4-1BB agonists for cancer immunotherapy have shown good preliminary efficacy in clinical trials, but several of the first-generation 4-1BB agonistic antibodies entering the clinic have failed due to safety issues. Selenium nanoparticles (SeNPs) exhibit anti-inflammatory, anti-tumor, antioxidant, and immune-modulating properties. In addition, they have been shown to have detoxifying effects and prevent oxidative liver damage. In this study, we used an anti-4-1BB antibody in combination with SeNPs to evaluate the anti-lung cancer effects in in vitro and in vivo experiments and explore the underlying mechanisms by pathological analyses, quantitative PCR, and enzyme-linked immunoassay. We found that 5 μmol·L-1 anti-4-1BB antibody combined with 1 μmol·L-1 SeNPs increased the expression of IFN-γ and promoted the killing effects of peripheral blood mononuclear cells on Lewis lung carcinoma cells, with a lethality rate up to 56.88 %. Experiments in tumor-bearing mice showed that the tumor inhibition rate was 58.61 % after treatment with 3.5 mg/kg anti-4-1BB antibody combined with 0.25 mg/kg SeNPs, and the liver function index returned to normal. When the combined treatment was compared with the antibody treatment alone, detection of immune relevant factors demonstrated that the expression of FOXP3, IL-2, IL-12, and TNF-α in the spleen was downregulated, whereas the expression of IFN-γ in the spleen, serum, and tumor was upregulated, accompanied by increased Fas ligand expression in the tumor tissues. Based on these findings, we get the conclusion that anti-4-1BB antibody combined with SeNPs may alleviate the immunosuppression of regulatory T cells, promote the immune cell proliferation and metastasis to synergistically kill tumor cells. This combination also reduces the inflammatory damage to normal tissues and slows overstimulation of the splenic immune response.

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Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor\'s deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

Photothermal therapy (PTT), which utilizes nanomaterials to harvest laser energy and convert it into heat to ablate tumor cells, has been rapidly developed for lung tumor treatment, but most of the PTT-related nanomaterials are not degradable, and the immune response associated with PTT is unclear, which leads to unsatisfactory results of the actual PTT. Herein, we rationally designed and prepared a manganese ion-doped polydopamine nanomaterial (MnPDA) for immune-activated PTT with high efficiency. Firstly, MnPDA exhibited 57.2% photothermal conversion efficiency to accomplish high-efficiency PTT, and secondly, MnPDA can be stimulated by glutathione (GSH) to the release of Mn2+, and it can produce ·OH in a Fenton-like reaction with the overexpressed H2O2 and stimulate the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway. These two synergistically can effectively remove lung tumor cells that have not been ablated by PTT, resulting in an 86.7% tumor suppression rate under laser irradiation of MnPDA in vivo, and further significantly activated the downstream immune response, as evidenced by an increased ratio of cytotoxic T cells to immunosuppressive Treg cells. Conclusively, the GSH degradable MnPDA nanoparticles can be used for photothermal therapy and cGAS-STING-activated immunotherapy of lung tumors, which provides a new idea and strategy for the future treatment of lung tumors.

Computed tomography (CT)-guided thermal ablation is an emerging treatment method for lung tumors. Ablation needle path planning in preoperative diagnosis is of critical importance. In this work, we proposed an automatic needle path-planning method for thermal lung tumor ablation. First, based on the improved cube mapping algorithm, binary classification was performed on the surface of the bounding box of the patient\'s CT image to obtain a feasible puncture area that satisfied all hard constraints. Then, for different clinical soft constraint conditions, corresponding grayscale constraint maps were generated, respectively, and the multi-objective optimization problem was solved by combining Pareto optimization and weighted product algorithms. Finally, several optimal puncture paths were planned within the feasible puncture area obtained for the clinicians to choose. The proposed method was evaluated with 18 tumors of varying sizes (482.79 mm3 to 9313.81 mm3) and the automatically planned paths were compared and evaluated with manually planned puncture paths by two clinicians. The results showed that over 82% of the paths (74 of 90) were considered reasonable, with clinician A finding the automated planning path superior in 7 of 18 cases, and clinician B in 9 cases. Additionally, the time efficiency of the algorithm (35 s) was much higher than that of manual planning. The proposed method is expected to aid clinicians in preoperative path planning for thermal ablation of lung tumors. By providing a valuable reference for the puncture path during preoperative diagnosis, it may reduce the clinicians\' workload and enhance the objectivity and rationality of the planning process, which in turn improves the effectiveness of treatment.

UNASSIGNED: Despite adherence to guidelines, recurrence of lesions remains possible in lung tumor microwave ablation (MWA) even when termination is enabled by 5-10 mm ground glass changes. Limited evidence exists regarding the correlation between timely management of perioperative complications (including pneumothorax, pleural effusion, hemorrhage, cavity formation, and infection) and local tumor progression. This retrospective study aimed to investigate the relationship among peri-procedural factors, complications, and local tumor progression in 164 cases of lung tumors treated with computed tomography-guided MWA (CT-MWA), and improve the local prognosis and reduce the complication rate of CT-guided lung tumor ablation.
UNASSIGNED: We reviewed 164 consecutive patients who underwent CT-MWA at Fudan University Shanghai Cancer Center\'s Minimally Invasive Therapy Center for lung cancer from September 2019 to May 2020. Correlative analysis was performed between peri-procedural factors, complications and outcomes (local tumor progression rates). Patients who have had prior surgery or previous MWA were excluded. Ablation was the first treatment of choice, and all patients who have had other treatments were excluded. Patients were followed every 3 months with CT. Outcomes of ablation including complications and local tumor progression were evaluated. Peri-procedural factors included demographical factors, tumor features, ablation parameters, management of intra-procedural pneumothorax, and CT features. Complications included pneumothorax, post-procedural refractory infection, and pleural effusion.
UNASSIGNED: The study included 98 males and 68 females, with an average age of 56.1 years. Local tumor progression rate was negatively correlated with intra-procedural management of pneumothorax (R=-0.550, P=0.0003) and Hounsfield unit (HU) difference between HU before and after procedure (R=-0.855, P=0.006), and positively correlated with the average HU value of immediate post-procedural CT at the measurement points (R=0.857, P=0.00002). The correlation analysis results also showed a positive correlation between infection after procedure and pneumothorax (R=0.340, P=0.0001).
UNASSIGNED: A greater difference between HU before and after the procedure or a decrease in CT values immediately after ablation may predict a higher rate of local complete ablation. Prompt management of intraoperative pneumothorax may lower local tumor progression rates and decrease incidence of post-procedural infection.

The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols.
The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time.
To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and vascular structure and measure the distance between the nodule and vascular.
First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the difference between the two results.
In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink effect prediction, the positive rate of the algorithm group was 28.3%, and that of the expert group was 32.1%, with no significant difference between the two groups (p=0.687).
The algorithm model developed in this study shows good performance in predicting the heat sink effect during pulmonary thermal ablation. It can improve the speed and accuracy of nodule and vessel segmentation, save ablation planning time, reduce the interference of human factors, and provide more reference information for surgeons to make ablation plans to improve the ablation effect.

UNASSIGNED: Epstein-Barr virus (EBV) is a virus that is ubiquitous in humans. To investigate the association between EBV infection and lung cancer risk to reveal whether it is involved in the development and development of lung cancer. Although there has been discussion of EBV and lung cancer in the past. Through this study, we hope to deepen our understanding of the causes of lung cancer and provide new clues and targets for the prevention, early diagnosis and treatment of lung cancer. This study is also beneficial to the development of medical science and public health. First of all, the research results are expected to be incorporated into lung cancer prevention and treatment strategies and policies, so as to provide better treatment decisions for lung cancer patients and improve the survival rate and quality of life of patients. At the same time, communicating the research results to the public can help raise awareness of lung cancer risk factors. By encouraging healthy lifestyles and screening measures, the public can reduce their risk of lung cancer. In addition, this study also provides an important foundation for subsequent academic research and scientific exploration. It provides valuable information and inspiration for in-depth understanding of lung cancer and other related fields. Overall, this study makes an important contribution to both medical science and public health.
UNASSIGNED: By September 26, 2022, an online database was used to conduct a literature search in English. Random effects models were employed to estimate the prevalence of EBV with 95% confidence intervals (CIs). Additionally, the pooled odds ratio (OR) and 95%CI were calculated from case-control studies to determine the association between EBV and lung cancer.
UNASSIGNED: In this study of 886 patients with lung cancer, the overall prevalence of EBV infection was found to be 44.36% (95%CI: 4.08-16.9). Fourteen studies were included in the analysis, all of which used a case-control design and involved comparisons of tumors with adjacent or non-adjacent normal and non-cancerous controls. There was a significant difference in the prevalence of EBV infection in lung cancer tissues between China and other regions, with an odds ratio (OR) of 9.36 (95% confidence interval: 4.00-21.94, P<0.001, I²=73.5%). This suggests that the association between EBV infection and lung cancer cases is stronger in China than in other regions. Additionally, the prevalence of EBV infection varied across different pathological types of lung cancer, with rates of 81.08% for pulmonary lymphoepithelioma-like carcinoma (LELC),this a rare subtype of non-small cell lung cancer (NSCLC).34.78% for non-small cell lung cancer, and 21.17% for small cell lung cancer. The statistical analysis indicated that EBV infection was most significantly associated with cancer risk in LELC, while non-small cell lung cancer was more strongly associated with EBV than small cell lung cancer.
UNASSIGNED: The study found that EBV infection increases the risk of lung cancer by more than four times, and this risk is associated with the pathological type, lymphatic infiltration, and degree of differentiation of the lung cancer, particularly in the rare subtype of pulmonary lymphoepithelioma in non-small cell lung cancer(NSCLC). Additionally, there are racial and regional differences in the correlation between EBV-infected lung cancer, with the Asian population showing greater susceptibility. The study used normal or abnormal tissue adjacent to the tumor as a control, which is considered a more accurate method for determining the relationship between EBV infection and lung cancer.

Lung tumor PET and CT image fusion is a key technology in clinical diagnosis. However, the existing fusion methods are difficult to obtain fused images with high contrast, prominent morphological features, and accurate spatial localization. In this paper, an isomorphic Unet fusion model (GMRE-iUnet) for lung tumor PET and CT images is proposed to address the above problems. The main idea of this network is as following: Firstly, this paper constructs an isomorphic Unet fusion network, which contains two independent multiscale dual encoders Unet, it can capture the features of the lesion region, spatial localization, and enrich the morphological information. Secondly, a Hybrid CNN-Transformer feature extraction module (HCTrans) is constructed to effectively integrate local lesion features and global contextual information. In addition, the residual axial attention feature compensation module (RAAFC) is embedded into the Unet to capture fine-grained information as compensation features, which makes the model focus on local connections in neighboring pixels. Thirdly, a hybrid attentional feature fusion module (HAFF) is designed for multiscale feature information fusion, it aggregates edge information and detail representations using local entropy and Gaussian filtering. Finally, the experiment results on the multimodal lung tumor medical image dataset show that the model in this paper can achieve excellent fusion performance compared with other eight fusion models. In CT mediastinal window images and PET images comparison experiment, AG, EI, QAB/F, SF, SD, and IE indexes are improved by 16.19%, 26%, 3.81%, 1.65%, 3.91% and 8.01%, respectively. GMRE-iUnet can highlight the information and morphological features of the lesion areas and provide practical help for the aided diagnosis of lung tumors.

The standard four-dimensional (4D) treatment planning includes all breathing states in the optimization process, which is time-consuming. This work was aimed to optimize the number of intermediate phases needed for 4D proton treatment planning optimization to reduce the computational cost. Five 4D optimization strategies adopting different numbers of intermediate states and one three-dimensional (3D) optimization plan were studied for fifteen lung cancer patients treated with scanned protons, optimizing on all ten phases (4D_10), two extreme phases (4D_2), six phases during the exhalation stage (4D_6EX), six phases during the inhalation stage (4D_6IN), two extreme phases plus an intermediate state (4D_3) and average computed tomography image (3D), respectively. The 4D dose evaluation was conducted on all the ten phases, considering the interplay effect. The resulting doses accumulated on the reference phase were computed and compared. Compared to the 4D optimization plans, the 3D optimization plan performed inferiorly in target coverage, but superiorly in organ at risks (OARs) sparing. For the 4D optimization, all the five 4D plans showed similar performance in OARs protection. However, the 4D_6EX and 4D_6IN strategies out-performed the 4D_2 and 4D_3 plans in dose homogeneity. The computing times of the 4D_2, 4D_3, 4D_6EX and 4D_6IN approaches decreased to 32%, 41%, 66% and 67% of the 4D_10 method, respectively. Thus, our study suggested that the use of all phases during inhalation or exhalation stage might be a feasible approach substituting for the full phase strategy to reduce the calculation load while guaranteeing the plan quality for scanned proton therapy.