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

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.

Primary outcomes

• Diagnostic efficacy of EBUS multimodal artificial intelligence prediction model based on videos [6 months post-procedure]: Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.

Secondary outcomes

• Diagnostic efficacy of traditional qualitative and quantitative methods [6 months post-procedure]: Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
• Diagnostic efficacy of multimodal deep learning model based on images [6 months post-procedure]: Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
• Comparion of prediction model based on deeping learning with traditional qualitative and quantitative methods [6 months post-procedure]: Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.

Sex: All, ages: 18 Years to any

Inclusion Criteria:

1. Chest CT shows enlarged intrathoracic LNs (short diameter > 1 cm) or PET / CT shows patients with increased FDG uptake (SUV ≧ 2.0) in intrathoracic LNs;
2. Operating physician considered EBUS-TBNA should be performed on LNs for diagnosis or preoperative staging of lung cancer;
3. Patients agree to undergo EBUS-TBNA, sign informed consent, and have no contraindications.

Exclusion Criteria:

\- Patients having other situations that are not suitable for EBUS-TBNA.

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• Jiayuan Sun, MD, PhD, Study director, Shanghai Chest Hospital