A New Method to Differentiate Benign and Malignant Pulmonary Nodules by Mass Spectrometry Combined With Artificial Intelligence.
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Pulmonary Nodules
- Sponsor
- China-Japan Friendship Hospital
- Enrollment
- 150
- Locations
- 1
- Primary Endpoint
- Establish an model for differentiating lung nodules.
- Status
- Recruiting
- Last Updated
- last year
Overview
Brief Summary
The goal of this observational clinical trial is to establish a new method for differentiating benign and malignant pulmonary nodules by peripheral blood detection in patients with pulmonary nodules (<3cm). The main questions it aims to answer is: How to combine blood metabolomic mass spectrometry detection and artificial intelligence image analysis to establish a new model for differentiating benign and malignant pulmonary nodules. Participants will be asked provide 4 mL peripheral blood for the test.
Detailed Description
The aim of this clinical trial is to establish a new method for differentiating benign and malignant pulmonary nodules by the combination of metabolomics analysis and artificial intelligence (AI) analysis. It is expected to improve the accuracy of the identification of benign and malignant pulmonary nodules. Patients with clinical suspected malignant pulmonary nodules will be included in this trial. The subjects will be divided into three group by CT image presentation: (1) pure ground-glass nodule (pGGN), (2) part-solid nodule (PSN), (3) solid nodule (SN). Peripheral blood of subjects will be collected and detected by mass spectrometry to obtain the metabolomic characterization. The classification model of each group will be constructed based on the data analysis algorithm by machine learning. The diagnostic efficacy of the new model combined with the AI image analysis system for differentiating benign and malignant pulmonary nodules will be analyzed.
Investigators
Guangying Zhu
chief physician
China-Japan Friendship Hospital
Eligibility Criteria
Inclusion Criteria
- •Age ≥18 years;
- •CT imaging shows the presence of pulmonary nodule \<3cm which is scheduled for puncture biopsy or surgery (i.e., the target lesion), the presence of ≥2 target lesions of the same type (categorized by density) are allowed;
- •Subjects are in good condition with Eastern Cooperative Oncology Group (ECOG) scale of 0-2;
- •Subjects with fair vital organ function, defined as: white blood cells ≥3.0×10\^9/L, platelets ≥75×10\^9/L, hemoglobin ≥90g/L, alanine aminotransferase and aspartate aminotransferase ≤2.5 times the upper limit of normal values, and serum creatinine \<178μmol/L;
- •Subjects must have the ability to understand and sign the informed consent in writing voluntarily.
Exclusion Criteria
- •Imaging examination have suggested the possibility of metastasis at other sites;
- •≥2 target lesions with different type categorized by density;
- •History of malignant disease;
- •Severe vascular lesions within the last 3 months, or known significant active infection, during acute/chronic tuberculosis infection, or severe cardiovascular and cerebrovascular diseases, dysfunction of liver and renal, or significant endocrine and metabolic disorders, or other serious concomitant diseases that are not controlled;
- •The specialist/surgeon assessed that puncture or surgery is not available, with contraindication such as coagulation disorders, cardiorespiratory insufficiency, etc.;
- •History of uncontrolled epilepsy, central nervous system disease, or psychiatric disorders, that may affect the signing of informed consent;
- •Pregnant or breastfeeding women;
- •Other conditions deemed by the investigator to be unsuitable for enrollment.
Outcomes
Primary Outcomes
Establish an model for differentiating lung nodules.
Time Frame: 6 months
To establish a new method for differentiating benign and malignant pulmonary nodules by the combination of metabolomics analysis and artificial intelligence (AI) image analysis.