Clinical trial NCT04816981: AI-EBUS-Elastography for LN Staging
Official title: Clinical Utility of Artificial Intelligence-Augmented Endobronchial Ultrasound-Elastography in Lymph Node Staging for Lung Cancer
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Artificial Intelligence
Endobronchial Ultrasound
Elastography
NSCLC
Lung Cancer
Lead sponsor: St. Joseph's Healthcare Hamilton
Sponsor class: OTHER
First posted: 2021-03-25
Enrollment: 100
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: This is a single-centre, prospective clinical trial, in which patients will be enrolled in a consecutive sample and patient involvement will conclude when the procedure ends. No follow-up will be required after the study.
Primary purpose: DIAGNOSTIC

Brief summary: Before any treatment decisions are made for patients with lung cancer, it is crucial to determine whether the cancer has spread to the lymph nodes in the chest. Traditionally, this is determined by taking biopsy samples from these lymph nodes, using the Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA) procedure. Unfortunately, in 40% of the time, the results of EBUS-TBNA are not informative and wrong treatment decisions are made. There is, therefore, a recognized need for a better way to determine whether the cancer has spread to the lymph nodes in the chest. The investigators believe that elastography, a recently discovered imaging technology, can fulfill this need. In this study, the investigators are proposing to determine whether elastography can diagnose cancer in the lymph nodes. Elastography determines the tissue stiffness in the different parts of the lymph node and generates a colour map, where the stiffest part of the lymph node appears blue, and the softest part appears red. It has been proposed that if a lymph node is predominantly blue, then it contains cancer, and if it is predominantly red, then it is benign. To study this, the investigators have designed an experiment where the lymph nodes are imaged by EBUS-Elastography, and the images are subsequently analyzed by a computer algorithm using Artificial Intelligence. The algorithm will be trained to read the images first, and then predict whether these images show cancer in the lymph node. To evaluate the success of the algorithm, the investigators will compare its predictions to the pathology results from the lymph node biopsies or surgical specimens.


Eligibility criteria: Inclusion Criteria:

* Patients that are diagnosed with suspected or confirmed NSCLC that have been referred to mediastinal staging through EBUS-TBNA at St. Joseph's Healthcare Hamilton will be eligible for this study.

Exclusion Criteria:

* No exclusion criteria will apply.

Primary outcomes:
measure: Stiffness Area Ratio
timeFrame: 8 months
Secondary outcomes:
measure: NeuralSeg's prediction of lymph node malignancy
timeFrame: 2 months
measure: The agreement between NeuralSeg's predictions and pathology results, as measured by diagnostic accuracy, sensitivity, specificity, positive and negative predictive values
timeFrame: 2 months
Other outcomes:
Locations:
city: Hamilton
country: Canada
facility: St. Joseph's Healthcare Hamilton
state: Ontario
Study officials:
affiliation: McMaster University
name: Wael C Hanna, MDCM, MBA, FRCSC
role: PRINCIPAL_INVESTIGATOR
References: