Lung CancerLIBRANCT04270799
Lung Nodule Imaging Biobank for Radiomics and AI Research
An observational study of Lung Cancer and Pulmonary Nodule, Multiple, run by Royal Marsden NHS Foundation Trust at 5 sites in United Kingdom.
Trial summary
This study will collect retrospective CT scan images and clinical data from participants with incidental lung nodules seen in hospitals across London. The investigators will research whether machine learning can be used to predict which participants will develop lung cancer, to improve early diagnosis. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer, Pulmonary Nodule, Multiple, Pulmonary Nodule, Solitary
- What is being tested?
- Machine Learning Classification
- How many people
- 1,000 participants
- Where
- 5 sitesUnited Kingdom
- Study dates
- 1 Jun 2020to Aug 2021
- 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 previously identified lung nodules.
Who may be able to join
- Age > 18
- Baseline CT thorax imaging reported as having pulmonary nodule(s) between 5 and 30mm in the last 10 years.
- Ground truth known (either scan data showing stability for 2 years (based on diameter) or one year (based on volumetry), complete resolution, or biopsy-proven malignancy.
- Slice thickness < 2.5mm.
Who cannot join
- • Absence of at least one technically adequate CT thorax imaging series (defined by visual inspection of presence of imaging data of the thorax in the DICOM record).
- Slice thickness > 2.5mm.
- Imaging > 10 years old.
- Ground truth unknown.
Trial locations and contacts
5 sites in 1 country.
5 sites in 1 country
United Kingdom5 sites, 5 recruiting
- Not yet recruiting
Epsom and St Helier's Hospitals NHS Trust
Carshalton, Surrey, SM5 1AA
Jonathon Ratoff, Jonathan.ratoff@nhs.net
Jonathon Ratoff
- Recruiting
Lewisham and Greenwich NHS Trust
London, Greater London, SE6 4JH
Dr Shafick Gareeboo, shafick.gareeboo@nhs.net
Shafick Gareeboo
- Recruiting
University College London Hospitals NHS Foundation Trust
London, NW1 2PG
Dr Neal Navani, neal.navani@uclh.nhs.uk
Neal Navani
- Recruiting
The Royal Brompton NHS Foundation Trust
London, SW3 6NP
Anand Deveraj, 020 7352 8121, A.Devaraj@rbht.nhs.uk
Dr Anand Deveral
- Recruiting
Royal Marsden - Surrey
Sutton, England, SM2 5PT
Benjamin Hunter, benjamin.hunter@rmh.nhs.uk
Richard Lee
About this study
This study will collect retrospective CT scan images and clinical data from participants with incidental lung nodules seen in hospitals across London. The investigators will research whether machine learning can be used to predict which participants will develop lung cancer, to improve early diagnosis.
- Being tested
- Machine Learning Classification
- Keywords
- Incidental lung nodules, Radiomics, Artificial Intelligence, Machine Learning
- Medical subject headings
- Lung Neoplasms, Multiple Pulmonary Nodules, Solitary Pulmonary Nodule
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine Learning ClassificationDiagnostic test
Patient's scans will be used as input into in-house software to extract multiple radiomics features. These features will be used to develop a risk-signature which can predict malignancy risk. Patient scans will also be used as input into deep learning/convolutional neural network models to perform automated imaging classification.
| Study group | Role | What they receive |
|---|---|---|
| Lung Nodules | A cohort of 1000 patients with incidental lung nodules will be identified using clinical records at participating NHS sites.
Link-anonymised CT scan images and data will be stored using a central database for radiomics and artificial intelligence research, to predict the risk of malignancy. Receives: Diagnostic Test: Machine Learning Classification |
What is being measured
The primary outcome is the main question the study is designed to answer.
Development of an imaging biobank
The primary endpoint will be met if we are able to store baseline CT scans and the minimum clinical data set for 1000 patients.
Measured: 1 year
Secondary outcomes (1)
Discovery of a CT-thorax based radiomics profile to predict cancer risk.
We aim to identify distinct clusters of radiomics variables to generate a radiomics predictive vector (RPV), which can be used to stratify patients according to malignancy risk. This vector will be used in multivariate analysis and compared to existing risk models.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Feb 2020
- Study start1 Jun 2020
- Primary completionAug 2021 (estimated)
- Study completionAug 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Royal Marsden NHS Foundation TrustThe sponsor's other registered studies.
- 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
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Royal Marsden NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Collaborators
- RM Partners West London Cancer Alliance, Royal Brompton & Harefield NHS Foundation Trust, University College London Hospitals, Imperial College Healthcare NHS Trust, Lewisham and Greenwich NHS Trust, King's College Hospital NHS Trust, Epsom and St Helier University Hospitals NHS Trust, Institute of Cancer Research, United Kingdom, Guy's and St Thomas' NHS Foundation Trust
- Responsible party
- Sponsor
- Investigators
- Richard Lee, MBBS PhD, Study chair, The Royal Marsden Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04270799
- Sponsor's protocol ID
- CCR5215
- Acronym
- LIBRA
- Status last verified
- Jun 2021
- First submitted
- 12 Feb 2020
- Last updated
- 11 Jun 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 11 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Lung Nodule Imaging Biobank for Radiomics and AI Research
- ClinicalTrials.gov ID
- NCT04270799
- Acronym / sponsor ID
- LIBRA / CCR5215
- Keywords
- Incidental lung nodules, Radiomics, Artificial Intelligence, Machine Learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Royal Marsden NHS Foundation TrustUniversity, hospital or other
- Collaborators
- RM Partners West London Cancer Alliance, Royal Brompton & Harefield NHS Foundation Trust, University College London Hospitals, Imperial College Healthcare NHS Trust, Lewisham and Greenwich NHS Trust, King's College Hospital NHS Trust, Epsom and St Helier University Hospitals NHS Trust, Institute of Cancer Research, United Kingdom, Guy's and St Thomas' NHS Foundation Trust
- Study start
- 1 Jun 2020 (actual)
- Primary completion
- Aug 2021 (estimated)
- Study completion
- Aug 2021 (estimated)
- First posted
- 17 Feb 2020 (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.
This study will collect retrospective CT scan images and clinical data from participants with incidental lung nodules seen in hospitals across London. The investigators will research whether machine learning can be used to predict which participants will develop lung cancer, to improve early diagnosis.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Lung Nodules | – | Diagnostic Test: Machine Learning ClassificationA cohort of 1000 patients with incidental lung nodules will be identified using clinical records at participating NHS sites. Link-anonymised CT scan images and data will be stored using a central database for radiomics and artificial intelligence research, to predict the risk of malignancy. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with previously identified lung nodules.
Key inclusion criteria (4 listed)
- Age > 18
- Baseline CT thorax imaging reported as having pulmonary nodule(s) between 5 and 30mm in the last 10 years.
- Ground truth known (either scan data showing stability for 2 years (based on diameter) or one year (based on volumetry), complete resolution, or biopsy-proven malignancy.
- Slice thickness < 2.5mm.
Key exclusion criteria (4 listed)
- • Absence of at least one technically adequate CT thorax imaging series (defined by visual inspection of presence of imaging data of the thorax in the DICOM record).
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 |
|---|---|---|
| Machine Learning ClassificationPatient's scans will be used as input into in-house software to extract multiple radiomics features. These features will be used to develop a risk-signature which can predict malignancy risk. Patient scans will also be used as input into deep learning/convolutional neural network models to perform automated imaging classification. | Diagnostic test | Lung Nodules |
Primary outcome
Development of an imaging biobank
Time frame: 1 year
The primary endpoint will be met if we are able to store baseline CT scans and the minimum clinical data set for 1000 patients.
1 secondary outcome
Discovery of a CT-thorax based radiomics profile to predict cancer risk.
Time frame: 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jun 2021.
- 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.
Richard Lee, MBBS PhD study chair
The Royal Marsden Hospital
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Pulmonary Nodule, Multiple, run by Royal Marsden NHS Foundation Trust at 5 sites in United Kingdom.
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 4 inclusion and 4 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: status not confirmed, as of the sponsor's last update on 11 Jun 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in United Kingdom. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Richard Lee, MBBS PhD (020 7352 8171, richard.lee@rmh.nhs.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of an imaging biobank (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 11 Jun 2021.