NCT05542992RecruitingNot Applicable
Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study
Chang Chen5 sites in 1 country260 target enrollmentStarted: January 1, 2022Last updated:
Conditions
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 260
- Locations
- 5
- Primary Endpoint
- AUC
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to 75 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
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.
Outcomes
Primary Outcomes
AUC
Time Frame: 2022.01-2023.12
Area under the curve of the receiver operating characteristic
Secondary Outcomes
- Specificity(2022.01-2023.12)
- PPV(2022.01-2023.12)
- NPV(2022.01-2023.12)
- Accuracy(2022.01-2023.12)
- sensitivity(2022.01-2023.12)
Investigators
Chang Chen
Professor
Shanghai Pulmonary Hospital, Shanghai, China
Study Sites (5)
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