NCT05739331

Brief Summary

To evaluate the usefulness of Deep neural network (DNN) in the evaluation of mediastinal and hilar lymph nodes with Endobronchial ultrasound (EBUS). The study will explore the feasibility of DNN to identify lymph nodes and blood vessel examined with EBUS.

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
50

participants targeted

Target at P25-P50 for all trials

Timeline
19mo left

Started May 2023

Longer than P75 for all trials

Geographic Reach
1 country

2 active sites

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress66%
May 2023Dec 2027

First Submitted

Initial submission to the registry

February 13, 2023

Completed
9 days until next milestone

First Posted

Study publicly available on registry

February 22, 2023

Completed
2 months until next milestone

Study Start

First participant enrolled

May 1, 2023

Completed
4 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

May 1, 2027

Expected
7 months until next milestone

Study Completion

Last participant's last visit for all outcomes

December 1, 2027

Last Updated

August 22, 2025

Status Verified

June 1, 2025

Enrollment Period

4 years

First QC Date

February 13, 2023

Last Update Submit

August 18, 2025

Conditions

Artificial IntelligenceEndobronchial UltrasoundLung Cancer

Outcome Measures

Primary Outcomes (1)

  • Capability

    To explore if Deep neural network (DNN) has capability to segment lymph nodes and blood vessels from EBUS images

    8 months

Secondary Outcomes (6)

  • Precision

    2 months

  • Sensitivity

    2 months

  • Specificity

    2 months

  • Dice similarity coefficient

    2 months

  • Run-time

    2 months

  • +1 more secondary outcomes

Interventions

machine learning algorithmDEVICE

Machine learning algorithm run on EBUS images for real-time labelling of mediastinal lymph nodes and lymph node level

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patents with undiagnosed enlarged mediastinal and hilar lymph nodes who have been recommended for Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA).

You may qualify if:

  • Subjects referred to thoracic department in any of the participating hospitals with undiagnosed enlarged mediastinal and hilar lymph nodes.
  • Subjects have to be ≥ 18 years of age

You may not qualify if:

  • Pregnancy
  • Any patient that the Investigator feels is not appropriate for this study for any reason.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (2)

Department of Pulmonology, Levanger Hospital, North Trøndelag Hospital Trust

Levanger, 7600, Norway

RECRUITING

Department of Thoracic Medicine, St Olavs Hospital

Trondheim, 7030, Norway

RECRUITING

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract NeoplasmsThoracic NeoplasmsNeoplasms by SiteNeoplasmsLung DiseasesRespiratory Tract Diseases

Study Officials

  • Øivind Rognmo, Dr.philos

    Norwegian University of Science and Technology

    STUDY DIRECTOR

Central Study Contacts

Øyvind Ervik, MD

CONTACT

+4791634595oyvind.ervik@ntnu.no

Hanne Sorger, MD,PhD

CONTACT

+4791816787hanne.sorger@ntnu.no

Study Design

Study Type
observational
Observational Model
CASE ONLY
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

February 13, 2023

First Posted

February 22, 2023

Study Start

May 1, 2023

Primary Completion (Estimated)

May 1, 2027

Study Completion (Estimated)

December 1, 2027

Last Updated

August 22, 2025

Record last verified: 2025-06

Locations

NO(2)