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

Shanghai Pulmonary Hospital, Shanghai, China

Status: Enrolling

Conditions: Artificial Intelligence; Whole Slide Image; IASLC Grading System; Lung Adenocarcinoma

Treatments: Diagnostic Test: Whole Slide Image based Deep Learning

Study type: Observational

Funder types: Other

Identifiers

NCT05925764

WSIGS

Details and patient eligibility

About

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.

Enrollment: 200 estimated patients

Sex: All

Ages: 18 to 85 years old

Volunteers: No Healthy Volunteers

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.