Clinical trial NCT06056999: New Method to Differentiate Benign and Malignant Pulmonary Nodules.
Official title: A New Method to Differentiate Benign and Malignant Pulmonary Nodules by Mass Spectrometry Combined With Artificial Intelligence.
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Pulmonary Nodules
Keywords: differential diagnosis
benign and malignant pulmonary nodules
early diagnosis of lung cancer
Lead sponsor: China-Japan Friendship Hospital
Sponsor class: OTHER
First posted: 2023-09-28
Enrollment: 150
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with pulmonary nodule \<3cm who are admitted to China-Japan Friendship Hospital and are scheduled for puncture biopsy or surgery.

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.

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.

Primary outcomes:
measure: Establish an model for differentiating lung nodules.
timeFrame: 6 months
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: China-Japan Friendship Hospital
state: Beijing
Study officials:
affiliation: China-Japan Friendship Hospital
name: Guangying Zhu
role: PRINCIPAL_INVESTIGATOR
References: