The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging

Study Overview
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Brief Summary
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This study aims to determine if a deep neural artificial intelligence (AI) network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by endobronchial ultrasound transbronchial needle aspiration(EBUS-TBNA), using the technique of segmentation. Images will be created from 300 lymph nodes videos from a prospective library and will be used as a derivation set to develop the algorithm. An additional100 lymph node images will be prospectively collected to validate if NeuralSeg can correctly apply the score.

Official Title
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Development and Validation of a Computer-aided Algorithm Using Artificial Intelligence and Deep Neural Networks for the Segmentation of Ultrasonographic Features of Lymph Nodes During Endobronchial Ultrasound

Conditions
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Lung Diseases, Lung Neoplasm

Intervention / Treatment
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* Procedure: Endobronchial Ultrasound


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging Exclusion Criteria: None

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Procedure: Endobronchial Ultrasound|All patients will undergo EBUS-TBNA as per routine care, except for the one difference where the procedures will be video-recorded so that they can be used for computer analysis at a later time. Static images will be obtained from EBUS videos in order to perform segmentation. Segmentation will be conducted by both an experienced endoscopist and NeuralSeg.|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Development of computer algorithm to identify lymph node ultrasonographic features | Objective: to determine whether a deep neural AI network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by EBUS, using the technique of segmentation on an existing (derivation) set of lymph node videos | From retrospective data collection to algorithm development (1 month) | 
| Validation of computer algorithm to identify lymph node ultrasonographic features | Objective: to determine whether NeuralSeg can correctly apply the Canada Lymph Node Score to a new (validation) set of lymph node videos that it has never seen before | From prospective data collection to algorithm validation (6 months) | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy and reliability of the segmentation performed by NeuralSeg | Objective: to compare the accuracy and reliability of the segmentation performed by NeuralSeg to the segmentation performed by an experienced endoscopic surgeon using DICE-SORENSEN coefficients. | From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month) | 
| NeuralSeg prediction of lymph node malignancy | Objective: to determine whether NeuralSeg can accurately predict malignancy in lymph node when compared to biopsy results of the lymph node that was examined. | From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months) | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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endobronchial ultrasound, ultrasonographic features, artificial intelligence, deep neural network, segmentation