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5,054
datasets available to search
ShareScore release 0.9.0
Dataset results
5,054 results for “small cell lung cancer”
Cetuximab as Therapy for Recurrent Non-Small Cell Lung Cancer Patients Who Have Received Prior Therapy
ClinicalTrials.gov study NCT00694603. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Paclitaxel Albumin-Stabilized Nanoparticle Formulation and Carboplatin in Treating Patients With Stage IIIB, Stage IV, or Recurrent Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00729612. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Allogeneic Cellular Vaccine 1650-G for Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00654030. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Carboplatin and Gemcitabine With Bevacizumab Every 2 Weeks for Stage IIIb/IV Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT00400803. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Chemotherapy for Patients With Non-Small Cell Lung Cancer (NSCLC)
ClinicalTrials.gov study NCT00391274. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Cancer Vaccine Study for Unresectable Stage III Non-small Cell Lung Cancer (START)
ClinicalTrials.gov study NCT00409188. IPD Sharing: Not stated. Countries: 33. Publications: 4.
Study of Lorlatinib In People With ALK-positive Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT04362072. IPD Sharing: YES. Countries: 7. Publications: 0.
Cisplatin/Etoposide/Radiotherapy +/- Consolidation Docetaxel in Advanced Stage III Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00216125. IPD Sharing: NO. Countries: 1. Publications: 1.
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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Single-cell multi-modal analysis of tumor microenvironment in human non-small cell 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: 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.
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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Immunotherapy-mediated thyroid dysfunction: genetic risk and impact on outcomes with PD-1 blockade in non-small cell lung cancer
<p>Polygenic risk score weights derived using LDpred for hypothyroidism and thyroid medication use.</p>
Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade
<p>Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligand (PD-L1) blockade. We aimed to use deep-learning algorithm to estimate clinical benefit in patients with non-small-cell lung cancer (NSCLC) before immunotherapy. Peripheral blood samples or tumor tissues of 915 patients from three independent centers were profiled by whole-exome sequencing or next-generation sequencing. Based on convolutional neural network (CNN) and three conventional machine learning (cML) methods, we used multi-panels to train the models for predicting the durable clinical benefit (DCB) and combined them to develop a nomogram model for predicting prognosis. In the three cohorts, the CNN achieved the highest area under the curve of predicting DCB among cML, PD-L1 expression, and tumor mutational burden (area under the curve [AUC] = 0.965, 95% confidence interval [CI]: 0.949–0.978, <em>P</em> < 0.001; AUC =0.965, 95% CI: 0.940–0.989, <em>P</em> < 0.001; AUC = 0.959, 95% CI: 0.942–0.976, <em>P</em> < 0.001, respectively). Patients with CNN-high had longer progression-free survival (PFS) and overall survival (OS) than patients with CNN-low in the three cohorts. Subgroup analysis confirmed the efficient predictive ability of CNN. Combining three cML methods (CNN, SVM, and RF) yielded a robust comprehensive nomogram for predicting PFS and OS in the three cohorts (each <em>P</em> < 0.001). The proposed deep-learning method based on mutational genes revealed the potential value of clinical benefit prediction in patients with NSCLC and provides novel insights for combined machine learning in PD-1/PD-L1 blockade.</p>
Elimination of PTV Margins Based on MRI-guided Adaptive Stereotactic Radiotherapy for Non-small Cell Lung Cancer With Brain Metastasis
ClinicalTrials.gov study NCT06582940. IPD Sharing: NO. Countries: 1. Publications: 8.
Tempus Sculptor Study: Small Cell Lung Cancer (SCLC) Observational Study
ClinicalTrials.gov study NCT05257551. IPD Sharing: Not stated. Countries: 1. Publications: 21.
Methylprednisolone After Split-course Chemoradiotherapy For Bulky Local Advanced None-small Cell Lung Cancer
ClinicalTrials.gov study NCT03661567. IPD Sharing: UNDECIDED. Countries: 1. Publications: 16.
Early Specialized Cardiovascular Intervention Based on Impedance Cardiography in Locally Advanced Non-small Cell Lung Cancer Patients
ClinicalTrials.gov study NCT04980716. IPD Sharing: Not stated. Countries: 1. Publications: 25.
Combination Chemotherapy With or Without Bevacizumab in Treating Patients With Advanced, Metastatic, or Recurrent Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00021060. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.