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

Lung Diseases Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03849040
• Other study ID #: StJoes EBUS AI (5636)
• Status: Completed
• Start date: April 8, 2019
• Est. completion date: November 20, 2019

Study information

• Verified date: March 2020
• Source: St. Joseph's Healthcare Hamilton
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 52
• Est. completion date: November 20, 2019
• Est. primary completion date: September 23, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

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

Exclusion Criteria:

• None

Related Conditions & MeSH terms

• Lung Diseases
• Lung Neoplasm
• Lung Neoplasms

Intervention

Procedure:
• Endobronchial Ultrasound
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.

Locations

Country	Name	City	State
Canada	St. Joseph's Healthcare Hamilton	Hamilton	Ontario

Sponsors (1)

Lead Sponsor	Collaborator
St. Joseph's Healthcare Hamilton

Country where clinical trial is conducted

Canada, 

References & 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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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)
Primary	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	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)
Secondary	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)