SarcomaNASANCT06073314
Novel Applications for Sarcoma Assessment
An observational study of Sarcoma, run by The Leeds Teaching Hospitals NHS Trust at 1 site in United Kingdom.
Trial summary
This research aims to improve the way of deciding whether a lump in soft tissue such as fat or muscle is a type of cancer called a soft tissue sarcoma, or if it is benign (non-cancerous). To do this the investigators will use routine clinical MRI scans, additional quantitative MRI scans and artificial intelligence. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Sarcoma
- What is being tested?
- Quantitative MRI
- How many people
- 186 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 9 Aug 2023to 31 Mar 2026
- 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 who have been referred for MRI for a soft tissue mass that may be a soft tissue sarcoma, and have not yet undergone treatment for the lesion.
Who may be able to join
- Patients with a soft tissue mass that are discussed at the sarcoma multi-disciplinary meeting
- Undergoing MRI as part of their standard of care
- Participant is willing and able to give informed consent for participation in the trial.
Who cannot join
- Patient has already had the mass, or part of the mass, surgically removed prior to their MRI scan
- Contraindication to MRI (e.g. presence of contraindicated implants e.g. cardiac pacemakers, claustrophobia).
Trial locations and contacts
1 site in 1 country.
About this study
This research aims to improve the way of deciding whether a lump in soft tissue such as fat or muscle is a type of cancer called a soft tissue sarcoma, or if it is benign (non-cancerous). To do this the investigators will use routine clinical MRI scans, additional quantitative MRI scans and artificial intelligence.
The aims of this research are:
Read more
To develop AI algorithms that can accurately classify soft tissue masses as benign or malignant using routine and quantitative MR images.
To classify malignant soft tissue masses into their pathological grade. Compare different AI models on external, unseen testing sets to determine which offers the best performance.
Participants will be asked if they can spend up to a maximum of 10 extra minutes in an MRI scanner so that the extra images can be acquired. A small subset of participants will be invited back so the investigators can check the reproducibility of the images and the AI software.
Read the detailed description
This research's aim is to improve the way of deciding whether a lump in soft tissue such as fat or muscle is a type of cancer called a soft tissue sarcoma, or if it is benign using artificial intelligence (AI).
Soft tissue sarcomas are a type of cancer that can appear anywhere in the body where there is soft tissue such as muscle or fat. While sarcomas are rare, benign lumps in soft tissue are common and it is currently very difficult to tell the difference between the two using imaging. This means many patients with benign masses are referred for painful biopsies and waiting lists for biopsies are long due to the large diagnostic workload.
This research aims to develop an AI algorithm that can differentiate between benign and malignant soft tissue masses. While an algorithm can be developed using existing routine data the researchers would like to investigate if adding quantitative MR images could make it more accurate.
Patients who are already having a scan for sarcoma will be asked if they consent to extra MR images being acquired. These images will be used to provide extra information to the AI. The extra images will add a maximum of 10 minutes to the patients' standard MRI scan, meaning patients will not need to make an extra trip or undergo any extra procedures. Study participants will not need to receive MR contrast as part of this research. The extra images will not be used to make a diagnosis during this research. A small subset of patients will be asked if they would be willing to come for a second scan so that the researchers can see how reliable the measurements are, but this will be entirely optional.
- Conditions
- Sarcoma
- Being tested
- Quantitative MRI
- Keywords
- MRI, Diffusion weighted imaging, Radiomics, Deep Learning, Machine Learning, Quantitative MRI
- Medical subject headings
- Sarcoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Quantitative MRIDiagnostic test
Patients will be asked to remain in the scanner for an additional 10 minutes while we acquire additional quantitative MR images
| Study group | Role | What they receive |
|---|---|---|
| Original cohort | This cohort will have a maximum of 10 minutes of quantitative MRI sequences added on to the end of the clinical standard MRI scan Receives: Diagnostic Test: Quantitative MRI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy - ROC analysis of accuracy, sensitivity and specificity of AI algorithms for distinguishing between benign and malignant soft tissue lesions
AI algorithms will be trained to distinguish between benign and malignant soft tissue lesions. To assess the accuracy of these algorithms, sensitivity and specificity of the algorithm will be calculated using the patients diagnosis from biopsy/surgical resection as the gold standard.
Measured: 3 years
Secondary outcomes (1)
Classification accuracy - ROC analysis of accuracy, sensitivity and specificity of AI algorithms for classifying malignant lesions into their pathological grade
AI algorithms will be trained to distinguish between grade 1,2 and 3 malignant soft tissue lesions. To assess the accuracy of these algorithms, sensitivity and specificity of the algorithm will be calculated using the patients diagnosis from biopsy/surgical resection as the gold standard.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Oct 2023
- Study start9 Aug 2023
- Primary completion30 Oct 2025
- Study completion31 Mar 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SarcomaStudies 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
- Other
- Time perspective
- Prospective
- Enrolment
- 186 participants (actual)
Sponsor and investigators
- Lead sponsor
- The Leeds Teaching Hospitals NHS Trust
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06073314
- Sponsor's protocol ID
- MP23/150492
- Acronym
- NASA
- Status last verified
- Apr 2023
- First submitted
- 12 Sep 2023
- Last updated
- 14 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: All data will be de-identified prior to being used in this research
Study at a glance
From the registry record, last updated by the sponsor on 14 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Novel Applications for Sarcoma Assessment
- ClinicalTrials.gov ID
- NCT06073314
- Acronym / sponsor ID
- NASA / MP23/150492
- Conditions
- Sarcoma
- Keywords
- MRI, Diffusion weighted imaging, Radiomics, Deep Learning, Machine Learning, Quantitative MRI
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 186 participants (actual)
- Lead sponsor
- The Leeds Teaching Hospitals NHS TrustUniversity, hospital or other
- Study start
- 9 Aug 2023 (actual)
- Primary completion
- 30 Oct 2025 (actual)
- Study completion
- 31 Mar 2026 (actual)
- First posted
- 10 Oct 2023 (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 research aims to improve the way of deciding whether a lump in soft tissue such as fat or muscle is a type of cancer called a soft tissue sarcoma, or if it is benign (non-cancerous). To do this the investigators will use routine clinical MRI scans, additional quantitative MRI scans and artificial intelligence.
The aims of this research are:
To develop AI algorithms that can accurately classify soft tissue masses as benign or malignant using routine and quantitative MR images.
To classify malignant soft tissue masses into their pathological grade. Compare different AI models on external, unseen testing sets to determine which offers the best performance.
Participants will be asked if they can spend up to a maximum of 10 extra minutes in an MRI scanner so that the extra images can be acquired. A small subset of participants will be invited back so the investigators can check the reproducibility of the images and the AI software.
Detailed description
This research's aim is to improve the way of deciding whether a lump in soft tissue such as fat or muscle is a type of cancer called a soft tissue sarcoma, or if it is benign using artificial intelligence (AI).
Soft tissue sarcomas are a type of cancer that can appear anywhere in the body where there is soft tissue such as muscle or fat. While sarcomas are rare, benign lumps in soft tissue are common and it is currently very difficult to tell the difference between the two using imaging. This means many patients with benign masses are referred for painful biopsies and waiting lists for biopsies are long due to the large diagnostic workload.
This research aims to develop an AI algorithm that can differentiate between benign and malignant soft tissue masses. While an algorithm can be developed using existing routine data the researchers would like to investigate if adding quantitative MR images could make it more accurate.
Patients who are already having a scan for sarcoma will be asked if they consent to extra MR images being acquired. These images will be used to provide extra information to the AI. The extra images will add a maximum of 10 minutes to the patients' standard MRI scan, meaning patients will not need to make an extra trip or undergo any extra procedures. Study participants will not need to receive MR contrast as part of this research. The extra images will not be used to make a diagnosis during this research. A small subset of patients will be asked if they would be willing to come for a second scan so that the researchers can see how reliable the measurements are, but this will be entirely optional.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Original cohort | – | Diagnostic Test: Quantitative MRIThis cohort will have a maximum of 10 minutes of quantitative MRI sequences added on to the end of the clinical standard MRI scan |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who have been referred for MRI for a soft tissue mass that may be a soft tissue sarcoma, and have not yet undergone treatment for the lesion.
Key inclusion criteria (3 listed)
- Patients with a soft tissue mass that are discussed at the sarcoma multi-disciplinary meeting
- Undergoing MRI as part of their standard of care
- Participant is willing and able to give informed consent for participation in the trial.
Key exclusion criteria (2 listed)
- Patient has already had the mass, or part of the mass, surgically removed prior to their MRI scan
- Contraindication to MRI (e.g. presence of contraindicated implants e.g. cardiac pacemakers, claustrophobia).
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 |
|---|---|---|
| Quantitative MRIPatients will be asked to remain in the scanner for an additional 10 minutes while we acquire additional quantitative MR images | Diagnostic test | Original cohort |
Primary outcome
Diagnostic accuracy - ROC analysis of accuracy, sensitivity and specificity of AI algorithms for distinguishing between benign and malignant soft tissue lesions
Time frame: 3 years
AI algorithms will be trained to distinguish between benign and malignant soft tissue lesions. To assess the accuracy of these algorithms, sensitivity and specificity of the algorithm will be calculated using the patients diagnosis from biopsy/surgical resection as the gold standard.
1 secondary outcome
Classification accuracy - ROC analysis of accuracy, sensitivity and specificity of AI algorithms for classifying malignant lesions into their pathological grade
Time frame: 3 years
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 14 Apr 2026.
- Individual participant dataSharing planned: No. All data will be de-identified prior to being used in this research
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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 Sarcoma, run by The Leeds Teaching Hospitals NHS Trust at 1 site 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 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 14 Apr 2026. 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 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?
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?
Diagnostic accuracy - ROC analysis of accuracy, sensitivity and specificity of AI algorithms for distinguishing between benign and malignant soft tissue lesions (time frame: 3 years). 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 14 Apr 2026.