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

• Sponsor: Shanghai Pulmonary Hospital, Shanghai, China (Other)
• Overall Status: Recruiting
• CT.gov ID: NCT05925764
• Collaborator: Ningbo HwaMei Hospital, Zhejiang, China (Other), Zunyi Medical College (Other), The First Affiliated Hospital of Nanchang University, Jiangxi, China (Other)
• Enrollment: 800
• Locations: 3
• Anticipated Duration (Months): 6
• Patients Per Site: 266.7
• Patients Per Site Per Month: 44.4

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Lung Adenocarcinoma; Whole Slide Image; Novel Grading System	Diagnostic Test: Whole Slide Image based Deep Learning Signature

Study Design

• Study Type: Observational
• Anticipated Enrollment: 800 participants
• Observational Model: Cohort
• Time Perspective: Prospective
• Official Title: Deep Learning Signature Based on Whole Slide Images for Predicting the Novel Grading System of Resected Lung Adenocarcinoma
• Actual Study Start Date: May 1, 2023
• Anticipated Primary Completion Date: Oct 31, 2023
• Anticipated Study Completion Date: Oct 31, 2023

Outcome Measures

Primary Outcome Measures

1. Area under the receiver operating characteristic curve [2023.5.1-2023.10.31]

   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.

Secondary Outcome Measures

1. Sensitivity [2023.5.1-2023.10.31]

   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.

Other Outcome Measures

1. Specificity [2023.5.1-2023.10.31]

   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.

2. Positive predictive value [2023.5.1-2023.10.31]

   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.

3. Negative predictive value [2023.5.1-2023.10.31]

   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.

4. Accuracy [2023.5.1-2023.10.31]

   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.

Eligibility Criteria

Criteria

• Ages Eligible for Study: 20 Years to 75 Years
• Sexes Eligible for Study: All

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.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Affiliated Hospital of Zunyi Medical University	Zunyi	Guizhou	China
2	The First Affiliated Hospital of Nanchang University	Nanchang	Jiangxi	China
3	Ningbo HwaMei Hospital	Ningbo	Zhejiang	China

Sponsors and Collaborators

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

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Chang Chen, Professor, Shanghai Pulmonary Hospital, Shanghai, China
• ClinicalTrials.gov Identifier: NCT05925764
• Other Study ID Numbers: WSIGS
• First Posted: Jun 29, 2023
• Last Update Posted: Jun 29, 2023
• Last Verified: Jun 1, 2023

• Individual Participant Data (IPD) Sharing Statement: No
• Plan to Share IPD: No
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Additional relevant MeSH terms: Adenocarcinoma; Adenocarcinoma of Lung

Study Results

No Results Posted as of Jun 29, 2023