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

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

Positron Emission Tomography/ Computed Tomography (PET/CT) Based Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

1500

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

Yes

Sampling Method

Non-Probability Sample

Study Population

Resected Stage I-III Non-small Cell Lung Cancer

Description

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.

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 the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
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 the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
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 the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
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 the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
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 the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
2023.5.1-2023.10.31
Accuracy
Time Frame: 2023.5.1-2023.10.31
The accuracy of the deep learning model in predicting the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
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

  • DLAHP

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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