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NCT Number: NCT05925764

WSI Based DL for Diagnosing the IASLC 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.

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Key information

Conditions

Lung Adenocarcinoma Adenocarcinoma Adenocarcinoma of Lung Artificial Intelligence Carcinoma IASLC Grading System Lung Neoplasms Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Respiratory Tract Neoplasms Thoracic Neoplasms Whole Slide Image

Age range

18 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China

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Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age ranging from 18-85 years old;
  • Pathological confirmation of primary lung adenocarcinoma after surgery;
  • Obtained written informed consent.

Exclusion criteria

  • Multiple lung lesions;
  • Poor quality of whole slide images;
  • Mucinous adenocarcinomas and variants;
  • Participants who have received neoadjuvant therapy.

Treatment and study plan

Whole Slide Image based Deep Learning

Diagnostic Test

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

Primary outcomes

  1. 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.

Secondary outcomes

  1. Agreement rate of the predominant subtypes

    Time frame: 2024.11.01-2024.12.31

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

Sponsors and collaborators

Lead sponsor

Shanghai Pulmonary Hospital, Shanghai, China

Other

Registry information

Official study title

Whole Slide Image Based Deep Learning for Diagnosing the International Association for the Study of Lung Cancer Proposed Grading System of Lung Adenocarcinoma

Important dates

Study start
2024
Primary completion
2024
Study completion
2024
First posted
Jun 29, 2023
Registry last updated
Oct 21, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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