Artificial IntelligenceNCT04563416
Application of Artificial Intelligence for Early Diagnosis of Gastric Cancer During Optical Enhancement Magnifying Endoscopy
An observational study of Artificial Intelligence and Optical Enhancement Endoscopy, run by Shandong University at 1 site in China.
Trial summary
Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artificial Intelligence, Optical Enhancement Endoscopy, Magnifying Endoscopy
- How many people
- 80 participants
- Where
- 1 siteChina
- Study dates
- 10 Jul 2020to 30 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
- No healthy volunteers
- Adult, Older adult
Study population: Consecutive patients suspected of early gastric cancer and receive optical magnifying OE endoscopy examination.
Who may be able to join
- patients receive optical magnifying OE endoscopy examination
Who cannot join
- Patients with advanced cancer, lymphoma,active stage of ulcer and artificial ulcer after ESD were excluded.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Department of Gastroenterology, Qilu Hospital, Shandong University
Jinan, Shandong, 250012
Yanqing Li, PhD. MD., 18678827666, liyanqing@sdu.edu.cn
About this study
Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. we developed a new computer-aided system to assist endoscopists in identifying early gastric cancers in magnifying optical enhancement images.
Read the detailed description
Gastric cancer is the third most common cause of cancer-associated deaths worldwide especially in Asia.Early detection and treatment would cure the disease with 5-year survival rate greater than 90%.However, the sensitivity of conventional endoscopy with white-light imaging (C-WLI) in diagnosis of early gastric cancers (EGCs) is merely 40%. Magnifying image-enhanced endoscopy (IEE) techniques such as magnifying narrow band imaging (M-NBI) have been developed and 2 RCT report that white-light imaging combine with M-NBI can increase the sensitivity to 95%. The strategy that using white-light imaging to detect the suspicious lesion and using M-IEE techniques to make a diagnosis of early gastric cancer is recommend in screening endoscopy.
Optical enhancement (OE) which is one of the M-IEE techniques was developed by HOYA Co. (Tokyo, Japan) . This technology combines digital signal processing and optical filterers to clear display of mucosal microsurface (MS) and microvessel (MV). The advantage of OE is to overcome the darkness of NBI which leads to less usefulness for detect-ability in the full extended gastrointestinal lumen.Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner, and expertise with sub-optimal inter-observer agreement is essential for the use of M-IEE.
Nowadays, Artificial intelligence (AI) using deep machine learning has made a major breakthrough in gastroenterology, which using gradient descent method and backpropagation to automatically extract specific images features. The diagnostic accuracy in identifying upper gastrointestinal cancer was 0.955 in C-WLI . Polyps can be identified in real time with 96% accuracy in screening colonoscopy. AI show an outstanding application in detection and diagnosis.
This study aims to develop a M-OE assistance model in the diagnosis of EGCs by distinguishing cancer or not.
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 |
|---|---|---|
| Patients who need undergo magnifying endoscopy | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
the diagnosis efficiency of the computer-assist diagnosis tool
the sensitivity, specificity and accuracy of the computer-assist diagnosis tool
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Sep 2020
- Study start10 Jul 2020
- Primary completion30 Nov 2020 (estimated)
- Study completion30 Dec 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Artificial IntelligenceStudies 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
- Other
- Time perspective
- Other
- Enrolment
- 80 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yanqing Li, Shandong University
- Investigators
- Yanqing Li, PHD, Principal investigator, Study Principal Investigator Qilu Hospital, Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04563416
- Sponsor's protocol ID
- 2018SDU-QILU-3
- Status last verified
- Sep 2020
- First submitted
- 22 Sep 2020
- Last updated
- 24 Sep 2020
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Ma M, Li Z, Yu T, Liu G, Ji R, Li G, Guo Z, Wang L, Qi Q, Yang X, Qu J, Wang X, Zuo X, Ren H, Li Y. Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos. Front Oncol. 2022 Aug 5;12:945904. doi: 10.3389/fonc.2022.945904. eCollection 2022.
PubMed 35992850
Study at a glance
From the registry record, last updated by the sponsor on 24 Sep 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Artificial Intelligence for Early Diagnosis of Gastric Cancer During Optical Enhancement Magnifying Endoscopy
- ClinicalTrials.gov ID
- NCT04563416
- Acronym / sponsor ID
- 2018SDU-QILU-3
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 10 Jul 2020 (actual)
- Primary completion
- 30 Nov 2020 (estimated)
- Study completion
- 30 Dec 2020 (estimated)
- First posted
- 24 Sep 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.
Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. we developed a new computer-aided system to assist endoscopists in identifying early gastric cancers in magnifying optical enhancement images.
Detailed description
Gastric cancer is the third most common cause of cancer-associated deaths worldwide especially in Asia.Early detection and treatment would cure the disease with 5-year survival rate greater than 90%.However, the sensitivity of conventional endoscopy with white-light imaging (C-WLI) in diagnosis of early gastric cancers (EGCs) is merely 40%. Magnifying image-enhanced endoscopy (IEE) techniques such as magnifying narrow band imaging (M-NBI) have been developed and 2 RCT report that white-light imaging combine with M-NBI can increase the sensitivity to 95%. The strategy that using white-light imaging to detect the suspicious lesion and using M-IEE techniques to make a diagnosis of early gastric cancer is recommend in screening endoscopy.
Optical enhancement (OE) which is one of the M-IEE techniques was developed by HOYA Co. (Tokyo, Japan) . This technology combines digital signal processing and optical filterers to clear display of mucosal microsurface (MS) and microvessel (MV). The advantage of OE is to overcome the darkness of NBI which leads to less usefulness for detect-ability in the full extended gastrointestinal lumen.Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner, and expertise with sub-optimal inter-observer agreement is essential for the use of M-IEE.
Nowadays, Artificial intelligence (AI) using deep machine learning has made a major breakthrough in gastroenterology, which using gradient descent method and backpropagation to automatically extract specific images features. The diagnostic accuracy in identifying upper gastrointestinal cancer was 0.955 in C-WLI . Polyps can be identified in real time with 96% accuracy in screening colonoscopy. AI show an outstanding application in detection and diagnosis.
This study aims to develop a M-OE assistance model in the diagnosis of EGCs by distinguishing cancer or not.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients who need undergo magnifying endoscopy | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Consecutive patients suspected of early gastric cancer and receive optical magnifying OE endoscopy examination.
Key inclusion criteria (1 listed)
- patients receive optical magnifying OE endoscopy examination
Key exclusion criteria (1 listed)
- Patients with advanced cancer, lymphoma,active stage of ulcer and artificial ulcer after ESD were excluded.
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
the diagnosis efficiency of the computer-assist diagnosis tool
Time frame: 12 months
the sensitivity, specificity and accuracy of the computer-assist diagnosis tool
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Sep 2020.
- Derived from this study: PubMed 35992850Ma M, Li Z, Yu T, Liu G, Ji R, Li G, Guo Z, Wang L, Qi Q, Yang X, Qu J, Wang X, Zuo X, Ren H, Li Y. Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos. Front Oncol. 2022 Aug 5;12:945904. doi: 10.3389/fonc.2022.945904. eCollection 2022.
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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.
Yanqing Li, PHD principal investigator
Study Principal Investigator Qilu Hospital, Shandong University
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 Artificial Intelligence and Optical Enhancement Endoscopy, run by Shandong University 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 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: status not confirmed, as of the sponsor's last update on 24 Sep 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 Yanqing Li, PHD (86-531-82169236, liyanqing@sdu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
the diagnosis efficiency of the computer-assist diagnosis tool (time frame: 12 months). 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 NCT04563416, which also lists 1 linked publication.
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 24 Sep 2020.