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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.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
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| 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) |
| 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) |
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Inclusion Criteria:
Exclusion Criteria:
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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.
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| Name | Affiliation | Role |
|---|---|---|
| Wael C Hanna | St. Josephs Healthcare Hamilton | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| St. Joseph's Healthcare Hamilton | Hamilton | Ontario | L8N 4A6 | Canada |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 12527560 | Background | 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. | |
| 23258501 | 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. |
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| ID | Term |
|---|---|
| D008171 | Lung Diseases |
| D008175 | Lung Neoplasms |
| ID | Term |
|---|---|
| D012140 | Respiratory Tract Diseases |
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
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| 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) |
| 30527199 | 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. |
| 30617339 | 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. |
| 25310423 | 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. |
| D009369 | Neoplasms |