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

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

Study Overview

• Status: Recruiting
• Conditions: Lung Adenocarcinoma; Artificial Intelligence; Whole Slide Image; IASLC Grading System
• Intervention / Treatment: Diagnostic test: Whole Slide Image based Deep Learning

• Study Type: Observational
• Enrollment (Estimated): 200

Contacts and Locations

Study Locations

• China
  • Guizhou
    • Zunyi, Guizhou, China
      • Recruiting
      • Affiliated Hospital of Zunyi Medical University
      • Contact: Yongxiang Song, Dr; Phone Number: 15505177258; Email: zhong961008@163.com
  • Jiangxi
    • Nanchang, Jiangxi, China
      • Recruiting
      • The First Affiliated Hospital of Nanchang University
      • Contact: Bentong Yu, Dr; Phone Number: 021-65115006; Email: 1151697503@qq.com
  • Zhejiang
    • Ningbo, Zhejiang, China
      • Recruiting
      • Ningbo HwaMei Hospital
      • Contact: Minglei Yang, Dr; Phone Number: 021-65115006; Email: almondjj@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

Resected Lung Adenocarcinoma

Description

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 Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Agreement rate of the predominant subtypes	Agreement rate between the deep learning model and pathologists in diagnosing the predominant growth patterns of lung adenocarcinoma.	2024.11.01-2024.12.31

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Specificity	The specificity of the deep learning model based on whole slide imge in predicting the novel grading system of resected lung adenocarcinoma. The novel grading system of lung adenocarcinoma includes grade I, grade II, and grade III. And the model will output the predictive values (grade I/grade II/grade III) of the grade for each patient with resected lung adenocarcinoma.	2023.5.1-2023.10.31
Positive predictive value	The positive predictive value of the deep learning model based on whole slide imge in predicting the novel grading system of resected lung adenocarcinoma. The novel grading system of lung adenocarcinoma includes grade I, grade II, and grade III. And the model will output the predictive values (grade I/grade II/grade III) of the grade for each patient with resected lung adenocarcinoma.	2023.5.1-2023.10.31
Negative predictive value	The negative predictive value of the deep learning model based on whole slide imge in predicting the novel grading system of resected lung adenocarcinoma. The novel grading system of lung adenocarcinoma includes grade I, grade II, and grade III. And the model will output the predictive values (grade I/grade II/grade III) of the grade for each patient with resected lung adenocarcinoma.	2023.5.1-2023.10.31
Accuracy	The accuracy of the deep learning model based on whole slide imge in predicting the novel grading system of resected lung adenocarcinoma. The novel grading system of lung adenocarcinoma includes grade I, grade II, and grade III. And the model will output the predictive values (grade I/grade II/grade III) of the grade for each patient with resected lung adenocarcinoma.	2023.5.1-2023.10.31

Collaborators and Investigators

• Sponsor: Shanghai Pulmonary Hospital, Shanghai, China
• Collaborators: Zunyi Medical College; Ningbo HwaMei Hospital, Zhejiang, China; The First Affiliated Hospital of Nanchang University, Jiangxi, China

Study record dates

Study Major Dates

• Study Start (Actual): October 15, 2024
• Primary Completion (Estimated): December 31, 2024
• Study Completion (Estimated): December 31, 2024

Study Registration Dates

• First Submitted: May 12, 2023
• First Submitted That Met QC Criteria: June 27, 2023
• First Posted (Actual): June 29, 2023

Study Record Updates

• Last Update Posted (Actual): October 21, 2024
• Last Update Submitted That Met QC Criteria: October 17, 2024
• Last Verified: October 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Carcinoma; Neoplasms, Glandular and Epithelial; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Adenocarcinoma; Adenocarcinoma of Lung
• Other Study ID Numbers: WSIGS

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No