Lung Diseases, Lung Neoplasm
Conditions
Keywords
endobronchial ultrasound, ultrasonographic features, artificial intelligence, deep neural network, segmentation
Brief 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. 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.
Interventions
PROCEDUREEndobronchial Ultrasound
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.
Sponsors
St. Joseph's Healthcare Hamilton
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging
Exclusion criteria
* None
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Development of computer algorithm to identify lymph node ultrasonographic features | 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 | 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
| Measure | Time frame | Description |
|---|---|---|
| Accuracy and reliability of the segmentation performed by NeuralSeg | 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. |
| NeuralSeg prediction of lymph node malignancy | 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. |
Countries
Canada
Outcome results
None listed