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ClinicalTrials.gov ID

NCT05542992

Lead Sponsor

Chang ChenOTHER

Overall Status

Unknown status

Study Type

Observational

Enrollment

260Estimated

Last Update Posted

Sep 16, 2022Actual

Start Date

Jan 1, 2022Actual

Primary Completion Date

Dec 31, 2023Estimated

Completion Date

Dec 31, 2023Estimated

Official Title

Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study

Brief Summary

The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.

• Lung Cancer

• CT-based deep learning model

Ages Eligible for Study

Adult (18-64)Older Adult (65+)

Sexes Eligible for Study

All

Accepts Healthy Volunteers

No

Eligibility Criteria

Inclusion Criteria:

• Participants scheduled for surgery for radiological finding of pulmonary pure-solid lesions from the preoperative thin-section CT scans;
• The maximum short-axis diameter of lymph nodes less than 3 cm on CT scan;
• Age ranging from 18-75 years;
• definied pathological examination report available;
• Obtained written informed consent.

Exclusion Criteria:

• Multiple lung lesions;
• Poor quality of CT images;
• Participants with incomplete clinical information;
• Participants who have received neoadjuvant therapy before initial CT evaluation.

Observational Model

Cohort

Time Perspective

Prospective

Lead Sponsor

Chang ChenOTHER

Collaborators

Ningbo No.2 HospitalOTHER

Zunyi Medical CollegeOTHER

The First Affiliated Hospital of Nanchang UniversityOTHER

LanZhou UniversityOTHER

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