Deep Learning Signature for Predicting Occult Nodal Metastasis of Clinical N0 Lung Cancer

Shanghai Pulmonary Hospital, Shanghai, China

Status: Enrolling

Conditions: Non-small Cell Lung Cancer

Treatments: Diagnostic Test: PET/CT-based Deep Learning Signature

Study type: Observational

Funder types: Other

Identifiers

NCT05425134

DLNMS

Details and patient eligibility

About

The purpose of this study is to evaluate the performance of a PET/CT-based deep learning signature for predicting occult nodal metastasis of clinical stage N0 non-small cell lung cancer in a multicenter prospective cohort.

Enrollment: 5,000 estimated patients

Sex: All

Ages: 20 to 75 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

(1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) The maximum short-axis diameter of N1 and N2 lymph nodes less than 1 cm on CT scan; (3) The SUVmax of N1 and N2 lymph nodes less than 2.5; (4) Pathological confirmation of primary NSCLC; (5) Age ranging from 20-75 years; (6) Obtained written informed consent.

Exclusion criteria

(1) Multiple lung lesions; (2) Poor quality of PET-CT images; (3) Participants with incomplete clinical information; (4) Participants not receiving systematic lymph node dissection; (5) Participants who have received neoadjuvant therapy.