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

June 27, 2023 updated by: Chang Chen, Shanghai Pulmonary Hospital, Shanghai, China

An Integration of a Computed Tomography/Positron Emission Tomography/Whole Slide Image (CT/PET/WSI) Based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer: A Multicenter Study

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

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Guizhou
      • Zunyi, Guizhou, China
        • Recruiting
        • Affiliated Hospital of Zunyi Medical University
        • Contact:
    • Jiangxi
      • Nanchang, Jiangxi, China
        • Recruiting
        • The First Affiliated Hospital of Nanchang University
        • Contact:
    • Zhejiang
      • Ningbo, Zhejiang, China
        • Recruiting
        • Ningbo HwaMei Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

Resected Stage I-III NSCLC following neoadjuvant chemoimmunotherapy

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area under the receiver operating characteristic curve
Time Frame: 2023.5.1-2023.10.31
The area under the receiver operating characteristic curve (ROC) of the deep learning model in predicting complete pathological response (CPR). CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.
2023.5.1-2023.10.31

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity
Time Frame: 2023.5.1-2023.10.31
The sensitivity of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.
2023.5.1-2023.10.31

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Specificity
Time Frame: 2023.5.1-2023.10.31
The specificity of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.
2023.5.1-2023.10.31
Positive predictive value
Time Frame: 2023.5.1-2023.10.31
The positive predictive value of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.
2023.5.1-2023.10.31
Negative predictive value
Time Frame: 2023.5.1-2023.10.31
The negative predictive value of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.
2023.5.1-2023.10.31
Accuracy
Time Frame: 2023.5.1-2023.10.31
The accuracy of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.
2023.5.1-2023.10.31

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shanghai Pulmonary Hospital, Shanghai, China

Collaborators

Zunyi Medical College
Ningbo HwaMei Hospital, Zhejiang, China
The First Affiliated Hospital of Nanchang University, Jiangxi, China

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 1, 2023

Primary Completion (Estimated)

October 31, 2023

Study Completion (Estimated)

October 31, 2023

Study Registration Dates

First Submitted

May 12, 2023

First Submitted That Met QC Criteria

June 27, 2023

First Posted (Actual)

June 29, 2023

Study Record Updates

Last Update Posted (Actual)

June 29, 2023

Last Update Submitted That Met QC Criteria

June 27, 2023

Last Verified

June 1, 2023

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • DLCPR

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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