Clinical trial NCT06073314: Novel Applications for Sarcoma Assessment
Official title: Novel Applications for Sarcoma Assessment
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Sarcoma
Keywords: MRI
Diffusion weighted imaging
Radiomics
Deep Learning
Machine Learning
Quantitative MRI
Lead sponsor: The Leeds Teaching Hospitals NHS Trust
Sponsor class: OTHER
First posted: 2023-10-10
Enrollment: 250
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
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.

Brief 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.

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.

Eligibility criteria: 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).

Primary outcomes:
measure: Diagnostic accuracy - ROC analysis of accuracy, sensitivity and specificity of AI algorithms for distinguishing between benign and malignant soft tissue lesions
timeFrame: 3 years
Secondary outcomes:
measure: Classification accuracy - ROC analysis of accuracy, sensitivity and specificity of AI algorithms for classifying malignant lesions into their pathological grade
timeFrame: 3 years
Other outcomes:
Locations:
city: Leeds
country: United Kingdom
facility: Leeds Teaching Hospitals
Study officials:
References: