Novel Applications for Sarcoma Assessment (NASA)

The Leeds Teaching Hospitals NHS Trust

Status: Enrolling

Conditions: Sarcoma

Treatments: Diagnostic Test: Quantitative MRI; Diagnostic Test: Reproducibility study

Study type: Observational

Funder types: Other

Identifiers

NCT06073314

MP23/150492

Details and patient eligibility

About

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.

Full 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.

Enrollment: 250 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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).

Trial design

250 participants in 2 patient groups

Original cohort

Description:

This cohort will have a maximum of 10 minutes of quantitative MRI sequences added on to the end of the clinical standard MRI scan

Treatment:

Diagnostic Test: Quantitative MRI

Reproducibility cohort

Description:

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.

Treatment:

Diagnostic Test: Reproducibility study

Diagnostic Test: Quantitative MRI