A Preliminary Study on the Detection of Plasma Markers in Early Diagnosis for Lung Cancer

Lung Cancer Clinical Trial

Official title:

Plasma Biomarkers as a Non-invasive Approach for Early Diagnosis of Lung Cancer

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04558255
• Other study ID #: PTHO1903
• Status: Recruiting
• Start date: January 1, 2020
• Est. completion date: December 1, 2021

Study information

• Verified date: September 2020
• Source: Peking University People's Hospital
• Contact: Kezhong Chen, M.D.
• Phone: +8613488752289
• Email: mdkzchen[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: December 1, 2021
• Est. primary completion date: December 1, 2020
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 20 Years to 75 Years

Eligibility

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

Related Conditions & MeSH terms

• Lung Cancer
• Lung Neoplasms

Intervention

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.

Locations

Country	Name	City	State
China	Peking University People's Hospital	Beijing	Beijing

Sponsors (1)

Lead Sponsor	Collaborator
Peking University People's Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Rates of malignant and benign pulmonary nodules measured by the postoperative pathology	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.	5 days after the surgery