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New Method to Differentiate Benign and Malignant Pulmonary Nodules.

Pulmonary Nodules Clinical Trial

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

Clinical Trial 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.

Clinical Trial 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. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT06056999
Study type Observational
Source China-Japan Friendship Hospital
Contact Guangying Zhu, doctor
Phone +86-010-84205381
Email zryyfa@163.com
Status Recruiting
Phase
Start date September 29, 2023
Completion date July 2025
View Details
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