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

Completed

• Conditions: Lung Neoplasm; Lung Diseases

• Registration Number: NCT03849040
• Lead Sponsor: St. Joseph's Healthcare Hamilton

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2019-02-19
• Study First Submitted QC: 2019-02-20
• Study First Posted: 2019-02-21
• Study Start: 2019-04-08
• Primary Completion: 2019-09-23
• Study Completion: 2019-11-20
• Status Verified: 2020-03
• Last Update Submitted: 2020-03-10
• Last Update Posted: 2020-03-11

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 52

Inclusion Criteria

• must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging

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Exclusion Criteria

• None

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Development of computer algorithm to identify lymph node ultrasonographic features	From retrospective data collection to algorithm development (1 month)	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
Validation of computer algorithm to identify lymph node ultrasonographic features	From prospective data collection to algorithm validation (6 months)	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

Secondary Outcome Measures

Name	Time	Method
NeuralSeg prediction of lymph node malignancy	From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)	Objective: to determine whether NeuralSeg can accurately predict malignancy in lymph node when compared to biopsy results of the lymph node that was examined.
Accuracy and reliability of the segmentation performed by NeuralSeg	From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)	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.

Trial Locations

Locations (1)

St. Joseph's Healthcare Hamilton; 🇨🇦 Hamilton, Ontario, Canada