Lung NeoplasmsRMBPNNCT03872362
Radiomics Multifactorial Biomarker for Pulmonary Nodules
An observational study of Lung Neoplasms and Carcinoma, Non-Small-Cell Lung, run by Maastricht University at 1 site in China.
Trial summary
The investigators aim to investigate the utility of radiomics to differentiate malignant nodules from benign nodules and invasive adenocarcinoma from non-invasive adenocarcinoma. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Neoplasms, Carcinoma, Non-Small-Cell Lung, Lung Diseases
- What is being tested?
- radiomics
- How many people
- 800 participants
- Where
- 1 siteChina
- Study dates
- 11 Jul 2018to 1 Feb 2019
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with pulmonary nodules in the collaborating institutes.
Who may be able to join
- intraoperative frozen section diagnosis and final pathology diagnosis are available
- preoperative standard non-enhanced CT is available
- Pathologically confirmed
Who cannot join
- with a previous history of radiation therapy, chemotherapy or biopsy
- the time interval between the CT examination and surgery was more than two weeks
Trial locations and contacts
1 site in 1 country.
China1 site
Affiliated Zhongshan Hospital of Dalian University
Dalian, Liaoning, 116000
About this study
Official title: 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.
Read the 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.
- Being tested
- radiomics
- Keywords
- Lung cancer, CT, Frozen sections, Radiomics, Lung adenocarcinoma
- Medical subject headings
- Lung Neoplasms, Carcinoma, Non-Small-Cell Lung, Lung Diseases, Neoplasms, Adenocarcinoma of Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
radiomicsDiagnostic test
The high-throughput extraction of large amounts of quantitative image features from medical images
| Study group | Role | What they receive |
|---|---|---|
| Training dataset | No interventions Receives: Diagnostic Test: radiomics | |
| External validation1 | No interventions Receives: Diagnostic Test: radiomics | |
| External validation2 | No interventions Receives: Diagnostic Test: radiomics |
What is being measured
The primary outcome is the main question the study is designed to answer.
Malignant nodules classifier
Model based on Radiomic that can differentiate malignant nodules from benign nodules.
Measured: 30 days
Invasive adenocarcinoma classifier
Model based on Radiomic that can differentiate invasive adenocarcinoma from non-invasive adenocarcinoma.
Measured: 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Mar 2019
- Study start11 Jul 2018
- Primary completion11 Jan 2019
- Study completion1 Feb 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Lung NeoplasmsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 800 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maastricht University
- Sponsor type
- University, hospital or other
- Collaborators
- Affiliated Zhongshan Hospital of Dalian University, The Second Affiliated Hospital of Dalian Medical University, The Fifth Hospital of Dalian
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03872362
- Sponsor's protocol ID
- UM2019DLABGY1
- Acronym
- RMBPN
- Status last verified
- Jan 2019
- First submitted
- 11 Mar 2019
- Last updated
- 13 Mar 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Wu G, Woodruff HC, Shen J, Refaee T, Sanduleanu S, Ibrahim A, Leijenaar RTH, Wang R, Xiong J, Bian J, Wu J, Lambin P. Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study. Radiology. 2020 Nov;297(2):451-458. doi: 10.1148/radiol.2020192431. Epub 2020 Aug 25.
PubMed 32840472
Study at a glance
From the registry record, last updated by the sponsor on 13 Mar 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Radiomics and Clinical Variables Can Differentiate Malignant Nodules and Detect Invasive Adenocarcinoma in Pulmonary Nodules: a Multi-center Study
- ClinicalTrials.gov ID
- NCT03872362
- Acronym / sponsor ID
- RMBPN / UM2019DLABGY1
- Keywords
- Lung cancer, CT, Frozen sections, Radiomics, Lung adenocarcinoma
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 800 participants (actual)
- Lead sponsor
- Maastricht UniversityUniversity, hospital or other
- Collaborators
- Affiliated Zhongshan Hospital of Dalian University, The Second Affiliated Hospital of Dalian Medical University, The Fifth Hospital of Dalian
- Study start
- 11 Jul 2018 (actual)
- Primary completion
- 11 Jan 2019 (actual)
- Study completion
- 1 Feb 2019 (actual)
- First posted
- 13 Mar 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The investigators aim to investigate the utility of radiomics to differentiate malignant nodules from benign nodules and invasive adenocarcinoma from non-invasive adenocarcinoma.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training dataset | – | Diagnostic Test: radiomicsNo interventions |
| External validation1 | – | Diagnostic Test: radiomicsNo interventions |
| External validation2 | – | Diagnostic Test: radiomicsNo interventions |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with pulmonary nodules in the collaborating institutes.
Key inclusion criteria (3 listed)
- intraoperative frozen section diagnosis and final pathology diagnosis are available
- preoperative standard non-enhanced CT is available
- Pathologically confirmed
Key exclusion criteria (2 listed)
- with a previous history of radiation therapy, chemotherapy or biopsy
- the time interval between the CT examination and surgery was more than two weeks
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| radiomicsThe high-throughput extraction of large amounts of quantitative image features from medical images | Diagnostic test | External validation1, External validation2, Training dataset |
Primary outcome
Malignant nodules classifier
Time frame: 30 days
Model based on Radiomic that can differentiate malignant nodules from benign nodules.
Invasive adenocarcinoma classifier
Time frame: 30 days
Model based on Radiomic that can differentiate invasive adenocarcinoma from non-invasive adenocarcinoma.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Mar 2019.
- Derived from this study: PubMed 32840472Wu G, Woodruff HC, Shen J, Refaee T, Sanduleanu S, Ibrahim A, Leijenaar RTH, Wang R, Xiong J, Bian J, Wu J, Lambin P. Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study. Radiology. 2020 Nov;297(2):451-458. doi: 10.1148/radiol.2020192431. Epub 2020 Aug 25.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Neoplasms and Carcinoma, Non-Small-Cell Lung, run by Maastricht University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 Mar 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Malignant nodules classifier (time frame: 30 days). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03872362, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Mar 2019.