Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer

Shanghai Pulmonary Hospital, Shanghai, China

Status: Enrolling

Conditions: Neoadjuvant Chemoimmunotherapy; Non-small Cell Lung Cancer; Complete Pathological Response

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

Study type: Observational

Funder types: Other

Identifiers

NCT05925751

DLCPR

Details and patient eligibility

About

The purpose of this study is to evaluate the performance of a CT/PET/ WSI-based deep learning signature for predicting complete pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer

Enrollment: 100 estimated patients

Sex: All

Ages: 20 to 75 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Age ranging from 20-75 years;
2. Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC;
3. Obtained written informed consent.

Exclusion criteria

1. Missing image data;
2. Pathological N3 disease.