NCT04328792

Prediction Model of CP-EBUS in the Diagnosis of Lymph Nodes

Prediction Model Based on Deep Learning of CP-EBUS Multimodal Image in the Diagnosis of Benign and Malignant Lymph Nodes

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 1,300 (estimated)
• Sponsor: Shanghai Chest Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: —

Summary

Endobronchial ultrasound (EBUS) multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. In this study, EBUS multimodal image database of 1000 inthoracic benign and malignant lymph nodes (LNs) will be constructed to train deep learning neural networks, which can automatically select representative images and diagnose LNs. Investigators will establish an artificial intelligence prediction model based on deep learning of intrathoracic LNs, and verify the model in other 300 LNs.

Detailed description

Intrathoracic LNs enlargement has a wide range of diseases, among which intrathoracic LNs metastasis of lung cancer is the most common malignant disease. Benign lesions, including inflammation, tuberculosis and sarcoidosis, also need to be differentiated for targeted treatment. EBUS multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. This study includes two parts: retrospectively construction of EBUS artificial intelligence prediction model and multi-center prospectively validation of the prediction model. A total of 1300 LNs will be enrolled in the study. During the retention of videos, target LNs and peripheral vessels are examined using ultrasound hosts (EU-ME2, Olympus or Hi-vision Avius, Hitachi) equipped with elastography and doppler functions and ultrasound bronchoscopy (BF-UC260FW, Olympus or EB1970UK, Pentax). Multimodal image data of target LNs are collected. Investigators will construct artificial intelligence prediction model based on deep learning using images from 1000 LNs firstly, and verify the model in other 300 LNs. This model will be compared with traditional qualitative and quantitative evaluation methods to verify the diagnostic efficacy.

Conditions

• Lymph Node Disease

Timeline

• Start date: 2018-07-01
• Primary completion: 2020-06-30
• Completion: 2020-12-31
• First posted: 2020-03-31
• Last updated: 2020-04-02

Locations

1 site across 1 country: China