Novel Applications for Sarcoma Assessment

NCT ID: NCT06073314

Last Updated: 2023-12-14

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

RECRUITING

Total Enrollment

250 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-08-09

Study Completion Date

2026-03-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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

See the medical conditions and disease areas that this research is targeting or investigating.

Sarcoma

Keywords

Explore important study keywords that can help with search, categorization, and topic discovery.

MRI Diffusion weighted imaging Radiomics Deep Learning Machine Learning Quantitative MRI

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

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

Quantitative MRI

Intervention Type DIAGNOSTIC_TEST

Patients will be asked to remain in the scanner for an additional 10 minutes while we acquire additional quantitative MR images

Reproducibility cohort

This group will be invited back for a second MRI scan to test reproducibility of quantitative scans and the machine learning algorithms developed to interpret them.

Quantitative MRI

Intervention Type DIAGNOSTIC_TEST

Patients will be asked to remain in the scanner for an additional 10 minutes while we acquire additional quantitative MR images

Reproducibility study

Intervention Type DIAGNOSTIC_TEST

A subset of patients will be invited back for a repeat MRI scan (prior to any treatment for their condition) to help measure reproducibility of our Artificial Intelligence model

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Quantitative MRI

Patients will be asked to remain in the scanner for an additional 10 minutes while we acquire additional quantitative MR images

Intervention Type DIAGNOSTIC_TEST

Reproducibility study

A subset of patients will be invited back for a repeat MRI scan (prior to any treatment for their condition) to help measure reproducibility of our Artificial Intelligence model

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

1. Patients with a soft tissue mass that are discussed at the sarcoma multi-disciplinary meeting
2. Undergoing MRI as part of their standard of care
3. Participant is willing and able to give informed consent for participation in the trial.

Exclusion Criteria

1. Patient has already had the mass, or part of the mass, surgically removed prior to their MRI scan
2. Contraindication to MRI (e.g. presence of contraindicated implants e.g. cardiac pacemakers, claustrophobia).
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

The Leeds Teaching Hospitals NHS Trust

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Leeds Teaching Hospitals

Leeds, , United Kingdom

Site Status RECRUITING

Countries

Review the countries where the study has at least one active or historical site.

United Kingdom

Central Contacts

Reach out to these primary contacts for questions about participation or study logistics.

Matthew Marzetti, MSc

Role: CONTACT

Phone: +44 1133 923055

Email: [email protected]

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

MP23/150492

Identifier Type: -

Identifier Source: org_study_id