Imaging-based Deep Learning for Lung Cancer Diagnosis and Staging

November 15, 2021 updated by: Yang Jin, Wuhan Union Hospital, China

The Role of CNN Architecture-based Transfer Learning of Medical Imaging in Lung Cancer Diagnosis and Staging

Lung cancer diagnosis and staging are two fundamental and critical issue in clinical lung cancer management and therapeutic decision-making. Invasive procedures for pathologic analysis are gold standard for diagnosis and staging, however, invasive procedures related-complications are inevitable. Noninvasive medical imaging is a powerful tool, however there is almost no room for improvement just according to the experience of radiologist and clinician. The researchers will investigate the role of computer based deep learning of medical imaging in the diagnosis of lesion of lung, lymph node and other sites suspected with metastasis.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Radiologist

Study Type

Observational

Enrollment (Anticipated)

500

Contacts and Locations

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

Study Contact

Study Locations

  • China
    • Hubei
      • Wuhan, Hubei, China, 430000
        • Recruiting
        • Wuhan Union 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

18 years to 75 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Lung cancer patients with PET/CT or CT examination before any cancer-specific treatment

Description

Inclusion Criteria:

  • Pathological diagnosis of lung cancer
  • PET/CT or CT examination before any cancer-specific treatment

Exclusion Criteria:

  • A history of other malignancies

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

  • Observational Models: Cohort
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
cancer cell involvement predicted by deep learning
the participants with lesions of lung, lymph node or other sites predicted as positive for cancer cell involvement by imaging based deep learning.
Procedure: surgery
treatment intent surgery
Procedure: punture
diagnostic punture
no cancer cell involvement predicted by deep learning
the participants with lesions of lung, lymph node or other sites predicted as negative for cancer cell involvement by imaging based deep learning.
Procedure: surgery
treatment intent surgery
Procedure: punture
diagnostic punture

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
pathologic result revealed cancer cell involvement in lesion
Time Frame: 1 month after the pathologic test
1 month after the pathologic test

Collaborators and Investigators

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

Sponsor

Wuhan Union Hospital, 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, 2018

Primary Completion (Anticipated)

December 30, 2021

Study Completion (Anticipated)

May 1, 2024

Study Registration Dates

First Submitted

June 24, 2019

First Submitted That Met QC Criteria

June 24, 2019

First Posted (Actual)

June 27, 2019

Study Record Updates

Last Update Posted (Actual)

November 16, 2021

Last Update Submitted That Met QC Criteria

November 15, 2021

Last Verified

November 1, 2021

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AFDSFA

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

Undecided

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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