Clinical Trials and Modern Advertising: The Lung Cancer Trials Flowchart

2006 ◽  
Vol 18 (5) ◽  
pp. 376-377
Author(s):  
R. Stephens ◽  
P. Hoskin
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Cancer Trials

scholarly journals Current clinical trials and patent update on lung cancer: a retrospective review

2021 ◽  
pp. LMT45
Author(s):  
Harshul Batra ◽  
Shrikant Pawar ◽  
Dherya Bahl
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Data Analysis ◽  
Main Study ◽  
Phase Ii Trials ◽  
Comprehensive Description ◽  
Multicenter Studies ◽  
The Usa ◽  
Cancer Trials ◽  
Patent Filing

Several clinical trials using different interventions are currently being sponsored to combat lung cancer at its different stages. The purpose of this study was to provide a portfolio of those trials. All active, open and recruiting clinical trials registered at ClinicalTrials.gov up to March 2018 were included. Information related to 6092 registered lung cancer trials was downloaded. Phase II trials were in the majority, comprising nearly 48.7% of total clinical trials with industry the major sponsor (41.3%) followed by NIH (12.3%). Multicenter studies were the norm accounting for 47.9% and the main study location was the USA (50.9%). Common interventions were radiation (26%), surgery (22%) and EGFR inhibitors (17%). Patent information includes major patent filing office and sponsors. The data analysis provides a comprehensive description of lung cancer trials.

Anticoagulation and bleeding: A pooled analysis of lung cancer trials of the National Cancer Institute of Canada Clinical Trials Group

2008 ◽  
Vol 26 (15_suppl) ◽  
pp. 8065-8065
Author(s):  
A. Le Maître ◽  
K. Ding ◽  
F. A. Shepherd ◽  
N. B. Leighl ◽  
A. Arnold ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
National Cancer Institute ◽  
Pooled Analysis ◽  
Cancer Trials

Representing cancer clinical trial criteria and attributes using ontologies: An NLP-assisted approach.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14079-e14079
Author(s):  
Kyeryoung Lee ◽  
Zhongzhi Liu ◽  
Meng Ma ◽  
Chris Gilman ◽  
Yun Mai ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Lung Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Deep Learning ◽  
Small Cell ◽  
Learning Models ◽  
Small Cell Lung ◽  
Eligibility Criteria ◽  
Cancer Trials

e14079 Background: Low patient recruitment is one of the main reasons clinical trials fail. Identifying eligible patients for clinical trials using electric health records (EHRs) can help reach accrual targets. Ontology reasoning implemented in Trial2Patient, a scalable system we developed for matching patient to clinical trials, forms the basis for generating patient cohorts in our system. For efficient cohort definition, an attribute ontology for eligibility criteria and entity categorization is a necessary first step. To meet this requirement, we constructed an ontology platform for lung cancer trials. Methods: We classified 128 non-small cell lung cancer and 38 small cell lung cancer trials into different therapy groups. Among the 166 trials we examined, 110 were immuno-oncology therapy-based, 48 were targeted therapy-based, and 8 were chemotherapy or device trials. We analyzed the eligibility criteria for each trial manually to identify entities from all trials as well as indication specific and further therapy group specific entities. To incorporate a semi-automated, natural language process (NLP)-assisted named entity recognition (NER) into the future cohort definition process, we trained NLP and deep learning models for NER and ontology encoding. Attributes generated from 50 processed NSCLC trials were evaluated with our manually curated attributes. The ontology generated from lung cancer was tested in 74 prostate cancer trials for generalizability. Results: The ontology for lung cancer trials, which is generalizable to prostate cancer and other cancer clinical trials, were constructed. Total 507 attributes were extracted and entities were categorized into 8 groups. Evaluation of attributes generated by NLP and deep learning models compared with manually extracted attributes showed high consistency and accuracy. The average precision, recall and F1 values of 15 most commonly appearing entities (disease, histology, targeted therapy, immunotherapy, radiotherapy, neoadjuvant therapy, age, gender, test, vitals, value, drug, gene, mutation, problem) are 0.873, 0.769, and 0.805, respectively. Conclusions: We contribute to a clinical trial ontology platform for lung cancer and prostate cancer trial recruitment. This ontology platform can be expanded to other solid tumors or hematologic malignancies for clinical trial analysis, and can also be applied to generate synthetic control arm cohorts. We believe NLP-assisted NER can be successfully incorporated for the future work of large scale of clinical trial cohort definition.

scholarly journals Anticoagulation and Bleeding: A Pooled Analysis of Lung Cancer Trials of the NCIC Clinical Trials Group

2009 ◽  
Vol 4 (5) ◽  
pp. 586-594 ◽  
Author(s):  
Aurélie Le Maître ◽  
Keyue Ding ◽  
Frances A. Shepherd ◽  
Natasha Leighl ◽  
Andrew Arnold ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Pooled Analysis ◽  
Cancer Trials

scholarly journals Publication Rate and Characteristics of Lung Cancer Clinical Trials

2019 ◽  
Vol 2 (11) ◽  
pp. e1914531
Author(s):  
Ghassan Al-Shbool ◽  
Hira Latif ◽  
Saira Farid ◽  
Shuqi Wang ◽  
Jaeil Ahn ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Publication Rate ◽  
Cancer Clinical Trials

scholarly journals The changing landscape of anti-lung cancer drug clinical trials in mainland China from 2005 to 2020

2021 ◽  
Vol 11 ◽  
pp. 100151
Author(s):  
Qiaofeng Zhong ◽  
Yunxia Tao ◽  
Haizhu Chen ◽  
Yu Zhou ◽  
Liling Huang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Mainland China ◽  
Cancer Drug
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