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

NCT ID: NCT05925751

Last Updated: 2023-06-29

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-05-01

Study Completion Date

2023-10-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Non-small Cell Lung Cancer Neoadjuvant Chemoimmunotherapy Complete Pathological Response

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

CT/PET/WSI-based Deep Learning Signature

CT/PET/WSI-based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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.
Minimum Eligible Age

20 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

Ningbo No.2 Hospital

OTHER

Sponsor Role collaborator

Zunyi Medical College

OTHER

Sponsor Role collaborator

The First Affiliated Hospital of Nanchang University

OTHER

Sponsor Role collaborator

Shanghai Pulmonary Hospital, Shanghai, China

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Chang Chen

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Affiliated Hospital of Zunyi Medical University

Zunyi, Guizhou, China

Site Status RECRUITING

The First Affiliated Hospital of Nanchang University

Nanchang, Jiangxi, China

Site Status RECRUITING

Ningbo HwaMei Hospital

Ningbo, Zhejiang, China

Site Status RECRUITING

Countries

Review the countries where the study has at least one active or historical site.

China

Facility Contacts

Find local site contact details for specific facilities participating in the trial.

Yongxiang Song, Dr

Role: primary

Bentong Yu, Dr

Role: primary

Minglei Yang, Dr

Role: primary

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

DLCPR

Identifier Type: -

Identifier Source: org_study_id