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8,187 results for “Lung cancer”
Phenethyl Isothiocyanate in Preventing Lung Cancer in Smokers
ClinicalTrials.gov study NCT00691132. IPD Sharing: NO. Countries: 1. Publications: 2.
HINC dataset from: Homeopathic treatment as an add-on therapy may improve quality of life and prolong survival in patients with non-small cell lung cancer: A prospective, randomized, placebo-controlled, double-blind, three-arm, multicenter study
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Commonly asked questions for lung cancer screening
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Single-cell multi-modal analysis of tumor microenvironment in human non-small cell lung cancer tissues
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MALDI-MS raw files of primary human lung cancer samples, lung cancer patient derived xenografts and lung cancer mouse models
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Innate immune activation by checkpoint inhibition in patient-derived lung cancer tissues
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Molecular dynamics dataset for pharmacological repositioning in the treatment of non-small-cell lung cancer
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Data from: Association of chronic obstructive pulmonary disease with risk of lung cancer in individuals aged 40 years and older: A cross-sectional study based on NHANES 2013-2018
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Data from: Gal-1 promotes lung cancer cell survival by enhancing PARP1/H1.2 interaction to promote DNA repair upon DNA damage response
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Data from: Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps - a feasibility study
Objective: The aim of this study was to prospectively evaluate the feasibility of monitoring treatment response to chemotherapy in patients with non-small cell lung carcinoma using functional diffusion maps (fDMs). Materials and Methods: This study was approved by the Cantonal Research Ethics Committee and informed written consent was obtained from all patients. Nine patients (mean age = 66 years; range = 53–76 years, 5 females, 4 males) with overall 13 lesions were included. Imaging was performed within two weeks before initiation of chemotherapy and at one, two, and six weeks after initiation of chemotherapy. Imaging included a respiratory-triggered diffusion-weighted sequence including three b-factors (100, 600, and 800 s/mm2). Treatment response was defined by change in tumor diameter on computed tomography (CT) after two cycles of chemotherapy. Changes in the apparent diffusion coefficient (ADC) on a per-lesion basis and the percentages of voxel with significantly increased or decreased ADCs on fDMs were analyzed using repeated measures analysis of variance (ANOVA). Changes in tumor size were used as covariate to examine the ability of ADCs and fDM parameters to predict treatment response. Results: Repeated measures ANOVA revealed that the percentage of voxels with increased ADCs on fDMs (p = 0.002) as well as the mean ADC increase (p = 0.011) were significantly higher in good responders with a large reduction in tumor size on CT. Conclusion: Our results indicate that the percentage of voxels with significantly increased ADCs on fDMs seems to be a promising biomarker for early prediction of treatment response in patients with non-small cell lung carcinoma. Contrary to averaged values, this approach allows the spatial heterogeneity of treatment response to be resolved.
MIHIC: A multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification
<p>A cohort of 47 TMA sections from 114 patients was collected from Liaoning cancer hospital \& Institute, where each TMA section has the size of 188,416$\times$110,080 pixels (i.e., 42660.87um$\times$24924.15um) at 40$\times$ magnification. TMA sections contain different number of tissue cores, ranging from 28 to 48. After excluding poor quality TMA sections with tissue folding, missing or contamination, there are totally 114 patients. Each patient has tissue cores with 12 different IHC stains, including CD3, CD20, CD34, CD38, CD68, CDK4, cyclin-D1, D2-40, FAP, Ki67, P53, and SMA. Two pathologists have manually labeled clear tissue regions (i.e., without controversy) in TMA sections based on visual examination via Qupath software, where six tissue types including Alveoli, Immune cells, Nerosis, Other, Stroma, Tumor were annotated. Besides the annotated six tissue types, we added one more Background type.</p> <p>To build histological classification models, we split 309,698 image patches in MIHIC dataset into three sets: training, validation and test. Note that image patches extracted from the same annotated tissue region are distributed into the same set, which avoids data leakage during classification model optimization. According to the number of extracted ROIs, train, val and test accounted for 64\%, 16\% and 20\%.</p> <h1>if you use this dataset, please cite:</h1> <pre>@article{wang2024mihic, title={MIHIC: a multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification}, author={Wang, Ranran and Qiu, Yusong and Wang, Tong and Wang, Mingkang and Jin, Shan and Cong, Fengyu and Zhang, Yong and Xu, Hongming}, journal={Frontiers in Immunology}, volume={15}, year={2024}, publisher={Frontiers Media SA} }</pre>
Awareness of Lung Cancer Risk Factors and Symptoms in Syria: An Online Cross-Sectional Study
<p><strong>Background:</strong> Globally, lung cancer is the leading cause of cancer fatalities and the second most frequent cancer. Population knowledge of the features of lung cancer is a crucial strategy for early diagnosis and decreasing the mortality rate of lung cancer patients. In this study, we aim to assess the Syrian population's knowledge of lung cancer and its risk factors and to measure awareness of symptoms related to lung cancer. <strong>Methods:</strong> This national cross-sectional study was conducted between October 12 to November 21, 2022 in Syria. We included Syrian people above 18 years from all Syrian governorates. The questionnaire consisted of three categories of questions: sociodemographic information, awareness of lung cancer symptoms, and awareness of lung cancer risk factors. <strong>Results:</strong> Overall, 2251 participants were involved in this research; almost half of them (47.3%) were aged between 21-30 years, and 30.9% indicated they are smoking cigarettes. The overall mean score of knowledge regarding closed questions about risk factors of lung cancer was 4.29; however, the mean score of knowledge regarding open questions about symptoms of lung cancer was 1.52. About half of the study sample (51.3%) indicated that unexplained weight loss is a possible symptom of lung cancer. Our findings showed that cigarette smokers have a lower probability of having adequate knowledge toward lung cancer risk factors than a non-smoker (AOR=0.73, COR=0.68, P-value<0.05). We also defined that females have higher statistically significant odds (AOR=1.3, COR=1.38, P-value<0.05) for being knowledgeable about the symptoms of lung cancer compared to the male sample study. Conclusion: According to our findings, there is inadequate knowledge toward lung cancer risk factors and moderate knowledge of lung cancer symptoms. Along with educational programs to raise public knowledge of the dangers of smoking and other LC risk factors, effective tobacco control policy execution is crucial. </p>
Genomic features of lung cancer patients in Indonesia's National Cancer Center
<p><strong>Introduction:</strong> Advances in molecular biology bring advantages to lung cancer management. Moreover, high-throughput molecular tests are currently useful for revealing genetic variations among lung cancer patients. We investigated the genomics profile of the lung cancer patients at the National Cancer Centre of Indonesia.</p> <p><strong>Methods:</strong> A retrospective study enrolled 627 tissue biopsy samples using real time polymerase chain reaction (RT-PCR) and 80 circulating tumour DNA (ctDNA) liquid biopsy samples using next-generation sequencing (NGS) from lung cancer patients admitted to the Dharmais Cancer Hospital from January 2018 to December 2022. Data were obtained from medical records. Data statistically analysed with p<0.05 is considered significant.</p> <p><strong>Result:</strong> The <em>EGFR</em> test results revealed by RT-PCR were wild type (51.5%), single variant (38.8%), double variant (8.3%), and triple variant (1.4%), with 18.66% L85R, 18.22% Ex19del, and 11.08% L861Q variant. Liquid biopsy ctDNA using NGS showed only 2.5% <em>EGFR</em> wild type, 62.5% single variant and 35% co-variant, with <em>EGFR/TP53</em> and <em>EGFR/PIK3CA</em> as the highest.</p> <p><strong>Conclusion:</strong> EGFR variants are the most found in our centre. Liquid biopsy with ctDNA using NGS examination could detect broad variants and co-variants that will influence the treatment planning.</p>
A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation (DICOM VERSION)
<p>We constructed a groundbreaking lung CT dataset, which includes 330 annotated nodules from 95 patients. It is worth noting that we have integrated the results of patient clinical diagnosis, frozen diagnosis, and pathological diagnosis, supplementing this with labeled lung cancer types on 308 samples containing nodules.</p>
Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
CT dataset for "Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer" Part 2
<p>To develop and validate a radiomics-based method for lung cancer detection, chest CT images of patients with pulmonary nodules from clinical 5 centers were collected. We hope this large-scale dataset could facilitate both clinical research for automatic lung cancer detection and diagnoses, and engineering research for 3D detection, segmentation and classification. Due to size limit of zenodo.org, we split the whole CT images sets into 2 parts; This is the Part 2.</p> <p>This dataset is a research effort of thousands of hours by experienced thoracic surgeons, and radiologists. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p>
Identification of biomarkers for the earlier detection of lung cancer: a systematic review and meta-analysis
<p>All data analysed during this study are included in this excel sheet showing the steps in this systematic literature review process.</p>
Proteomic and Metabolomic Profiling of Plasma Predict Immune-related Adverse Events in Older Patients with Advanced Non-small Cell Lung Cancer
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Characterizing NSD3 Amplification in Lung Cancer
<p><strong>SGC Open Notebook Project to Characterize the HMTase NSD3</strong></p> <p><strong>Exp020 Objective: </strong>NSD3 (WHSC1L1) is amplified in ~5% of Non-Small Cell Lung Cancer patients( cBioPortal : Cerami et<br> al. Cancer Discov. 2012 and Gao et al. Sci. Signal. 2013). However, the implications of this event on the<br> formation and progression of the disease are unclear. While NSD3 may be a driver of lung cancer, it is also<br> plausible that this locus is simply amplified at a higher frequency in the context of cancer-associated genomic<br> instability. To dive deeper into this question I will use The Cancer Genome Atlas (TCGA) lung cancer<br> data-sets to look for associations between NSD3 amplification and mutational status as well as gene expression<br> profiles. This data has been generated by the TCGA Research Network: http://cancergenome.nih.gov/. I<br> hypothesize that if NSD3 amplification is a driving force in a subset of lung tumors, these samples will share<br> similar gene expression profiles and exhibit higher expression levels of NSD3. Here, I am using FirebrowserR<br> (Deng M., et al. Database. 2017 - PMID:28062517), an R client for Broad Institute’s Firehose Web API,<br> which allows TCGA data processed by the Firehose Pipeline to be directly imported into R for analysis.</p>
NSD3-Short Promotes Migration of A549 Lung Cancer Cells
<p><strong>SGC Open Notebook Project to Characterize the HMTase NSD3</strong></p> <p><strong>Exp025 Objective:</strong> In a previous experiment (exp024), we observed an increase in E-cadherin expression in response to knockdown of the short isoform of NSD3. To determine if this change is functionally relevant, we performed wound healing assays to measure any corresponding alteration in the migratory potential of A549 lung epithelial cancer cells.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.