The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Study Design
Outcome Measures
Primary Outcome Measures
- 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
- 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.
- 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.
Eligibility Criteria
Criteria
Inclusion Criteria:
- must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging
Exclusion Criteria:
- None
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | St. Joseph's Healthcare Hamilton | Hamilton | Ontario | Canada | L8N 4A6 |
Sponsors and Collaborators
- St. Joseph's Healthcare Hamilton
Investigators
- Principal Investigator: Wael C Hanna, St. Josephs Healthcare Hamilton
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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. Review.
- 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. Review.
- 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.
- 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. Review.
- StJoes EBUS AI (5636)