A Preliminary Study on the Detection of Plasma Markers in Early Diagnosis for Lung Cancer
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|ClinicalTrials.gov Identifier: NCT04558255|
Recruitment Status : Recruiting
First Posted : September 22, 2020
Last Update Posted : September 22, 2020
Lung cancer is the most common cancer with the highest morbidity and mortality in the world. Stagement is closely related to the 5 years of survival rate of patients. The postoperative 5-year survival rate is above 90% for stage ⅠA lung cancer patients, while the 5-year survival rate of stage IV lung cancer patients is less than 5%. Therefore, early screening and diagnosis for lung cancer is a key method to reduce lung cancer mortality and prolong survival for patients.
At present, low-dose computed tomography (LDCT) is the most effective method for early detection of lung cancer. In addition to imaging examination, plasma tumor markers detection is also a common clinical detection method for tumor screening and postoperative monitoring.
Liquid biopsy is a non-invasive or minimally invasive method for testing blood or other liquid samples to analyze tumor-related markers including nucleic acids and proteins. Several studies have explored the detection of hot spot gene mutations, methylation and methylation changes of DNA, protein markers and autoantibodies in peripheral blood in lung cancer patients. Liquid biopsy has generally become the most popular field for early diagnosis of lung cancer.
Based above, it is necessary to combine multi-omics methods to improve the detection of early stage lung cancer. In our study, we intend to integrate molecular features obtained through liquid biopsy and clinical data of lung cancer patients, and develop and prospectively validate a machine-learning method which can robustly discriminate early-stage lung cancer patients from controls.
|Condition or disease||Intervention/treatment|
|Lung Cancer||Diagnostic Test: A machine-learning method which can robustly discriminate early-stage lung cancer patients from controls|
|Study Type :||Observational|
|Estimated Enrollment :||1000 participants|
|Official Title:||Plasma Biomarkers as a Non-invasive Approach for Early Diagnosis of Lung Cancer|
|Actual Study Start Date :||January 1, 2020|
|Estimated Primary Completion Date :||December 1, 2020|
|Estimated Study Completion Date :||December 1, 2021|
- Diagnostic Test: A machine-learning method which can robustly discriminate early-stage lung cancer patients from controls
In our study, we intend to integrate molecular features obtained through liquid biopsy and clinical data of lung cancer patients, and develop and prospectively validate a machine-learning method which can robustly discriminate early-stage lung cancer patients from controls.
- Rates of malignant and benign pulmonary nodules measured by the postoperative pathology [ Time Frame: 5 days after the surgery ]After the sugery of each patients with pulmonary nodules, we will get the clinicopathologic characteristics of the patients. Tumor stage and grade will be evaluated by us and rates of malignant and benign pulmonary nodules will be the primary outcome which we follow.
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Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04558255
|Contact: Kezhong Chen, M.D.||+firstname.lastname@example.org|
|Peking University People's Hospital||Recruiting|
|Beijing, Beijing, China, 100044|
|Contact: Chen Kezhong, M.D.|
|Study Director:||Jun Wang, M.D.||Peking University People's Hospital|