Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer

Shanghai Pulmonary Hospital, Shanghai, China

Status: Enrolling

Conditions: Spread Through Air Space; Visceral Pleural Invasion; Non-small Cell Lung Cancer; Lymphovascular Invasion

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

Study type: Observational

Funder types: Other

Identifiers

NCT05925738

DLAHP

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 aggressive histological pattern in resected non-small cell lung cancer based on a multicenter prospective cohort.

Enrollment: 1,500 estimated patients

Sex: All

Ages: 20 to 75 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

(1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) Pathological confirmation of primary NSCLC; (3) Age ranging from 20-75 years; (4) 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 who have received neoadjuvant therapy.