WSI Based DL for Diagnosing the IASLC Grading System of Lung Adenocarcinoma
- Conditions
- Lung AdenocarcinomaWhole Slide ImageIASLC Grading SystemArtificial Intelligence
- Registration Number
- NCT05925764
- Lead Sponsor
- Shanghai Pulmonary Hospital, Shanghai, China
- Brief 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 200
- Age ranging from 18-85 years old;
- Pathological confirmation of primary lung adenocarcinoma after surgery;
- Obtained written informed consent.
- Multiple lung lesions;
- Poor quality of whole slide images;
- Mucinous adenocarcinomas and variants;
- Participants who have received neoadjuvant therapy.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Agreement rate of the IASLC grading system 2024.11.01-2024.12.31 Agreement rate between the deep learning model and pathologists in diagnosing the IASLC grade of lung adenocarcinoma.
- Secondary Outcome Measures
Name Time Method Agreement rate of the predominant subtypes 2024.11.01-2024.12.31 Agreement rate between the deep learning model and pathologists in diagnosing the predominant growth patterns of lung adenocarcinoma.
Trial Locations
- Locations (3)
Affiliated Hospital of Zunyi Medical University
🇨🇳Zunyi, Guizhou, China
The First Affiliated Hospital of Nanchang University
🇨🇳Nanchang, Jiangxi, China
Ningbo HwaMei Hospital
🇨🇳Ningbo, Zhejiang, China