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Completed

NCT Number: NCT03849040

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

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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Key information

Conditions

Lung Diseases Lung Neoplasm Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

St. Joseph's Healthcare Hamilton

Hamilton, Ontario, L8N 4A6, Canada

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

  • None

Treatment and study plan

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.

Other names: NeuralSeg

Primary outcomes

  1. Development of computer algorithm to identify lymph node ultrasonographic features

    Time frame: 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

  2. Validation of computer algorithm to identify lymph node ultrasonographic features

    Time frame: 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 outcomes

  1. Accuracy and reliability of the segmentation performed by NeuralSeg

    Time frame: 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.

  2. NeuralSeg prediction of lymph node malignancy

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

St. Joseph's Healthcare Hamilton

Other

Registry information

Official study 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

Important dates

Study start
2019
Primary completion
2019
Study completion
2019
First posted
Feb 21, 2019
Registry last updated
Mar 11, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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