Lung DiseasesNCT03849040
The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging
An observational study of Lung Diseases and Lung Neoplasm, run by St. Joseph's Healthcare Hamilton at 1 site in Canada.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Diseases, Lung Neoplasm
- What is being tested?
- Endobronchial Ultrasound
- Type of treatment
- Surgery or procedure
- How many people
- 52 participants
- Where
- 1 siteCanada
- Study dates
- 8 Apr 2019to 20 Nov 2019
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging
Who cannot join
- None
Trial locations and contacts
1 site in 1 country.
About this study
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
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.
- Conditions
- Lung Diseases, Lung Neoplasm
- Being tested
- Endobronchial Ultrasound
- Keywords
- endobronchial ultrasound, ultrasonographic features, artificial intelligence, deep neural network, segmentation
- Medical subject headings
- Lung Diseases, Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Endobronchial UltrasoundProcedure or surgery
Also called NeuralSeg
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 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
Measured: From prospective data collection to algorithm validation (6 months)
Secondary outcomes (2)
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.
Measured: From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)
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.
Measured: From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2019
- Study start8 Apr 2019
- Primary completion23 Sep 2019
- Study completion20 Nov 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Lung DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 52 participants (actual)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- St. Joseph's Healthcare Hamilton
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wael Hanna, St. Joseph's Healthcare Hamilton
- Investigators
- Wael C Hanna, Principal investigator, St. Josephs Healthcare Hamilton
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03849040
- Sponsor's protocol ID
- StJoes EBUS AI (5636)
- Status last verified
- Mar 2020
- First submitted
- 19 Feb 2019
- Last updated
- 11 Mar 2020
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 12527560Hanna 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.
PubMed 23258501Hylton 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.
PubMed 30527199Topol 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.
PubMed 30617339El-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.
PubMed 25310423
Study at a glance
From the registry record, last updated by the sponsor on 11 Mar 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT03849040
- Acronym / sponsor ID
- StJoes EBUS AI (5636)
- Conditions
- Lung Diseases, Lung Neoplasm
- Keywords
- endobronchial ultrasound, ultrasonographic features, artificial intelligence, deep neural network, segmentation
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 52 participants (actual)
- Lead sponsor
- St. Joseph's Healthcare HamiltonUniversity, hospital or other
- Study start
- 8 Apr 2019 (actual)
- Primary completion
- 23 Sep 2019 (actual)
- Study completion
- 20 Nov 2019 (actual)
- First posted
- 21 Feb 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging
Key exclusion criteria (1 listed)
- None
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Endobronchial UltrasoundAlso: NeuralSegAll 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. | Procedure or surgery | – |
Primary outcome
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
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
2 secondary outcomes
Accuracy and reliability of the segmentation performed by NeuralSeg
Time frame: From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)
NeuralSeg prediction of lymph node malignancy
Time frame: From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Mar 2020.
- Background: PubMed 12527560American 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.
- Background: PubMed 23258501Hanna 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.
- Background: PubMed 30527199Hylton 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.
- Background: PubMed 30617339Topol 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.
- Background: PubMed 25310423El-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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Wael C Hanna principal investigator
St. Josephs Healthcare Hamilton
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Diseases and Lung Neoplasm, run by St. Joseph's Healthcare Hamilton at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 11 Mar 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of computer algorithm to identify lymph node ultrasonographic features (time frame: From retrospective data collection to algorithm development (1 month)). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03849040, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Mar 2020.