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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
Sponsor:
Information provided by (Responsible Party):
Jun Wang, Peking University People's Hospital

Brief Summary:

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

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Study Type : Observational
Estimated Enrollment : 1000 participants
Observational Model: Case-Control
Time Perspective: Retrospective
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

Resource links provided by the National Library of Medicine



Intervention Details:
  • 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.


Primary Outcome Measures :
  1. 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.



Information from the National Library of Medicine

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Ages Eligible for Study:   20 Years to 75 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
500 cases will be enrolled including 100 cases of benign pulmonary nodules, 300 cases of stage I and II lung cancer, 100 cases of stage III lung cancer. All enrolled patients are newly diagnosed as pulmonary nodules by imaging, benign and malignant conditions of the nodules are determined by postoperative pathology after surgical resection. All clinacal data including cancer stage information are available.
Criteria

Inclusion Criteria:

  • Enrolled patients are newly diagnosed patients
  • In patients diagnosed as pulmonary nodules by imaging, benign and malignant conditions of the nodules are determined by postoperative pathology after surgical resection
  • There is clear cancer stage information
  • In addition to pulmonary nodules, there are no suspicious nodules of other organs
  • No previous history of malignant tumor

Exclusion Criteria:

  • Patients with a history of malignant tumor
  • Patients with suspectednodules in other parts of the body at the time of diagnosis
  • Patients who have previously received surgery, chemotherapy or radiotherapy for pulmonary lesions
  • Patients with severe blood lipid in peripheral blood extracted which affects subsequent detection

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04558255


Contacts
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Contact: Kezhong Chen, M.D. +8613488752289 mdkzchen@163.com

Locations
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China, Beijing
Peking University People's Hospital Recruiting
Beijing, Beijing, China, 100044
Contact: Chen Kezhong, M.D.         
Sponsors and Collaborators
Peking University People's Hospital
Investigators
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Study Director: Jun Wang, M.D. Peking University People's Hospital
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Responsible Party: Jun Wang, Director of the Thoracic Surgery Department, Peking University People's Hospital
ClinicalTrials.gov Identifier: NCT04558255    
Other Study ID Numbers: PTHO1903
First Posted: September 22, 2020    Key Record Dates
Last Update Posted: September 22, 2020
Last Verified: September 2020

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Lung Neoplasms
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site
Neoplasms
Lung Diseases
Respiratory Tract Diseases