Lymph Node DiseaseNCT04328792
Prediction Model of CP-EBUS in the Diagnosis of Lymph Nodes
An observational study of Lymph Node Disease, run by Shanghai Chest Hospital at 1 site in China.
Trial summary
Endobronchial ultrasound (EBUS) multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lymph Node Disease
- How many people
- 1,300 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2018to 31 Dec 2020
- 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
- Adult, Older adult
Study population: Patients with enlarged intrathoracic LNs that need to be diagnosed by EBUS-TBNA are enrolled in this study.
Who may be able to join
- Chest CT shows enlarged intrathoracic LNs (short diameter > 1 cm) or PET / CT shows patients with increased FDG uptake (SUV ≧ 2.0) in intrathoracic LNs;
- Operating physician considered EBUS-TBNA should be performed on LNs for diagnosis or preoperative staging of lung cancer;
- Patients agree to undergo EBUS-TBNA, sign informed consent, and have no contraindications.
Who cannot join
- Patients having other situations that are not suitable for EBUS-TBNA.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Shanghai Chest Hospital
Shanghai, Shanghai Municipality, 200030
Jiayuan Sun, PhD, 86-21-22200000 ext. 1511, jysun1976@163.com
About this study
Official title: Prediction Model Based on Deep Learning of CP-EBUS Multimodal Image in the Diagnosis of Benign and Malignant Lymph Nodes
Endobronchial ultrasound (EBUS) multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. In this study, EBUS multimodal image database of 1000 inthoracic benign and malignant lymph nodes (LNs) will be constructed to train deep learning neural networks, which can automatically select representative images and diagnose LNs. Investigators will establish an artificial intelligence prediction model based on deep learning of intrathoracic LNs, and verify the model in other 300 LNs.
Read the detailed description
Intrathoracic LNs enlargement has a wide range of diseases, among which intrathoracic LNs metastasis of lung cancer is the most common malignant disease. Benign lesions, including inflammation, tuberculosis and sarcoidosis, also need to be differentiated for targeted treatment.
EBUS multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. This study includes two parts: retrospectively construction of EBUS artificial intelligence prediction model and multi-center prospectively validation of the prediction model. A total of 1300 LNs will be enrolled in the study.
During the retention of videos, target LNs and peripheral vessels are examined using ultrasound hosts (EU-ME2, Olympus or Hi-vision Avius, Hitachi) equipped with elastography and doppler functions and ultrasound bronchoscopy (BF-UC260FW, Olympus or EB1970UK, Pentax). Multimodal image data of target LNs are collected.
Investigators will construct artificial intelligence prediction model based on deep learning using images from 1000 LNs firstly, and verify the model in other 300 LNs. This model will be compared with traditional qualitative and quantitative evaluation methods to verify the diagnostic efficacy.
- Conditions
- Lymph Node Disease
- Keywords
- EBUS-TBNA, Intrathoracic lymph node, Multimodal image, Deep learning, Prediction model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Prospectively validation group | Two diagnosis methods will be used in the prospective validation section, one is traditional qualitative and quantitative method, the other is artificial intelligence prediction model based on videos to compare the diagnostic efficacy. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic efficacy of EBUS multimodal artificial intelligence prediction model based on videos
Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
Measured: 6 months post-procedure
Secondary outcomes (3)
Diagnostic efficacy of traditional qualitative and quantitative methods
Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
Measured: 6 months post-procedure
Diagnostic efficacy of multimodal deep learning model based on images
Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
Measured: 6 months post-procedure
Comparion of prediction model based on deeping learning with traditional qualitative and quantitative methods
Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
Measured: 6 months post-procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Mar 2020
- Study start1 Jul 2018
- Primary completion30 Jun 2020 (estimated)
- Study completion31 Dec 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Lymph Node DiseaseStudies of the same condition, open and under way.
- More trials run by Shanghai Chest HospitalThe sponsor's other registered studies.
- 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
- Other
- Enrolment
- 1,300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai Chest Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jiayuan Sun, Shanghai Chest Hospital
- Investigators
- Jiayuan Sun, MD, PhD, Study director, Shanghai Chest Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04328792
- Sponsor's protocol ID
- SHCHE201906
- Status last verified
- Mar 2020
- First submitted
- 2 Dec 2019
- Last updated
- 2 Apr 2020
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Investigators may release the database after the study, but no decision has been made yet.
Publications
Steinfort DP, Conron M, Tsui A, Pasricha SR, Renwick WE, Antippa P, Irving LB. Endobronchial ultrasound-guided transbronchial needle aspiration for the evaluation of suspected lymphoma. J Thorac Oncol. 2010 Jun;5(6):804-9. doi: 10.1097/jto.0b013e3181d873be.
PubMed 20521347Sun J, Teng J, Yang H, Li Z, Zhang J, Zhao H, Garfield DH, Han B. Endobronchial ultrasound-guided transbronchial needle aspiration in diagnosing intrathoracic tuberculosis. Ann Thorac Surg. 2013 Dec;96(6):2021-7. doi: 10.1016/j.athoracsur.2013.07.005. Epub 2013 Sep 12.
PubMed 24035300Fujiwara T, Yasufuku K, Nakajima T, Chiyo M, Yoshida S, Suzuki M, Shibuya K, Hiroshima K, Nakatani Y, Yoshino I. The utility of sonographic features during endobronchial ultrasound-guided transbronchial needle aspiration for lymph node staging in patients with lung cancer: a standard endobronchial ultrasound image classification system. Chest. 2010 Sep;138(3):641-7. doi: 10.1378/chest.09-2006. Epub 2010 Apr 9.
PubMed 20382710Nakajima T, Anayama T, Shingyoji M, Kimura H, Yoshino I, Yasufuku K. Vascular image patterns of lymph nodes for the prediction of metastatic disease during EBUS-TBNA for mediastinal staging of lung cancer. J Thorac Oncol. 2012 Jun;7(6):1009-14. doi: 10.1097/JTO.0b013e31824cbafa.
PubMed 22525556Wang L, Wu W, Hu Y, Teng J, Zhong R, Han B, Sun J. Sonographic Features of Endobronchial Ultrasonography Predict Intrathoracic Lymph Node Metastasis in Lung Cancer Patients. Ann Thorac Surg. 2015 Oct;100(4):1203-9. doi: 10.1016/j.athoracsur.2015.04.143. Epub 2015 Jul 28.
PubMed 26228606Izumo T, Sasada S, Chavez C, Matsumoto Y, Tsuchida T. Endobronchial ultrasound elastography in the diagnosis of mediastinal and hilar lymph nodes. Jpn J Clin Oncol. 2014 Oct;44(10):956-62. doi: 10.1093/jjco/hyu105. Epub 2014 Aug 13.
PubMed 25121724Saftoiu A, Vilmann P, Gorunescu F, Janssen J, Hocke M, Larsen M, Iglesias-Garcia J, Arcidiacono P, Will U, Giovannini M, Dietrich C, Havre R, Gheorghe C, McKay C, Gheonea DI, Ciurea T; European EUS Elastography Multicentric Study Group. Accuracy of endoscopic ultrasound elastography used for differential diagnosis of focal pancreatic masses: a multicenter study. Endoscopy. 2011 Jul;43(7):596-603. doi: 10.1055/s-0030-1256314. Epub 2011 Mar 24.
PubMed 21437851Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.
PubMed 27898976
Study at a glance
From the registry record, last updated by the sponsor on 2 Apr 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction Model Based on Deep Learning of CP-EBUS Multimodal Image in the Diagnosis of Benign and Malignant Lymph Nodes
- ClinicalTrials.gov ID
- NCT04328792
- Acronym / sponsor ID
- SHCHE201906
- Conditions
- Lymph Node Disease
- Keywords
- EBUS-TBNA, Intrathoracic lymph node, Multimodal image, Deep learning, Prediction model
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,300 participants (estimated)
- Lead sponsor
- Shanghai Chest HospitalUniversity, hospital or other
- Study start
- 1 Jul 2018 (actual)
- Primary completion
- 30 Jun 2020 (estimated)
- Study completion
- 31 Dec 2020 (estimated)
- First posted
- 31 Mar 2020 (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.
Endobronchial ultrasound (EBUS) multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. In this study, EBUS multimodal image database of 1000 inthoracic benign and malignant lymph nodes (LNs) will be constructed to train deep learning neural networks, which can automatically select representative images and diagnose LNs. Investigators will establish an artificial intelligence prediction model based on deep learning of intrathoracic LNs, and verify the model in other 300 LNs.
Detailed description
Intrathoracic LNs enlargement has a wide range of diseases, among which intrathoracic LNs metastasis of lung cancer is the most common malignant disease. Benign lesions, including inflammation, tuberculosis and sarcoidosis, also need to be differentiated for targeted treatment.
EBUS multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. This study includes two parts: retrospectively construction of EBUS artificial intelligence prediction model and multi-center prospectively validation of the prediction model. A total of 1300 LNs will be enrolled in the study.
During the retention of videos, target LNs and peripheral vessels are examined using ultrasound hosts (EU-ME2, Olympus or Hi-vision Avius, Hitachi) equipped with elastography and doppler functions and ultrasound bronchoscopy (BF-UC260FW, Olympus or EB1970UK, Pentax). Multimodal image data of target LNs are collected.
Investigators will construct artificial intelligence prediction model based on deep learning using images from 1000 LNs firstly, and verify the model in other 300 LNs. This model will be compared with traditional qualitative and quantitative evaluation methods to verify the diagnostic efficacy.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prospectively validation group | – | –Two diagnosis methods will be used in the prospective validation section, one is traditional qualitative and quantitative method, the other is artificial intelligence prediction model based on videos to compare the diagnostic efficacy. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients with enlarged intrathoracic LNs that need to be diagnosed by EBUS-TBNA are enrolled in this study.
Key inclusion criteria (3 listed)
- Chest CT shows enlarged intrathoracic LNs (short diameter > 1 cm) or PET / CT shows patients with increased FDG uptake (SUV ≧ 2.0) in intrathoracic LNs;
- Operating physician considered EBUS-TBNA should be performed on LNs for diagnosis or preoperative staging of lung cancer;
- Patients agree to undergo EBUS-TBNA, sign informed consent, and have no contraindications.
Key exclusion criteria (1 listed)
- Patients having other situations that are not suitable for EBUS-TBNA.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic efficacy of EBUS multimodal artificial intelligence prediction model based on videos
Time frame: 6 months post-procedure
Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
3 secondary outcomes
Diagnostic efficacy of traditional qualitative and quantitative methods
Time frame: 6 months post-procedure
Diagnostic efficacy of multimodal deep learning model based on images
Time frame: 6 months post-procedure
Comparion of prediction model based on deeping learning with traditional qualitative and quantitative methods
Time frame: 6 months post-procedure
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Apr 2020.
- Background: PubMed 20521347Steinfort DP, Conron M, Tsui A, Pasricha SR, Renwick WE, Antippa P, Irving LB. Endobronchial ultrasound-guided transbronchial needle aspiration for the evaluation of suspected lymphoma. J Thorac Oncol. 2010 Jun;5(6):804-9. doi: 10.1097/jto.0b013e3181d873be.
- Background: PubMed 24035300Sun J, Teng J, Yang H, Li Z, Zhang J, Zhao H, Garfield DH, Han B. Endobronchial ultrasound-guided transbronchial needle aspiration in diagnosing intrathoracic tuberculosis. Ann Thorac Surg. 2013 Dec;96(6):2021-7. doi: 10.1016/j.athoracsur.2013.07.005. Epub 2013 Sep 12.
- Background: PubMed 20382710Fujiwara T, Yasufuku K, Nakajima T, Chiyo M, Yoshida S, Suzuki M, Shibuya K, Hiroshima K, Nakatani Y, Yoshino I. The utility of sonographic features during endobronchial ultrasound-guided transbronchial needle aspiration for lymph node staging in patients with lung cancer: a standard endobronchial ultrasound image classification system. Chest. 2010 Sep;138(3):641-7. doi: 10.1378/chest.09-2006. Epub 2010 Apr 9.
- Background: PubMed 22525556Nakajima T, Anayama T, Shingyoji M, Kimura H, Yoshino I, Yasufuku K. Vascular image patterns of lymph nodes for the prediction of metastatic disease during EBUS-TBNA for mediastinal staging of lung cancer. J Thorac Oncol. 2012 Jun;7(6):1009-14. doi: 10.1097/JTO.0b013e31824cbafa.
- Background: PubMed 26228606Wang L, Wu W, Hu Y, Teng J, Zhong R, Han B, Sun J. Sonographic Features of Endobronchial Ultrasonography Predict Intrathoracic Lymph Node Metastasis in Lung Cancer Patients. Ann Thorac Surg. 2015 Oct;100(4):1203-9. doi: 10.1016/j.athoracsur.2015.04.143. Epub 2015 Jul 28.
- Background: PubMed 25121724Izumo T, Sasada S, Chavez C, Matsumoto Y, Tsuchida T. Endobronchial ultrasound elastography in the diagnosis of mediastinal and hilar lymph nodes. Jpn J Clin Oncol. 2014 Oct;44(10):956-62. doi: 10.1093/jjco/hyu105. Epub 2014 Aug 13.
- Background: PubMed 21437851Saftoiu A, Vilmann P, Gorunescu F, Janssen J, Hocke M, Larsen M, Iglesias-Garcia J, Arcidiacono P, Will U, Giovannini M, Dietrich C, Havre R, Gheorghe C, McKay C, Gheonea DI, Ciurea T; European EUS Elastography Multicentric Study Group. Accuracy of endoscopic ultrasound elastography used for differential diagnosis of focal pancreatic masses: a multicenter study. Endoscopy. 2011 Jul;43(7):596-603. doi: 10.1055/s-0030-1256314. Epub 2011 Mar 24.
- Background: PubMed 27898976Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.
- Individual participant dataSharing planned: Undecided. Investigators may release the database after the study, but no decision has been made yet.
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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.
Jiayuan Sun, MD, PhD study director
Shanghai Chest Hospital
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 Lymph Node Disease, run by Shanghai Chest Hospital at 1 site in China.
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 3 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: status not confirmed, as of the sponsor's last update on 2 Apr 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 China. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jiayuan Sun, MD, PhD (86-21-22200000 ext. 1511, jysun1976@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic efficacy of EBUS multimodal artificial intelligence prediction model based on videos (time frame: 6 months post-procedure). 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 NCT04328792, which also lists 8 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 2 Apr 2020.