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

NCT04558255 | Unknown

• 1 other identifier: PTHO1903
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Lung Cancer

Outcome Measures

Primary Outcomes (1)

• 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

Interventions

A machine-learning method which can robustly discriminate early-stage lung cancer patients from controls DIAGNOSTIC_TEST

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.

Eligibility Criteria

• Age: 20 Years - 75 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Peking University People's Hospital: lead

Study Sites (1)

Peking University People's Hospital

Beijing, Beijing Municipality, 100044, China

RECRUITING

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Jun Wang, M.D.

  Peking University People's Hospital

  STUDY DIRECTOR

Central Study Contacts

Kezhong Chen, M.D.

CONTACT

+8613488752289; mdkzchen@163.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director of the Thoracic Surgery Department

Study Record Dates

• First Submitted: September 13, 2020
• First Posted: September 22, 2020
• Study Start: January 1, 2020
• Primary Completion: December 1, 2020
• Study Completion: December 1, 2021
• Last Updated: September 22, 2020

Record last verified: 2020-09

Locations

CN (1)