Novel Applications for Sarcoma Assessment

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.

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Key information

Conditions

Sarcoma Neoplasms Neoplasms by Histologic Type Neoplasms, Connective and Soft Tissue

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Leeds Teaching Hospitals

Leeds, United Kingdom

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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.

Exclusion criteria

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).

Treatment and study plan

Quantitative MRI

Diagnostic Test

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

Primary outcomes

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.

Secondary outcomes

Classification accuracy - ROC analysis of accuracy, sensitivity and specificity of AI algorithms for classifying malignant lesions into their pathological grade

Time frame: 3 years

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.

Sponsors and collaborators

Lead sponsor

The Leeds Teaching Hospitals NHS Trust

Other

Registry information

Acronym: NASA

Important dates

Study start: 2023
Primary completion: 2025
Study completion: 2026
First posted: Oct 10, 2023
Registry last updated: Apr 14, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed