NCT05925764

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.

Trial Health

57
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
200

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Oct 2024

Shorter than P25 for all trials

Geographic Reach
1 country

3 active sites

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

May 12, 2023

Completed
2 months until next milestone

First Posted

Study publicly available on registry

June 29, 2023

Completed
1.3 years until next milestone

Study Start

First participant enrolled

October 15, 2024

Completed
3 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 31, 2024

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2024

Completed
Last Updated

October 21, 2024

Status Verified

October 1, 2024

Enrollment Period

3 months

First QC Date

May 12, 2023

Last Update Submit

October 17, 2024

Conditions

Lung AdenocarcinomaWhole Slide ImageIASLC Grading SystemArtificial Intelligence

Outcome Measures

Primary Outcomes (1)

  • Agreement rate of the IASLC grading system

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

    2024.11.01-2024.12.31

Secondary Outcomes (1)

  • Agreement rate of the predominant subtypes

    2024.11.01-2024.12.31

Interventions

Whole Slide Image based Deep LearningDIAGNOSTIC_TEST

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

Eligibility Criteria

Age18 Years - 85 Years
Sexall
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Resected Lung Adenocarcinoma

You may qualify if:

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

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (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

MeSH Terms

Conditions

Adenocarcinoma of Lung

Condition Hierarchy (Ancestors)

AdenocarcinomaCarcinomaNeoplasms, Glandular and EpithelialNeoplasms by Histologic TypeNeoplasmsLung NeoplasmsRespiratory Tract NeoplasmsThoracic NeoplasmsNeoplasms by Site

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Professor

Study Record Dates

First Submitted

May 12, 2023

First Posted

June 29, 2023

Study Start

October 15, 2024

Primary Completion

December 31, 2024

Study Completion

December 31, 2024

Last Updated

October 21, 2024

Record last verified: 2024-10

Data Sharing

IPD Sharing
Will not share

Locations

CN(3)