Deep Learning Model for Pure Solid Nodules Classification

Chang Chen

Status: Enrolling

Conditions: Lung Cancer

Treatments: Diagnostic Test: CT-based deep learning model

Study type: Observational

Funder types: Other

Identifiers

NCT05542992

L21-022

Details and patient eligibility

About

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.

Enrollment: 260 estimated patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

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.