Whole Slide Image for Predicting the Novel Grading System of Resected Lung Adenocarcinoma

Deep Learning Signature Based on Whole Slide Images for Predicting the Novel Grading System of Resected Lung Adenocarcinoma

The purpose of this study is to evaluate the performance of a whole slide image based deep learning signature for predicting the novel grading system in resected lung adenocarcinoma based on a multicenter prospective cohort.

Study Overview

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

• Study Type: Observational
• Enrollment (Estimated): 800

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. Pathological confirmation of primary lung adenocarcinoma;
2. Age ranging from 20-75 years;
3. Obtained written informed consent.

Exclusion Criteria:

1. Multiple lung lesions;
2. Poor quality of whole slide images;
3. Participants with incomplete clinical information;
4. Mucinous adenocarcinomas and variants;
5. 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
Area under the receiver operating characteristic curve	The area under the receiver operating characteristic curve 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

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity	The sensitivity 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

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): May 1, 2023
• Primary Completion (Estimated): October 31, 2023
• Study Completion (Estimated): October 31, 2023

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): June 29, 2023
• Last Update Submitted That Met QC Criteria: June 27, 2023
• Last Verified: June 1, 2023

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