New method to tell apart benign and malignant lung nodules

A New Method to Differentiate Benign and Malignant Pulmonary Nodules by Mass Spectrometry Combined With Artificial Intelligence.

This study is testing a new way to tell if lung nodules are harmless or cancerous by using blood samples and advanced computer analysis to help improve early lung cancer diagnosis.

Quick facts

• Study type: Observational
• Enrollment: 150 (estimated)
• Ages: 18 Years and up
• Sex: All
• Sponsor: China-Japan Friendship Hospital (other)
• Locations: 1 site (Beijing, Beijing Municipality)
• Trial ID: NCT06056999 on ClinicalTrials.gov

What this trial studies

This observational clinical trial aims to develop a novel approach for distinguishing between benign and malignant pulmonary nodules using a combination of blood metabolomic analysis and artificial intelligence (AI) image analysis. Participants with suspected malignant nodules will provide peripheral blood samples, which will be analyzed through mass spectrometry to characterize metabolites. The study will categorize nodules based on CT imaging and construct a classification model using machine learning algorithms to enhance diagnostic accuracy. The ultimate goal is to improve early diagnosis of lung cancer by accurately identifying the nature of pulmonary nodules.

Who should consider this trial

Good fit: Ideal candidates are adults aged 18 and older with pulmonary nodules less than 3 cm that are scheduled for biopsy or surgery.

Not a fit: Patients with a history of malignant disease or those whose imaging suggests metastasis at other sites may not benefit from this study.

Why it matters

Potential benefit: If successful, this method could significantly improve the early diagnosis of lung cancer, leading to better patient outcomes.

How similar studies have performed: While the approach of combining metabolomics and AI for diagnosis is innovative, similar studies have shown promise in improving diagnostic accuracy in other medical fields.

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.

Where this trial is running

Beijing, Beijing Municipality

• China-Japan Friendship Hospital — Beijing, Beijing Municipality, China (RECRUITING)

Study contacts

• Principal investigator: Guangying Zhu — China-Japan Friendship Hospital
• Study coordinator: Guangying Zhu, doctor

Conditions: Pulmonary Nodules, differential diagnosis, benign and malignant pulmonary nodules, early diagnosis of lung cancer