Clinical trial NCT03849040: The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging
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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Lung Diseases
Lung Neoplasm
Keywords: endobronchial ultrasound
ultrasonographic features
artificial intelligence
deep neural network
segmentation
Lead sponsor: St. Joseph's Healthcare Hamilton
Sponsor class: OTHER
First posted: 2019-02-21
Enrollment: 52
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* None

Primary outcomes:
measure: Development of computer algorithm to identify lymph node ultrasonographic features
timeFrame: From retrospective data collection to algorithm development (1 month)
measure: Validation of computer algorithm to identify lymph node ultrasonographic features
timeFrame: From prospective data collection to algorithm validation (6 months)
Secondary outcomes:
measure: Accuracy and reliability of the segmentation performed by NeuralSeg
timeFrame: From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)
measure: NeuralSeg prediction of lymph node malignancy
timeFrame: From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)
Other outcomes:
Locations:
city: Hamilton
country: Canada
facility: St. Joseph's Healthcare Hamilton
state: Ontario
Study officials:
affiliation: St. Josephs Healthcare Hamilton
name: Wael C Hanna
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
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.
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.
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.
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.