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WSI Based DL for Diagnosing the IASLC Grading System of Lung Adenocarcinoma

Recruiting
Conditions
Lung Adenocarcinoma
Whole Slide Image
IASLC Grading System
Artificial 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
Inclusion Criteria
  1. Age ranging from 18-85 years old;
  2. Pathological confirmation of primary lung adenocarcinoma after surgery;
  3. Obtained written informed consent.
Exclusion Criteria
  1. Multiple lung lesions;
  2. Poor quality of whole slide images;
  3. Mucinous adenocarcinomas and variants;
  4. Participants who have received neoadjuvant therapy.

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Agreement rate of the IASLC grading system2024.11.01-2024.12.31

Agreement rate between the deep learning model and pathologists in diagnosing the IASLC grade of lung adenocarcinoma.

Secondary Outcome Measures
NameTimeMethod
Agreement rate of the predominant subtypes2024.11.01-2024.12.31

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

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