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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-01-01

Study Completion Date

2021-12-01

Brief Summary

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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.

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Detailed Description

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Conditions

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Lung Cancer

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

RETROSPECTIVE

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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

Minimum Eligible Age

20 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes