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

NCT03849040 | Completed DMC

• 1 other identifier: StJoes EBUS AI (5636)
• Study Type: observational
• Target: 52
• Locations: 1 country
• Sites: 1

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.

Conditions

Lung Diseases; Lung Neoplasm

Keywords

endobronchial ultrasound; ultrasonographic features; artificial intelligence; deep neural network; segmentation

Outcome Measures

Primary Outcomes (2)

• 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 Outcomes (2)

• Accuracy and reliability of the segmentation performed by NeuralSeg

  From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)

• NeuralSeg prediction of lymph node malignancy

  From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)

Interventions

Endobronchial Ultrasound PROCEDURE

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.

Also known as: NeuralSeg

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Phase A does not require patient enrollment. Phase B will require prospective enrollment of patients to obtain the validation set of new lymph node videos. All patients who are scheduled to undergo an EBUS-TBNA procedure for mediastinal staging of NSCLC at St. Joseph's Healthcare Hamilton will be eligible to enroll in this study. There are no exclusion criteria. 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.

You may qualify if:

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

You may not qualify if:

• None

Sponsors & Collaborators

• St. Joseph's Healthcare Hamilton: lead

Study Sites (1)

St. Joseph's Healthcare Hamilton

Hamilton, Ontario, L8N 4A6, Canada

Location

Related Publications (5)

• American College of Chest Physicians; Health and Science Policy Committee. Diagnosis and management of lung cancer: ACCP evidence-based guidelines. American College of Chest Physicians. Chest. 2003 Jan;123(1 Suppl):D-G, 1S-337S. No abstract available.

  PMID: 12527560 BACKGROUND

• Hanna WC, Yasufuku K. Bronchoscopic staging of lung cancer. Ther Adv Respir Dis. 2013 Apr;7(2):111-8. doi: 10.1177/1753465812468041. Epub 2012 Dec 20.

  PMID: 23258501 BACKGROUND

• Hylton DA, Turner J, Shargall Y, Finley C, Agzarian J, Yasufuku K, Fahim C, Hanna WC. Ultrasonographic characteristics of lymph nodes as predictors of malignancy during endobronchial ultrasound (EBUS): A systematic review. Lung Cancer. 2018 Dec;126:97-105. doi: 10.1016/j.lungcan.2018.10.020. Epub 2018 Oct 30.

  PMID: 30527199 BACKGROUND

• Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.

  PMID: 30617339 BACKGROUND

• El-Sherief AH, Lau CT, Wu CC, Drake RL, Abbott GF, Rice TW. International association for the study of lung cancer (IASLC) lymph node map: radiologic review with CT illustration. Radiographics. 2014 Oct;34(6):1680-91. doi: 10.1148/rg.346130097.

  PMID: 25310423 BACKGROUND

MeSH Terms

Conditions

Lung Diseases; Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms

Study Officials

• Wael C Hanna

  St. Josephs Healthcare Hamilton

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr. Waël Hanna, MDCM, MBA, FRCSC

Study Record Dates

• First Submitted: February 19, 2019
• First Posted: February 21, 2019
• Study Start: April 8, 2019
• Primary Completion: September 23, 2019
• Study Completion: November 20, 2019
• Last Updated: March 11, 2020

Record last verified: 2020-03

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)