Metastatic Bone TumorNCT05110430
Automated Detection of Metastatic Bone Disease on Bone Scintigraphy Scans
An observational study of Metastatic Bone Tumor, run by Maastricht University at 1 site in Netherlands.
Trial summary
Bone scintigraphy scans are two dimensional medical images that are used heavily in nuclear medicine. The scans detect changes in bone metabolism with high sensitivity, yet it lacks the specificity to underlying causes. Therefore, further imaging would be required to confirm the underlying cause. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Metastatic Bone Tumor
- What is being tested?
- Deep learning based detection of metastatic bone disease on bone scintigraphy scans.
- How many people
- 2,365 participants
- Where
- 1 siteNetherlands
- Study dates
- 10 Mar 2021to 31 Dec 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Any patient who had an indication for undergoing bone scintigraphy in any of the participating centers.
Who may be able to join
- Patients who underwent a bone scintigraphy scan that is available with the radiologic report between 2010-2018
Who cannot join
- The lack of a bone scan, or corresponding radiologic report
Trial locations and contacts
1 site in 1 country.
About this study
Official title: In Silico Clinical Trial Comparing the Reading Accuracy of Doctors and a Deep Learning Algorithm for Detection of Metastatic Bone Disease on Bone Scintigraphy Scans.
Bone scintigraphy scans are two dimensional medical images that are used heavily in nuclear medicine. The scans detect changes in bone metabolism with high sensitivity, yet it lacks the specificity to underlying causes. Therefore, further imaging would be required to confirm the underlying cause. The aim of this study is to investigate whether deep learning can improve clinical decision based on bone scintigraphy scans.
- Conditions
- Metastatic Bone Tumor
- Being tested
- Deep learning based detection of metastatic bone disease on bone scintigraphy scans.
- Medical subject headings
- Bone Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning based detection of metastatic bone disease on bone scintigraphy scans.Other
The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.
| Study group | Role | What they receive |
|---|---|---|
| BS-UKA | Patients who underwent bone scintigraphy scanning between 2010 and 2018 at RTWH Aachen university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. Receives: Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans. | |
| BS-Namur | Patients who underwent bone scintigraphy scanning between 2010 and 2018 at Namur university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. Receives: Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans. | |
| BS-Aalborg | Patients who underwent bone scintigraphy scanning between 2010 and 2018 at Aalborg university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. Receives: Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans. |
What is being measured
The primary outcome is the main question the study is designed to answer.
The classification performance of DL algorithm compared to the ground truth
Reporting the performance measures (Area under the curve, accuracy, specificity..etc)
Measured: June 2021
Secondary outcomes (1)
Comparing the classification performance of the DL algorithm to that of physicians
Correctness of the diagnosis of Dr versus AI (dichotomous variable: correct versus not correct) on a subset of the validation data, using a McNemar statistical test
Measured: June 2021
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Nov 2021
- Study start10 Mar 2021
- Primary completion30 Dec 2021
- Study completion31 Dec 2021
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 Metastatic Bone TumorStudies 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
- 2,365 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maastricht University
- Sponsor type
- University, hospital or other
- Collaborators
- Aalborg University Hospital, Centre Hospitalier Universitaire de Liege, University Hospital, Aachen, University of Namur
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05110430
- Sponsor's protocol ID
- MBDDL
- Status last verified
- Mar 2023
- First submitted
- 19 Apr 2021
- Last updated
- 20 Mar 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Ibrahim A, Vaidyanathan A, Primakov S, Belmans F, Bottari F, Refaee T, Lovinfosse P, Jadoul A, Derwael C, Hertel F, Woodruff HC, Zacho HD, Walsh S, Vos W, Occhipinti M, Hanin FX, Lambin P, Mottaghy FM, Hustinx R. Deep learning based identification of bone scintigraphies containing metastatic bone disease foci. Cancer Imaging. 2023 Jan 25;23(1):12. doi: 10.1186/s40644-023-00524-3.
PubMed 36698217
Study at a glance
From the registry record, last updated by the sponsor on 20 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- In Silico Clinical Trial Comparing the Reading Accuracy of Doctors and a Deep Learning Algorithm for Detection of Metastatic Bone Disease on Bone Scintigraphy Scans.
- ClinicalTrials.gov ID
- NCT05110430
- Acronym / sponsor ID
- MBDDL
- Conditions
- Metastatic Bone Tumor
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,365 participants (actual)
- Lead sponsor
- Maastricht UniversityUniversity, hospital or other
- Collaborators
- Aalborg University Hospital, Centre Hospitalier Universitaire de Liege, University Hospital, Aachen, University of Namur
- Study start
- 10 Mar 2021 (actual)
- Primary completion
- 30 Dec 2021 (actual)
- Study completion
- 31 Dec 2021 (actual)
- First posted
- 8 Nov 2021 (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.
Bone scintigraphy scans are two dimensional medical images that are used heavily in nuclear medicine. The scans detect changes in bone metabolism with high sensitivity, yet it lacks the specificity to underlying causes. Therefore, further imaging would be required to confirm the underlying cause. The aim of this study is to investigate whether deep learning can improve clinical decision based on bone scintigraphy scans.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| BS-UKA | – | Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.Patients who underwent bone scintigraphy scanning between 2010 and 2018 at RTWH Aachen university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. |
| BS-Namur | – | Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.Patients who underwent bone scintigraphy scanning between 2010 and 2018 at Namur university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. |
| BS-Aalborg | – | Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.Patients who underwent bone scintigraphy scanning between 2010 and 2018 at Aalborg university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Any patient who had an indication for undergoing bone scintigraphy in any of the participating centers.
Key inclusion criteria (1 listed)
- Patients who underwent a bone scintigraphy scan that is available with the radiologic report between 2010-2018
Key exclusion criteria (1 listed)
- The lack of a bone scan, or corresponding radiologic report
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 |
|---|---|---|
| Deep learning based detection of metastatic bone disease on bone scintigraphy scans.The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity. | Other | BS-Aalborg, BS-Namur, BS-UKA |
Primary outcome
The classification performance of DL algorithm compared to the ground truth
Time frame: June 2021
Reporting the performance measures (Area under the curve, accuracy, specificity..etc)
1 secondary outcome
Comparing the classification performance of the DL algorithm to that of physicians
Time frame: June 2021
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 20 Mar 2023.
- Results publication: PubMed 36698217Ibrahim A, Vaidyanathan A, Primakov S, Belmans F, Bottari F, Refaee T, Lovinfosse P, Jadoul A, Derwael C, Hertel F, Woodruff HC, Zacho HD, Walsh S, Vos W, Occhipinti M, Hanin FX, Lambin P, Mottaghy FM, Hustinx R. Deep learning based identification of bone scintigraphies containing metastatic bone disease foci. Cancer Imaging. 2023 Jan 25;23(1):12. doi: 10.1186/s40644-023-00524-3.
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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 Metastatic Bone Tumor, run by Maastricht University at 1 site in Netherlands.
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 1 inclusion and 1 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 20 Mar 2023. 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 Netherlands. 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?
The classification performance of DL algorithm compared to the ground truth (time frame: June 2021). 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 NCT05110430, 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 20 Mar 2023.