The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging
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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
-
-
Ontario
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Hamilton, Ontario, Canada, L8N 4A6
- St. Joseph's Healthcare Hamilton
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging
Exclusion Criteria:
- None
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Development of computer algorithm to identify lymph node ultrasonographic features
Time Frame: From retrospective data collection to algorithm development (1 month)
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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
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From retrospective data collection to algorithm development (1 month)
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Validation of computer algorithm to identify lymph node ultrasonographic features
Time Frame: 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
|
From prospective data collection to algorithm validation (6 months)
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Accuracy and reliability of the segmentation performed by NeuralSeg
Time Frame: From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)
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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.
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From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)
|
|
NeuralSeg prediction of lymph node malignancy
Time Frame: From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)
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Objective: to determine whether NeuralSeg can accurately predict malignancy in lymph node when compared to biopsy results of the lymph node that was examined.
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From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Wael C Hanna, St. Josephs Healthcare Hamilton
Publications and helpful links
General Publications
- 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.
- 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.
- 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.
- 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.
- 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.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- StJoes EBUS AI (5636)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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