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Radiomics Multifactorial Biomarker for Pulmonary Nodules (RMBPN)

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ClinicalTrials.gov Identifier: NCT03872362
Recruitment Status : Completed
First Posted : March 13, 2019
Last Update Posted : March 13, 2019
Sponsor:
Collaborators:
The Affiliated Zhongshan Hospital of Dalian University
The Second Affiliated Hospital of Dalian Medical University
The Fifth Hospital of Dalian
Information provided by (Responsible Party):
Maastricht University

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

Condition or disease Intervention/treatment
Lung Neoplasms Carcinoma, Non-Small-Cell Lung Lung Diseases Neoplasms Pathology Diagnostic Test: radiomics

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.


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Study Type : Observational
Actual Enrollment : 800 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Radiomics and Clinical Variables Can Differentiate Malignant Nodules and Detect Invasive Adenocarcinoma in Pulmonary Nodules: a Multi-center Study
Actual Study Start Date : July 11, 2018
Actual Primary Completion Date : January 11, 2019
Actual Study Completion Date : February 1, 2019

Resource links provided by the National Library of Medicine


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




Primary Outcome Measures :
  1. Malignant nodules classifier [ Time Frame: 30 days ]
    Model based on Radiomic that can differentiate malignant nodules from benign nodules.

  2. Invasive adenocarcinoma classifier [ Time Frame: 30 days ]
    Model based on Radiomic that can differentiate invasive adenocarcinoma from non-invasive adenocarcinoma.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Probability Sample
Study Population
Patients with pulmonary nodules in the collaborating institutes.
Criteria

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03872362


Locations
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China, Liaoning
Affiliated Zhongshan Hospital of Dalian University
Dalian, Liaoning, China, 116000
Sponsors and Collaborators
Maastricht University
The Affiliated Zhongshan Hospital of Dalian University
The Second Affiliated Hospital of Dalian Medical University
The Fifth Hospital of Dalian

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Responsible Party: Maastricht University
ClinicalTrials.gov Identifier: NCT03872362     History of Changes
Other Study ID Numbers: UM2019DLABGY1
First Posted: March 13, 2019    Key Record Dates
Last Update Posted: March 13, 2019
Last Verified: January 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Maastricht University:
Lung cancer
CT
Frozen sections
Radiomics
Lung adenocarcinoma
Additional relevant MeSH terms:
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Carcinoma, Non-Small-Cell Lung
Lung Neoplasms
Multiple Pulmonary Nodules
Lung Diseases
Neoplasms
Respiratory Tract Diseases
Carcinoma, Bronchogenic
Bronchial Neoplasms
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site