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Dataset results
90 results for “Clinical oncology”
A Non-interventional, International, Multicentre Clinical Research Study to Build the Largest Collection of Multimodal Data (Including Clinical Data, Imaging Data and Omics Data) in Oncology
ClinicalTrials.gov study NCT06625203. IPD Sharing: YES. Countries: 4. Publications: 7.
Effect of Multi-Media Tool on Enrollment in Oncology Clinical Trials
ClinicalTrials.gov study NCT02020252. IPD Sharing: NO. Countries: 1. Publications: 1.
Cantemist corpus: gold standard of oncology clinical cases annotated with CIE-O 3 terminology
<p><strong>Intro:</strong></p> <p>Cantemist shared task dataset (divided in train, dev1, dev2 and test). In addition, we include here the Cantemist background set.</p> <p>It contains the train, development and test sets of the three subtasks: cantemist-ner, cantemist-norm and cantemist-coding with Gold Standard annotations.</p> <p>In addition, it contains the documents of the background set, without annotations.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</p> <pre><code>@inproceedings{miranda2020named, title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results}, author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M}, booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p><strong>Format:</strong></p> <p>For subtasks cantemist-norm and cantemist-ner, annotations are distributed in Brat format. See <a href="https://brat.nlplab.org/standoff.html">Brat webpage</a> for more information</p> <p>For subtask cantemist-coding, codes are grouped in a TSV file with the following columns (this follows the format used in <a href="https://temu.bsc.es/codiesp/">CodiEsp shared task</a>): </p> <blockquote> <p>filename code</p> </blockquote> <p> </p> <p><strong>Shared task goal:</strong></p> <p>In the three subtasks, the goal will be to predict the annotations (either the ANN files or the TSV with the codes) given only the plain text files. </p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/cantemist/">Web</a></strong></li> <li><strong><a href="http://ceur-ws.org/Vol-2664/cantemist_overview.pdf">Citation</a>: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://doi.org/10.5281/zenodo.4010899">Silver Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>For further information, please visit <a href="https://temu.bsc.es/cantemist/">https://temu.bsc.es/cantemist/</a> or email us at encargo-pln-life@bsc.es</p>
Cantemist Silver Standard: Participant predictions in SEPLN IberLEF2020 - Spanish oncology clinical cases coded in ICD-O
<p><strong>Introduction</strong></p> <p>Predictions in the background set of Cantemist participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per Cantemist subtask. Within each Cantemist subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong></p> <p>The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks Cantemist-NER and Cantemist-Norm, the files are in Brat format.</p> <p>For the sub-track Cantemist-Coding files have the following fields:</p> <pre>articleID ICDO-code </pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/cantemist/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><a href="https://doi.org/10.5281/zenodo.3773228"><strong>Gold Standard corpus</strong></a></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to Cantemist participants. </p> <p> </p> <p>For more information, visit the track webpage: <a href="http://temu.bsc.es/cantemist/">http://temu.bsc.es/cantemist/</a> or email us at encargo-pln-life@bsc.es</p>
Genomic profiling of NSCLC tumors with the TruSight Oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel associations between genomic alterations, TMB, and PD-L1
<p>Wallen ZD, Ko H, Nesline MK, Tierno M, Roos A, Schnettler E, Husain H, Sathyan P, Caveney B, Eisenberg M, Severson EA, Ramkissoon SH. <strong>Genomic profiling of NSCLC tumors with the TruSight Oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel associations between genomic.</strong> <em>Front Oncol.</em> 2024 Nov 5;14:1473327. doi: <a href="https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1473327">10.3389/fonc.2024.1473327</a>.</p> <p><strong>ABSTRACT</strong></p> <p><strong>Introduction: </strong>Matching patients to an effective targeted therapy or immunotherapy is a challenge for <br>advanced and metastatic non-small cell lung cancer (NSCLC), especially when relying on assays that test one <br>marker at a time. Unlike traditional single marker tests, comprehensive genomic profiling (CGP) can <br>simultaneously assess NSCLC tumors for hundreds of genomic biomarkers and markers for immunotherapy <br>response, leading to quicker and more precise matches to therapeutics. <strong>Methods: </strong>In this study, we performed <br>CGP on 7,606 patients with advanced or metastatic NSCLC using the Illumina TruSight Oncology 500 (TSO <br>500) CGP assay to show its coverage and utility in detecting known and novel features of NSCLC. <strong>Results: </strong><br>Testing revealed distinct genomic profiles of lung adenocarcinoma and squamous cell carcinomas and <br>detected variants with a current targeted therapy or clinical trial in >72% of patient tumors. Known associations <br>between genomic alterations and immunotherapy markers were observed including significantly lower TMB <br>levels in tumors with therapy-associated alterations and significantly higher PD-L1 levels in tumors with ALK, <br>MET, BRAF, or ROS1 driver mutations. Co-occurrence analysis followed by network analysis with gene <br>module detection revealed known and novel co-occurrences between genomic alterations. Further, certain <br>modules of genes with co-occurring genomic alterations had dose-dependent relationships with histology and <br>increasing or decreasing levels of PD-L1 and TMB, suggesting a complex relationship between PD-L1, TMB, <br>and genomic alterations in these gene modules. <strong>Discussion:</strong> This study is the largest clinical study to date <br>utilizing the TSO 500. It provides an opportunity to further characterize the landscape of NSCLC using this <br>newer technology and show its clinical utility in detecting known and novel facets of NSCLC to inform treatment <br>decision-making.</p> <p><strong>DATA AVAILABILITY</strong></p> <p>The data and code presented in the study are deposited in this Zenodo repository, accession number <br>13137232 (<a href="https://zenodo.org/record/13137232">https://zenodo.org/record/13137232</a>). Raw sequencing data were derived from routine clinical testing of real-world patients and cannot be shared publicly. Further data inquiries can be directed to the corresponding <br>author. </p>
Influence of Psychosocial Distress and Lifetime Trauma Exposure on Traumatic Stress Among Oncology Patients on Clinical Trials
ClinicalTrials.gov study NCT02948413. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Prehabilitation Plus ERAS Versus ERAS in Gynecologic Oncology: a Randomized Clinical Trial
ClinicalTrials.gov study NCT04596800. IPD Sharing: YES. Countries: 1. Publications: 10.
Clinical Research Platform on Decision Making and Clinical Impact of Biomarker-Driven Precision Oncology
ClinicalTrials.gov study NCT04389541. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical and Functional Variables in Oncology
ClinicalTrials.gov study NCT03879096. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Identifying, Understanding, and Overcoming Barriers to the Use of Clinical Practice Guidelines in Pediatric Oncology
ClinicalTrials.gov study NCT02847130. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Do Patients Participating In Oncology Clinical Trials Understand the Informed Consent Form?
ClinicalTrials.gov study NCT01772511. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prospective Cohort Study to Describe the Clinical Characteristics of COVID-19, the Acquired Immune Response and the Biological and Clinical Parameters of Patients Followed in Oncology by the Saint-Jos
ClinicalTrials.gov study NCT04437719. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Mutational Oncology in Clinical Practice
ClinicalTrials.gov study NCT06020625. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Clinical Benefits of Preoperative Nutrition Support With Enteral Immune-enhancing Formulas in Surgical Oncology
ClinicalTrials.gov study NCT01032512. IPD Sharing: Not stated. Countries: 1. Publications: 1.
TREATMENT AND MONITORING PATTERNS AND CLINICAL OUTCOMES IN PATIENTS RECEIVING PALBOCICLIB COMBINATION TREATMENT (WITH AI OR FULVESTRANT) FOR HR+/HER2- A/MBC IN A COMMUNITY ONCOLOGY SETTING.
ClinicalTrials.gov study NCT04498481. IPD Sharing: NO. Countries: 1. Publications: 1.
PATIENT VOICES Integration of Systematic Assessment of Patient Reported Outcomes Within Clinical Oncology Practice
ClinicalTrials.gov study NCT03968718. IPD Sharing: NO. Countries: 1. Publications: 2.
Is Radiation-before-pathology a Feasible Approach in the Palliative Oncology Setting? A Pragmatic Clinical Trial
ClinicalTrials.gov study NCT06156800. IPD Sharing: NO. Countries: 1. Publications: 1.
Feasibility of Quality of Life Assessment in Routine Clinical Oncology Practice at the University Hospital of Besancon
ClinicalTrials.gov study NCT02844608. IPD Sharing: NO. Countries: 1. Publications: 13.
Integration of Neurocognitive Biomarkers Into a Neuro-Oncology Clinic
ClinicalTrials.gov study NCT05504681. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
The Safety and Efficacy of Enhanced Recovery After Surgery on Clinical and Immune Outcomes for Gynecological Oncology
ClinicalTrials.gov study NCT03640299. IPD Sharing: NO. Countries: 1. Publications: 8.
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.