Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer
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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Locations
-
-
Guizhou
-
Zunyi, Guizhou, China
- Recruiting
- Affiliated Hospital of Zunyi Medical University
-
Contact:
- Yongxiang Song, Dr
- Phone Number: 15505177258
- Email: zhong961008@163.com
-
-
Jiangxi
-
Nanchang, Jiangxi, China
- Recruiting
- The First Affiliated Hospital of Nanchang University
-
Contact:
- Bentong Yu, Dr
- Phone Number: 021-65115006
- Email: 1151697503@qq.com
-
-
Zhejiang
-
Ningbo, Zhejiang, China
- Recruiting
- Ningbo HwaMei Hospital
-
Contact:
- Minglei Yang, Dr
- Phone Number: 021-65115006
- Email: almondjj@163.com
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Age ranging from 20-75 years;
- Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC;
- Obtained written informed consent.
Exclusion Criteria:
- Missing image data;
- Pathological N3 disease.
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
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
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
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
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- DLCPR
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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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