrospective studies, propensity score matching analysis was performed. Overall survival (OS) and lung carcinoid-specific survival (LCSS) were analyzed using Kaplan-Meier plots. Multivariate analysis was used to determine predictors of LCSS in different size subgroups. RESULTS: In total, 4552 patients with early-stage PCs ≤3 cm in diameter, including 435 (9.56%) who were observed and 4117 (90.44%) treated by surgery, were recruited. Patients with surgery had significantly better OS and LCSS than those who were observed. However, patients with observation had comparable LCSS to those with surgery for PCs with tumor diameters ≤1 cm. Multivariate analysis indicated that surgical resection was an independent prognostic factor for LCSS in 1 cm < tumors ≤2 cm, and 2 cm < tumors ≤3 cm groups, but not for tumors ≤1 cm in diameter. CONCLUSION: Surgical resection of small PCs is associated with a survival advantage over observation. However, for early PCs ≤1 cm in diameter, observation may be considered in patients with high risk for surgical resection.

Carbon Corrosion Induced by Surface Defects Accelerates Degradation of Platinum/Graphene Catalysts in Oxygen Reduction Reaction.

Graphene supported electrocatalysts have demonstrated remarkable catalytic performance for oxygen reduction reaction (ORR). However, their durability and cycling performance are greatly limited by Oswald ripening of platinum (Pt) and graphene support corrosion. Moreover, comprehensive studies on the mechanisms of catalysts degradation under 0.6-1.6 V versus RHE (Reversible Hydrogen Electrode) is still lacking. Herein, degradation mechanisms triggered by different defects on graphene supports are investigated by two cycling protocols. In the start-up/shutdown cycling (1.0-1.6 V vs. RHE), carbon oxidation reaction (COR) leads to shedding or swarm-like aggregation of Pt nanoparticles (NPs). Theoretical simulation results show that the expansion of vacancy defects promotes reaction kinetics of the decisive step in COR, reducing its reaction overpotential. While under the load cycling (0.6-1.0 V vs. RHE), oxygen containing defects lead to an elevated content of Pt in its oxidation state which intensifies Oswald ripening of Pt. The presence of vacancy defects can enhance the transfer of electrons from graphene to the Pt surface, reducing the d-band center of Pt and making it more difficult for the oxidation state of platinum to form in the cycling. This work will provide comprehensive understanding on Pt/Graphene catalysts degradation mechanisms.

Regulating the Pore Structure of Activated Carbon by Pitch for High-Performance Sodium-Ion Storage.

The pore structure of carbon anodes plays a crucial role in enhancing the sodium storage capacity. Designing more confined pores in carbon anodes is accepted as an effective strategy. However, current design strategies for confined pores in carbon anodes fail to achieve both high capacity and initial Coulombic efficiency (ICE) simultaneously. Herein, we develop a strategy for utilizing the repeated impregnation and precarbonization method of liquid pitch to regulate the pore structure of the activated carbon (AC) material. Driven by capillary coalescence, the pitch is impregnated into the pores of AC, which reduces the specific surface area of the material. During the carbonization process, numerous pores with diameters less than 1 nm are formed, resulting in a high capacity and improved ICE of the carbon anode. Moreover, the ordered carbon layers derived from the liquid pitch also enhance the electrical conductivity, thereby improving the rate capability of as-obtained carbon anodes. This enables the fabricated material (XA-4T-1300) to have a high ICE of 91.1% and a capacity of 383.0 mA h g-1 at 30 mA g-1. The capacity retention is 95.5% after 300 cycles at 1 A g-1. This study proposes a practical approach to adjust the microcrystalline and pore structures to enhance the performance of sodium-ion storage in materials.

MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning.

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations across different slices. This presents a significant hurdle to medical image segmentation, as conventional segmentation methods may either overlook the minority class or overly emphasize the majority class, ultimately leading to a decrease in the overall generalization ability of the segmentation results. To overcome this, we propose a novel approach based on multi-step reinforcement learning, which integrates prior knowledge of medical images and pixel-wise segmentation difficulty into the reward function. Our method treats each pixel as an individual agent, utilizing diverse actions to evaluate its relevance for segmentation. To validate the effectiveness of our approach, we conduct experiments on four imbalanced medical datasets, and the results show that our approach surpasses other state-of-the-art methods in highly imbalanced scenarios. These findings hold substantial implications for clinical diagnosis and research.

Prognostic Factors for Survival of Stage IB Non-small Cell Lung Cancer Patients: A 10-Year Follow-Up Retrospective Study.

BACKGROUND: This study aimed to determine the prognostic factors for the long-term outcome of stage IB non-small cell lung cancer (NSCLC). METHODS: Surgically resected patients with stage IB NSCLC diagnosed (based on TNM 8th edition) between April 2008 and December 2013 were retrospectively reviewed. The prognosis and possible risk factors among the stage IB NSCLC patients were evaluated. RESULTS: Of the 349 patients identified for the study, 80 (22.9%) received post-surgery adjuvant chemotherapy (ACT). The median follow-up time after surgery was 123.3 months. The 10-year overall survival (OS) rate was 69.6%, and the 10-year recurrence-free survival (RFS) rate was 62.8%. The patients in this cohort were divided into three groups (T1 with visceral pleural invasion [VPI], T2a without VPI, and T2a with VPI), and no significant differences in OS or RFS were found among the groups. Furthermore, survival analysis indicated that the absence of ground-glass opacity (GGO) components portends an adverse long-term OS and RFS. In a subgroup of patients with solid nodules, age older than 65 years (hazard ratio [HR] 1.987; 95% confidence interval [CI] 1.312-3.010; p = 0.001) and ACT (HR 0.392; 95% CI 0.225-0.684; p < 0.001) were independent prognostic factors for OS, whereas lymphovascular invasion (HR 1.792; 95% CI 0.995-3.227; p = 0.052) should be considered as an independent unfavorable prognostic factor for RFS. CONCLUSIONS: As an upstaging factor, VPI did not further stratify prognosis for the stage IB patients in our cohort. The presence of GGO components had a notable impact on a favorable prognosis in stage IB NSCLCs.

Elevated Mast Cell Abundance Is Associated with Enrichment of CCR2+ Cytotoxic T Cells and Favorable Prognosis in Lung Adenocarcinoma.

Mast cells constitute indispensable immunoregulatory sentinel cells in the tumor microenvironment. A better understanding of the regulation and functions of mast cells in lung adenocarcinoma (LUAD) could uncover therapeutic approaches to reprogram the immunosuppressive tumor microenvironment. Here, we performed flow cytometry and single-cell RNA sequencing (scRNA-seq) of patient LUAD samples to comprehensively characterize LUAD-infiltrating mast cells. Mast cells exhibited functional heterogeneity and were enriched in LUAD with ground-glass opacity features (gLUAD). The mast cells in gLUAD exhibited proinflammatory and chemotactic properties while those in radiologically solid LUAD (sLUAD) were associated with tumor angiogenesis. Mast cells were an important source of CCL2 and correlated with the recruitment of CCR2+ CTL, a specific subcluster of preexhausted T cells with tissue-resident memory phenotype and enhanced cytotoxicity. Increased infiltration of mast cells and CCR2+ CTLs and their colocalization showed a strong association with favorable prognosis after surgery but were not associated with improved survival after chemotherapy. Collectively, these findings reveal a key role of mast cells in LUAD and their potential cross-talk with CTLs, suggesting that targeting mast cells may be an immunotherapeutic strategy for LUAD. SIGNIFICANCE: Comprehensive characterization of mast cells in lung adenocarcinoma elucidates their heterogeneity and identifies interplay between mast cells and CCR2+ T cells that is associated with a favorable prognosis.

A Hybrid Particle Swarm Optimization Algorithm with Dynamic Adjustment of Inertia Weight Based on a New Feature Selection Method to Optimize SVM Parameters.

Support vector machine (SVM) is a widely used and effective classifier. Its efficiency and accuracy mainly depend on the exceptional feature subset and optimal parameters. In this paper, a new feature selection method and an improved particle swarm optimization algorithm are proposed to improve the efficiency and the classification accuracy of the SVM. The new feature selection method, named Feature Selection-score (FS-score), performs well on data sets. If a feature makes the class external sparse and the class internal compact, its FS-score value will be larger and the probability of being selected will be greater. An improved particle swarm optimization model with dynamic adjustment of inertia weight (DWPSO-SVM) is also proposed to optimize the parameters of the SVM. By improving the calculation method of the inertia weight of the particle swarm optimization (PSO), inertia weight can decrease nonlinearly with the number of iterations increasing. In particular, the introduction of random function brings the inertia weight diversity in the later stage of the algorithm and the global searching ability of the algorithm to avoid falling into local extremum. The experiment is performed on the standard UCI data sets whose features are selected by the FS-score method. Experiments demonstrate that our algorithm achieves better classification performance compared with other state-of-the-art algorithms.

Lymph Node Metastases in Surgically Resected Solitary Ground-Glass Opacities: A Two-Center Retrospective Cohort Study and Pooled Literature Analysis.

BACKGROUND: An increasing body of evidence supports the noninferiority of sublobar resection compared with lobectomy in terms of survival for patients with early-stage lung cancer with ground-glass opacities (GGOs). However, few studies have focused on the incidence of lymph node (LN) metastases in these patients. We aimed to analyze N1 and N2 lymph node involvement in patients with non-small cell lung cancer (NSCLC) with GGO components stratified with different consolidation tumor ratio (CTR). PATIENTS AND METHODS: We performed two-center studies by retrospectively reviewing a total of 864 patients with NSCLC with semisolid or pure GGO manifestation (diameter ≤ 3 cm). Clinicopathologic features and outcomes were analyzed. We also reviewed 35 studies to characterize the patient with NSCLC population with the GGO manifestation. RESULTS: In both cohorts, there was no LN involvement for pure GGO NSCLC, while solid predominant GGO exhibited a relatively high LN involvement rate. On the basis of a pooled literature analysis, the incidence of pathologic mediastinal LN was 0% and 3.8% for pure and semisolid GGOs, respectively. GGO NSCLCs with CTR ≤ 0.5 also had rare LN involvement (0.1%). CONCLUSIONS: From two cohorts and pooled literature analysis, LN involvement was not observed in patients with pure GGO, and very few patients with semisolid GGO NSCLC with CTR ≤ 0.5 had LN involvement, revealing that it may be unnecessary to perform lymphadenectomy for pure GGOs, while mediastinal lymph node sampling (MLNS) is enough for semisolid GGOs with CTR ≤ 0.5. For the patients with GGO CTR > 0.5, mediastinal lymphadenectomy (MLD) or MLNS should be considered.

Evaluation of corrective effect of 6 degree of freedom couch on setup errors in intensity modulated radiotherapy for postoperative rectal cancer patients.

Objective: To explore the corrective effect of 6 degree of freedom couch on rotation errors in intensity modulated radiotherapy (IMRT) for postoperative rectal cancer patients, further to probe into the clinical application value of 6 degree of freedom couch in radiotherapy. Methods: From January 1, 2020 to December 1, 2020, 30 patients with rectal cancer receiving postoperative intensity modulated radiotherapy in The First Hospital of Hebei Medical University were included in this retrospective study. The setup error values in all direction of patients before and after 6 degree of freedom correction were collected during each radiotherapy session. Results: In this study, a total of 382 data before and after the correction of 6 degree of freedom couch were collected. It was found that the setup errors in the Y direction gradually increased, was maximal in the third week, and then became smaller, and the setup errors in the other directions increased with the extension of radiotherapy time and reached the maximum at the 5th week. In the translation direction, the setup errors value in Z direction occurred more frequently than that in X and Y directions between the range of 0.21-0.80 cm. In the rotation direction, the setup errors value in rotation X direction occurred more frequently than that in rotation Y and Z directions between the range of 0.21°-2.99°. In addition, after the correction of the 6 degree of freedom couch in real time, the setup errors in patients were significantly reduced in all directions (P < 0.05). Conclusion: In summary, it was recommended to clinically use 6 degree of freedom couch combined with IMRT for real-time correction of placement errors in patients with rectal cancer undergoing radiotherapy. At the same time, it was necessary to observe the tumor size and body weight changes of patients on the 5th week. If necessary, radiotherapy positioning and planning should be performed in time.

Wide aspect ratio matching for robust face detection.

Recently, anchor-based methods have achieved great progress in face detection. They adopt standard anchor matching strategy to sample positive anchors according to predefined IoU threshold. However, the max IoUs of extreme aspect ratio faces are still lower than fixed positive threshold, leading to the sampling failure from these faces. To construct a more robust detection model, more positive anchors from extreme aspect ratio faces need to be sampled and participate in the training phase. The goal of the present research is to improve the detection performance by reasonably extending sampling range of face aspect ratio. In this paper, we firstly explore the factors that affect the max IoU of each face in theory. Then, anchor matching simulation is performed to evaluate the sampling range of face aspect ratio. Finally, we propose a Wide Aspect Ratio Matching (WARM) strategy to collect more representative positive anchors from ground-truth faces across a wide range of aspect ratios. Besides, we present a novel feature enhancement module, named Receptive Field Diversity (RFD) module, to provide diverse receptive field corresponding to different aspect ratios. Extensive experiments have been conducted on popular benchmarks to show the effectiveness of our method, which can help detectors better capture extreme aspect ratio faces. Our method achieves promising APs on WIDER FACE validation dataset (easy: 0.965, medium: 0.955, hard: 0.904) and impressive generalization capability on FDDB dataset.

VANet: a medical image fusion model based on attention mechanism to assist disease diagnosis.

BACKGROUND: Today's biomedical imaging technology has been able to present the morphological structure or functional metabolic information of organisms at different scale levels, such as organ, tissue, cell, molecule and gene. However, different imaging modes have different application scope, advantages and disadvantages. In order to improve the role of medical image in disease diagnosis, the fusion of biomedical image information at different imaging modes and scales has become an important research direction in medical image. Traditional medical image fusion methods are all designed to measure the activity level and fusion rules. They are lack of mining the context features of different modes of image, which leads to the obstruction of improving the quality of fused images. METHOD: In this paper, an attention-multiscale network medical image fusion model based on contextual features is proposed. The model selects five backbone modules in the VGG-16 network to build encoders to obtain the contextual features of medical images. It builds the attention mechanism branch to complete the fusion of global contextual features and designs the residual multiscale detail processing branch to complete the fusion of local contextual features. Finally, it completes the cascade reconstruction of features by the decoder to obtain the fused image. RESULTS: Ten sets of images related to five diseases are selected from the AANLIB database to validate the VANet model. Structural images are derived from MR images with high resolution and functional images are derived from SPECT and PET images that are good at describing organ blood flow levels and tissue metabolism. Fusion experiments are performed on twelve fusion algorithms including the VANet model. The model selects eight metrics from different aspects to build a fusion quality evaluation system to complete the performance evaluation of the fused images. Friedman's test and the post-hoc Nemenyi test are introduced to conduct professional statistical tests to demonstrate the superiority of VANet model. CONCLUSIONS: The VANet model completely captures and fuses the texture details and color information of the source images. From the fusion results, the metabolism and structural information of the model are well expressed and there is no interference of color information on the structure and texture; in terms of the objective evaluation system, the metric value of the VANet model is generally higher than that of other methods.; in terms of efficiency, the time consumption of the model is acceptable; in terms of scalability, the model is not affected by the input order of source images and can be extended to tri-modal fusion.

Lobe-Specific Analysis of Sublobar Lung Resection for NSCLC Patients with Tumors ≤ 2 cm.

(1) Background: Sublobar resection can be used as an alternative surgical strategy for early-stage non-small-cell lung cancer (NSCLC) patients. However, the choice between wedge resection and segmentectomy remains contentious. In this study, we investigated the optimal surgical procedure for sublobar resection in patients with NSCLC ≤ 2 cm with a lobe-specific analysis; (2) Methods: Data for patients with T1N0M0 with a diameter of ≤2 cm who had undergone sublobar resection were retrieved. Propensity score matching (PSM) was used to reduce the inherent bias, and the Kaplan-Meier method and log-rank tests were used to assess the differences in survival; (3) Results: A total of 1882 patients were identified after the PSM. Patients with NSCLC ≤ 2 cm who had undergone segmentectomy showed better survival than those who had undergone wedge resection. However, when NSCLC was ≤1 cm, there was no significant difference in OS between the two groups. This demonstrated an OS advantage of segmentectomy over wedge resection for patients with NSCLC tumors of 1-2 cm (p = 0.024). Further analysis indicated that this survival benefit was only observed in patients with right upper NSCLC of 1-2 cm, but not with NSCLC in the other four lobes; (4) Conclusions: Segmentectomy showed a greater survival benefit than wedge resection only in patients with NSCLC of 1-2 cm, particularly those with primary tumors in the right upper lobe. Therefore, we propose a lobe-specific sublobar resection strategy for early-stage NSCLC patients (tumors of 1-2 cm) who cannot tolerate lobectomy.

A Self-Supervised Residual Feature Learning Model for Multifocus Image Fusion.

Multi-focus image fusion (MFIF) attempts to achieve an "all-focused" image from multiple source images with the same scene but different focused objects. Given the lack of multi-focus image sets for network training, we propose a self-supervised residual feature learning model in this paper. The model consists of a feature extraction network and a fusion module. We select image super-resolution as a pretext task in the MFIF field, which is supported by a new residual gradient prior discovered by our theoretical study for low- and high-resolution (LR-HR) image pairs, as well as for multi-focus images. In the pretext task, our network's training set is LR-HR image pairs generated from natural images, and HR images can be regarded as pseudo-labels of LR images. In the fusion task, the trained network extracts residual features of multi-focus images firstly. Secondly, the fusion module, consisting of an activity level measurement and a new boundary refinement method, is leveraged for the features to generated decision maps. Experimental results, both subjective evaluations and objective evaluations, demonstrate that our approach outperforms other state-of-the-art fusion algorithms.

Sublobar Resection Versus Lobectomy for Early-Stage Pulmonary Carcinoid Tumors ≤3 cm in Size: A SEER Population-Based Study.

OBJECTIVE: This study aimed to determine the optimal surgical procedure for early-stage pulmonary carcinoids (PCs). BACKGROUND: PCs, comprising typical carcinoids (TCs) and atypical carcinoids (ACs), are rare low-grade malignant tumors. We determine the optimal surgical management for early-stage PCs using data from the Surveillance, Epidemiology, and End Results registry. METHODS: Clinical and survival data of patients with early-stage PC tumors with a diameter ≤3 cm were retrieved. The Kaplan-Meier method and logrank tests were used to assess the differences in overall survival (OS). Subgroup analyses were also performed. To reduce the inherent bias of retrospective studies, two propensity score matching (PSM) analysis with (PSM2) or without (PSM1) consideration of lymph node assessment were performed. RESULTS: In total, 2934 patients with PCs, including 2741 (93.42%) with TCs and 193 (6.58%) with ACs, were recruited. After PSM1 analysis, TC patients in the lobectomy group had a significantly better OS than those in the sublobar resection group ( P = 0.0067), which is more remarkable for patients with a tumor diameter of 2 cm

Multimodal medical image fusion using adaptive co-occurrence filter-based decomposition optimization model.

MOTIVATION: Medical image fusion has developed into an important technology, which can effectively merge the significant information of multiple source images into one image. Fused images with abundant and complementary information are desirable, which contributes to clinical diagnosis and surgical planning. RESULTS: In this article, the concept of the skewness of pixel intensity (SPI) and a novel adaptive co-occurrence filter (ACOF)-based image decomposition optimization model are proposed to improve the quality of fused images. Experimental results demonstrate that the proposed method outperforms 22 state-of-the-art medical image fusion methods in terms of five objective indices and subjective evaluation, and it has higher computational efficiency. AVAILABILITY AND IMPLEMENTATION: First, the concept of SPI is applied to the co-occurrence filter to design ACOF. The initial base layers of source images are obtained using ACOF, which relies on the contents of images rather than fixed scale. Then, the widely used iterative filter framework is replaced with an optimization model to ensure that the base layer and detail layer are sufficiently separated and the image decomposition has higher computational efficiency. The optimization function is constructed based on the characteristics of the ideal base layer. Finally, the fused images are generated by designed fusion rules and linear addition. The code and data can be downloaded at https://github.com/zhunui/acof. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HID: The Hybrid Image Decomposition Model for MRI and CT Fusion.

Multimodal medical image fusion can combine salient information from different source images of the same part and reduce the redundancy of information. In this paper, an efficient hybrid image decomposition (HID) method is proposed. It combines the advantages of spatial domain and transform domain methods and breaks through the limitations of the algorithms based on single category features. The accurate separation of base layer and texture details is conducive to the better effect of the fusion rules. First, the source anatomical images are decomposed into a series of high frequencies and a low frequency via nonsubsampled shearlet transform (NSST). Second, the low frequency is further decomposed using the designed optimization model based on structural similarity and structure tensor to get an energy texture layer and a base layer. Then, the modified choosing maximum (MCM) is designed to fuse base layers. The sum of modified Laplacian (SML) is used to fuse high frequencies and energy texture layers. Finally, the fused low frequency can be obtained by adding fused energy texture layer and base layer. And the fused image is reconstructed by the inverse NSST. The superiority of the proposed method is verified by amounts of experiments on 50 pairs of magnetic resonance imaging (MRI) images and computed tomography (CT) images and others, and compared with 12 state-of-the-art medical image fusion methods. It is demonstrated that the proposed hybrid decomposition model has a better ability to extract texture information than conventional ones.

Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis.

BACKGROUND: In medical diagnosis of brain, the role of multi-modal medical image fusion is becoming more prominent. Among them, there is no lack of filtering layered fusion and newly emerging deep learning algorithms. The former has a fast fusion speed but the fusion image texture is blurred; the latter has a better fusion effect but requires higher machine computing capabilities. Therefore, how to find a balanced algorithm in terms of image quality, speed and computing power is still the focus of all scholars. METHODS: We built an end-to-end Hahn-PCNN-CNN. The network is composed of feature extraction module, feature fusion module and image reconstruction module. We selected 8000 multi-modal brain medical images downloaded from the Harvard Medical School website to train the feature extraction layer and image reconstruction layer to enhance the network's ability to reconstruct brain medical images. In the feature fusion module, we use the moments of the feature map combined with the pulse-coupled neural network to reduce the information loss caused by convolution in the previous fusion module and save time. RESULTS: We choose eight sets of registered multi-modal brain medical images in four diease to verify our model. The anatomical structure images are from MRI and the functional metabolism images are SPECT and 18F-FDG. At the same time, we also selected eight representative fusion models as comparative experiments. In terms of objective quality evaluation, we select six evaluation metrics in five categories to evaluate our model. CONCLUSIONS: The fusion image obtained by our model can retain the effective information in source images to the greatest extent. In terms of image fusion evaluation metrics, our model is superior to other comparison algorithms. In terms of time computational efficiency, our model also performs well. In terms of robustness, our model is very stable and can be generalized to multi-modal image fusion of other organs.

Frequent Genetic Alterations and Their Clinical Significance in Patients With Thymic Epithelial Tumors.

PURPOSE: Thymic epithelial tumors (TETs) are relatively rare neoplasms, including thymomas (types A, AB, B1, B2, and B3) and thymic carcinomas (TCs). The current knowledge about the biological properties of TETs is limited due to their low incidence. This study aimed to detect genetic alterations in TETs using next-generation sequencing(NGS) and explore their clinical significance in survival. METHODS: Tumor tissues and clinical data were collected from 34 patients with resected TETs in the Tianjin Medical University General Hospital between January 2011 and January 2019, and 56 cancer-associated genes were analyzed. The data of 123 TETs were retrieved from TCGA, and the information on their clinical and somatic mutations was explored. RESULTS: The cohort comprised 34 TETs including 17 thymomas and 17 TCs. The NGS results indicated that 73.08% of TCs+type B3 TETs and 37.50% of non-TCs+type B3 TETs each exhibited gene mutations. For patients with type B3/C, TP53 was the most frequent mutation (19.23%), followed by CDKN2A (11.54%). Similarly, in 123 TETs from the TCGA cohort, TP53 mutations were more frequent in patients with type B3/C than in patients with non-type B3/C (11.53% vs 3.09%). Further, patients with TET with TP53 mutations in the present cohort and the TCGA cohort had a worse prognosis compared with those without TP53 mutations. CONCLUSIONS: Gene mutation profiles between TCs+type B3 TETs and non-TCs+type B3 TETs were significantly different. The presence of TP53 mutations was more frequent in TCs+type B3 TETs than in non-TCs+type B3 TETs, which was associated with a worse prognosis.

Comparison of Lobectomy and Sublobar Resection for Stage IA Elderly NSCLC Patients (≥70 Years): A Population-Based Propensity Score Matching's Study.

BACKGROUND: To investigate the differences in survival between lobectomy and sub-lobar resection for elderly stage I non-small-cell lung cancer (NSCLC) patients using the Surveillance, Epidemiology, and End Results (SEER) registry. METHOD: The data of stage IA elderly NSCLC patients (≥ 70 years) with tumors less than or equal to 3 cm in diameter were extracted. Propensity-matched analysis was used. Lung cancer-specific survival (LCSS) was compared among the patients after lobectomy and sub-lobar resection. The proportional hazards model was applied to identify multiple prognostic factors. RESULTS: A total of 3,504 patients met criteria after propensity score matching (PSM). Although the LCSS was better for lobectomy than for sub-lobar resection in patients with tumors ≤ 3 cm before PSM (p < 0.001), no significant difference in the LCSS was identified between the two treatment groups after PSM (p = 0.191). Multivariate Cox regression showed the elder age, male gender, squamous cell carcinoma (SQC) histology type, poor/undifferentiated grade and a large tumor size were associated with poor LCSS. The subgroup analysis of tumor sizes, histologic types and lymph nodes (LNs) dissection, there were also no significant difference for LCSS between lobectomy and sub-lobar resection. The sub-lobar resection was further divided into segmentectomy or wedge resection, and it demonstrated that no significant differences in LCSS were identified among the treatment subgroups either. Multivariate Cox regression analysis showed that the elder age, poor/undifferentiated grade and a large tumor size were a statistically significant independent factor associated with survival. CONCLUSION: In terms of LCSS, lobectomy has no significant advantage over sub-lobar resection in elderly patients with stage IA NSCLC if lymph node assessment is performed adequately. The present data may contribute to develop a more suitable surgical treatment strategy for the stage IA elderly NSCLC patients.

The Impact of Programmed Death-Ligand 1 Expression on the Prognosis of Early Stage Resected Non-Small Cell Lung Cancer: A Meta-Analysis of Literatures.

INTRODUCTION: Previous studies have demonstrated that programmed cell death-ligand 1 (PD-L1) serves as biomarker for poor prognosis and survival in advanced-stage non-small cell lung cancer (NSCLC) patients. However, the merit of PD-L1 expression to predict the prognosis of early stage NSCLC patients who underwent complete resection remains controversial. In the present study, we performed a meta-analysis to investigate the relationship between PD-L1 expression and prognosis in patients with early stage resected NSCLC. METHODS: Electronic databases, including PubMed, EMBASE, and the Cochrane Library, were searched until July 23 2020 for studies evaluating the expression of PD-L1 and the prognosis of resected NSCLCs. Hazard ratios (HRs) with 95% confidence intervals (CIs) of overall survival (OS) and disease-free survival (DFS) were pooled and analyzed. Heterogeneity and publication bias analyses were also assessed. RESULTS: A total of 15 studies involving 3,790 patients were considered in the present meta-analysis. The pooled HR indicated that PD-L1 expression related to a much shorter DFS (HR = 1.56, 95% CI: 1.18-2.05, p < 0.01), as well a significantly worse OS (HR = 1.68, 95% CI: 1.29-2.18, p < 0.01). Furthermore, our analysis indicated that PD-L1 expression was significantly associated with gender (male vs. female: OR = 1.27, 95% CI:1.01-1.59, p = 0.038), histology (ADC vs. SCC: OR = 0.54, 95% CI:0.38-0.77, p = 0.001), TNM stage (I vs. II-III: OR = 0.45, 95% CI:0.34-0.60, p = 0.000), smoking status (Yes vs No: OR = 1.43, 95% CI:1.14-1.80, p = 0.002) and lymph node metastasis (N+ vs N-: OR = 1.97, 95% CI:1.26-3.08, p = 0.003). CONCLUSIONS: The results of this meta-analysis suggest that PD-L1 expression predicts an unfavorable prognosis in early stage resected NSCLCs. The role of personalized anti-PD-L1/PD-1 immunotherapy in the adjuvant settings of resected NSCLC warrants further investigation.