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

Shanghai Pulmonary Hospital, Shanghai, China200 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

200

Conditions

Lung Adenocarcinoma Whole Slide Image IASLC Grading System Artificial Intelligence

Interventions

Whole Slide Image based Deep LearningDIAGNOSTIC_TEST

Whole Slide Image Based Deep Learning for Diagnosing the IASLC Grading System of Lung Adenocarcinoma

Eligibility

Sex: ALLMin age: 18 YearsMax age: 85 Years

Medical Language ↔ Plain English

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.

Locations (3)

Affiliated Hospital of Zunyi Medical University

Zunyi, Guizhou, China

RECRUITING

The First Affiliated Hospital of Nanchang University

Nanchang, Jiangxi, China

RECRUITING

Ningbo HwaMei Hospital

Ningbo, Zhejiang, China

RECRUITING

Outcomes

Primary Outcomes

Agreement rate of the IASLC grading system

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

Time frame: 2024.11.01-2024.12.31

Secondary Outcomes

Agreement rate of the predominant subtypes

Agreement rate between the deep learning model and pathologists in diagnosing the predominant growth patterns of lung adenocarcinoma.

Time frame: 2024.11.01-2024.12.31

Data from ClinicalTrials.gov

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