Lung AdenocarcinomaNCT05925764
WSI Based DL for Diagnosing the IASLC Grading System of Lung Adenocarcinoma
An observational study of Lung Adenocarcinoma and Whole Slide Image, run by Shanghai Pulmonary Hospital, Shanghai, China at 3 sites in China.
Trial summary
The purpose of this study is to evaluate the performance of a whole slide image based deep learning model for diagnosing the IASLC grading system in resected lung adenocarcinoma based on a multicenter prospective cohort. Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Lung Adenocarcinoma, Whole Slide Image, IASLC Grading System
- What is being tested?
- Whole Slide Image based Deep Learning
- How many people
- 200 participants
- Where
- 3 sitesChina
- Study dates
- 15 Oct 2024to 31 Dec 2024
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 to 85 years
- All sexes
- Adult, Older adult
Study population: Resected Lung Adenocarcinoma
Who may be able to join
- Age ranging from 18-85 years old;
- Pathological confirmation of primary lung adenocarcinoma after surgery;
- Obtained written informed consent.
Who cannot join
- Multiple lung lesions;
- Poor quality of whole slide images;
- Mucinous adenocarcinomas and variants;
- Participants who have received neoadjuvant therapy.
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China3 sites, 3 recruiting
- Recruiting
The First Affiliated Hospital of Nanchang University
Nanchang, Jiangxi
Bentong Yu, Dr, 021-65115006, 1151697503@qq.com
- Recruiting
Ningbo HwaMei Hospital
Ningbo, Zhejiang
Minglei Yang, Dr, 021-65115006, almondjj@163.com
- Recruiting
Affiliated Hospital of Zunyi Medical University
Zunyi, Guizhou
Yongxiang Song, Dr, 15505177258, zhong961008@163.com
About this study
Official title: Whole Slide Image Based Deep Learning for Diagnosing the International Association for the Study of Lung Cancer Proposed Grading System of Lung Adenocarcinoma
The purpose of this study is to evaluate the performance of a whole slide image based deep learning model for diagnosing the IASLC grading system in resected lung adenocarcinoma based on a multicenter prospective cohort.
- Being tested
- Whole Slide Image based Deep Learning
- Medical subject headings
- Adenocarcinoma of Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Whole Slide Image based Deep LearningDiagnostic test
Whole Slide Image Based Deep Learning for Diagnosing the IASLC Grading System of Lung Adenocarcinoma
What is being measured
The primary outcome is the main question the study is designed to answer.
Agreement rate of the IASLC grading system
Agreement rate between the deep learning model and pathologists in diagnosing the IASLC grade of lung adenocarcinoma.
Measured: 2024.11.01-2024.12.31
Secondary outcomes (1)
Agreement rate of the predominant subtypes
Agreement rate between the deep learning model and pathologists in diagnosing the predominant growth patterns of lung adenocarcinoma.
Measured: 2024.11.01-2024.12.31
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jun 2023
- Study start15 Oct 2024
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung AdenocarcinomaStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai Pulmonary Hospital, Shanghai, China
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chang Chen, Shanghai Pulmonary Hospital, Shanghai, China
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05925764
- Sponsor's protocol ID
- WSIGS
- Status last verified
- Oct 2024
- First submitted
- 12 May 2023
- Last updated
- 21 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Whole Slide Image Based Deep Learning for Diagnosing the International Association for the Study of Lung Cancer Proposed Grading System of Lung Adenocarcinoma
- ClinicalTrials.gov ID
- NCT05925764
- Acronym / sponsor ID
- WSIGS
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Shanghai Pulmonary Hospital, Shanghai, ChinaUniversity, hospital or other
- Study start
- 15 Oct 2024 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 29 Jun 2023 (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.
The purpose of this study is to evaluate the performance of a whole slide image based deep learning model for diagnosing the IASLC grading system in resected lung adenocarcinoma based on a multicenter prospective cohort.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Resected Lung Adenocarcinoma
Key inclusion criteria (3 listed)
- Age ranging from 18-85 years old;
- Pathological confirmation of primary lung adenocarcinoma after surgery;
- Obtained written informed consent.
Key exclusion criteria (4 listed)
- Multiple lung lesions;
- Poor quality of whole slide images;
- Mucinous adenocarcinomas and variants;
- Participants who have received neoadjuvant therapy.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Whole Slide Image based Deep LearningWhole Slide Image Based Deep Learning for Diagnosing the IASLC Grading System of Lung Adenocarcinoma | Diagnostic test | – |
Primary outcome
Agreement rate of the IASLC grading system
Time frame: 2024.11.01-2024.12.31
Agreement rate between the deep learning model and pathologists in diagnosing the IASLC grade of lung adenocarcinoma.
1 secondary outcome
Agreement rate of the predominant subtypes
Time frame: 2024.11.01-2024.12.31
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Oct 2024.
- Individual participant dataSharing planned: No
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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.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Adenocarcinoma and Whole Slide Image, run by Shanghai Pulmonary Hospital, Shanghai, China at 3 sites 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 4 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: looking for participants now, as of the sponsor's last update on 21 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement rate of the IASLC grading system (time frame: 2024.11.01-2024.12.31). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 21 Oct 2024.