Active not recruitingOBSERVATIONAL

Distinguishing Retroperitoneal Fibrosis and Sarcoma from Other Retroperitoneal Diseases Via Radiomics

This study is looking at ways to improve how doctors diagnose certain conditions found in the retroperitoneum, which is an area at the back of your tummy and pelvis. Specifically, it's focusing on retroperitoneal fibrosis (also known as Ormond's disease), sarcoma (a type of cancer), and other conditions like lymphoma or infections. Researchers are using a new technique called 'radiomics'. This involves a special computer program with artificial intelligence that can analyse past CT scans. It looks for hidden patterns and features in the scans that human eyes might not see. By combining this scan information with other details like symptoms and blood test results, the study hopes to create a tool that can help doctors tell these conditions apart more quickly and accurately, without needing invasive tests.

At a glance

Status

Active not recruiting

Sponsor

Heidelberg University

Enrolment target

600

Start

01 Nov 2023

Estimated completion

01 Dec 2025

Retroperitoneal Sarcoma Retroperitoneal Fibrosis

What is this study about?

This research project is trying to make it easier and quicker for doctors to identify different conditions that can appear in an area at the back of your belly, called the retroperitoneum. Sometimes, it can be tricky to tell the difference between conditions like retroperitoneal fibrosis (which is a build-up of scar tissue), a type of cancer called sarcoma, or other issues like infections or different kinds of tumours, just by looking at standard scans.

The study uses an advanced method called 'radiomics'. Think of this as giving a computer special 'eyes' and a 'brain' to look at medical scans. This computer program, powered by artificial intelligence, can analyse old CT scans of patients. It's designed to spot tiny details and patterns within these images that are not normally visible to the human eye. These patterns can then help to identify what kind of condition is present.

To make this even more accurate, the computer program will also use other information, such as your symptoms and results from blood tests. By putting all this data together, the goal is to develop a tool that could help doctors diagnose these conditions more reliably and faster in the future. This means fewer uncertainties and potentially quicker access to the right treatment, and it aims to do this non-invasively, meaning without needing surgery or other procedures to get a diagnosis.

Key takeaways

New computer program uses AI to analyse past CT scans.
Aims to tell the difference between retroperitoneal fibrosis, sarcoma, and other conditions.
Could lead to faster, easier diagnosis for future patients.
No new patient involvement, uses existing anonymised medical data.
Combines scan details with other patient information (symptoms, blood tests).

Who may be eligible?

This study is using information from past patients, so you wouldn't be actively involved if you signed up now. However, if you were one of the patients whose data is being used, you would have met certain criteria.

To be included, patients needed to have had a CT scan that clearly showed a mass or lump in the retroperitoneal area. They also needed to have had a confirmed diagnosis from a doctor, such as retroperitoneal fibrosis, sarcoma, or other conditions like lymphoma or different types of tumours.

Patients would not have been included if their CT scans were not clear enough to see the area properly, or if they had already had treatments or surgeries that might have changed the appearance of the mass on the scan, as this could confuse the computer program.

Could this study suit you?

Answer these quick questions to see if you may be eligible. This is a guide only — the research team makes the final call.

Did you have a CT scan showing a growth in your retroperitoneal area?
Was your condition officially diagnosed by a doctor?
Was your CT scan clear enough to see the growth properly?
Had you not received treatments or surgeries that might have changed the growth's appearance before your scan?

Answer every question to see your result.

What does participation involve?

Since this study is looking at information from past patients' records and scans, there is no direct participation required for new patients. You would not need to attend any appointments, make changes to your medication, or have any follow-up visits specifically for this research. There are no treatments given as part of this study.

Potential risks and benefits

There are no direct risks to patients as this study uses existing, anonymised medical records and scans; no new tests or treatments are involved. The potential benefit is that the findings could lead to a new, faster, and less invasive way for doctors to diagnose difficult conditions in the future, ultimately helping other patients. Patients whose data is used in such studies always have the right to have their information withdrawn, though as this is anonymised historical data, it typically doesn't affect individuals directly.

Locations (2)

Peking University International Hospital
Verified postcode
Beijing, China
Universitätsklinikum Mannheim
Verified postcode
Mannheim, Germany

Common questions

What is the retroperitoneum?

It's an area at the back of your belly and pelvis, behind your abdominal organs, where conditions like fibrosis or certain cancers can occur.

What is radiomics?

Radiomics is a special computer technique that uses artificial intelligence to find hidden patterns and details in medical scans that human eyes might not see.

Will this study affect my current treatment?

No, this study only looks at past patient data and scans. It doesn't involve any new treatments or changes to your current medical care.

Will my personal data be safe?

Yes, studies like this use anonymised data, meaning your personal details are removed so that information cannot be linked back to you.

What's the main goal of this research?

The main goal is to create a new computer tool that helps doctors tell the difference between difficult-to-diagnose retroperitoneal conditions more accurately and quickly.