Radiomics Multifactorial Biomarker for Pulmonary Nodules (RMBPN)

March 11, 2019 updated by: Maastricht University

Radiomics and Clinical Variables Can Differentiate Malignant Nodules and Detect Invasive Adenocarcinoma in Pulmonary Nodules: a Multi-center Study

The investigators aim to investigate the utility of radiomics to differentiate malignant nodules from benign nodules and invasive adenocarcinoma from non-invasive adenocarcinoma.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

With the development of computed tomography (CT) equipment and the increasing use of lung cancer screening programs with low-dose CT, a growing number of early-stage lung cancers were detected so that a large number of patients have undergone surgery.

Although a number of radiological studies have been used morphological signs so-called semantic features to make a differential diagnosis, it is still hard to apply by clinician because pulmonary nodules especially ground-glass nodules and small size nodules have atypical radiology signs and have strong subjectivity from different observers. Recently, CT-based radiomics, extracting the quantitative high-throughput features from medical images and facilitating clinical decision-making system, showed a good performance to predict diagnosis and prognosis of diverse cancer.

Therefore, the proposed project aims to develop and validate radiomics models based on CT images to identify malignant nodules and then to discriminate the different types of lung adenocarcinoma in patients with pulmonary nodules.

Study Type

Observational

Enrollment (Actual)

800

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
    • Liaoning
      • Dalian, Liaoning, China, 116000
        • Affiliated Zhongshan Hospital of Dalian University

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 and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

Patients with pulmonary nodules in the collaborating institutes.

Description

Inclusion Criteria:

  • intraoperative frozen section diagnosis and final pathology diagnosis are available
  • preoperative standard non-enhanced CT is available
  • Pathologically confirmed

Exclusion Criteria:

  • with a previous history of radiation therapy, chemotherapy or biopsy
  • the time interval between the CT examination and surgery was more than two weeks

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: Retrospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Training dataset
No interventions
Diagnostic test: radiomics
The high-throughput extraction of large amounts of quantitative image features from medical images
External validation1
No interventions
Diagnostic test: radiomics
The high-throughput extraction of large amounts of quantitative image features from medical images
External validation2
No interventions
Diagnostic test: radiomics
The high-throughput extraction of large amounts of quantitative image features from medical images

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Malignant nodules classifier
Time Frame: 30 days
Model based on Radiomic that can differentiate malignant nodules from benign nodules.
30 days
Invasive adenocarcinoma classifier
Time Frame: 30 days
Model based on Radiomic that can differentiate invasive adenocarcinoma from non-invasive adenocarcinoma.
30 days

Collaborators and Investigators

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

Sponsor

Maastricht University

Collaborators

The Second Affiliated Hospital of Dalian Medical University
The Affiliated Zhongshan Hospital of Dalian University
The Fifth Hospital of Dalian

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

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)

July 11, 2018

Primary Completion (Actual)

January 11, 2019

Study Completion (Actual)

February 1, 2019

Study Registration Dates

First Submitted

March 11, 2019

First Submitted That Met QC Criteria

March 11, 2019

First Posted (Actual)

March 13, 2019

Study Record Updates

Last Update Posted (Actual)

March 13, 2019

Last Update Submitted That Met QC Criteria

March 11, 2019

Last Verified

January 1, 2019

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • UM2019DLABGY1

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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