Clinical trial · Observational
The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Diseases | — | UNRESOLVED | — |
| Lung Neoplasm | Lung Neoplasm | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Endobronchial Ultrasound | Procedure | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (2)
- measure
- Development of computer algorithm to identify lymph node ultrasonographic features
- timeFrame
- From retrospective data collection to algorithm development (1 month)
- description
- 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
- measure
- Validation of computer algorithm to identify lymph node ultrasonographic features
- timeFrame
- From prospective data collection to algorithm validation (6 months)
- description
- 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 (2)
- measure
- Accuracy and reliability of the segmentation performed by NeuralSeg
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging Exclusion Criteria: * None
References
Publications (5)
- BACKGROUNDAmerican 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. PMID 12527560
- BACKGROUNDHanna 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. PMID 23258501
- BACKGROUNDHylton 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. PMID 30527199
- BACKGROUNDTopol 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. PMID 30617339
- BACKGROUNDEl-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. PMID 25310423