Distinguishing Retroperitoneal Fibrosis and Sarcoma from Other Retroperitoneal Diseases Via Radiomics

Active, not recruiting

• Conditions: Retroperitoneal Sarcoma; Retroperitoneal Fibrosis

• Registration Number: NCT06741423
• Lead Sponsor: Heidelberg University

Brief Summary

A retrospective study utilizing archived CT scans of patients diagnosed with retroperitoneal fibrosis, sarcoma or other malignancies (i.e. lymphoma, germ cell tumors, metastasis, infections, ganglioneuromas) in order to implement a radiomics algorithm which is able to differentiate between these malignancies.

Detailed Description

The aim of this project is to develop a radiomics algorithm that can reliably identify retroperitoneal fibrosis (Ormond's disease) and retroperitoneal sarcomas, automatically segment them and differentiate them from other retroperitoneal diseases. Radiomics is a technique that uses artificial intelligence to extract characteristics from radiological image data that are not visible to humans and to identify image morphological patterns of diseases. As it is difficult to differentiate between diseases using image data alone, clinical data such as symptoms and laboratory values are to be correlated with the image data and utilized by the algorithm. Among other things, this should increase the sensitivity, accuracy and specificity of image-based diagnostics in order to enable faster, non-invasive diagnosis.

Study Timeline

• Study Start: 2023-11-01
• Study First Submitted: 2024-06-24
• Status Verified: 2024-12
• Study First Submitted QC: 2024-12-14
• Last Update Submitted: 2024-12-14
• Study First Posted: 2024-12-19
• Last Update Posted: 2024-12-19
• Primary Completion: 2025-05
• Study Completion: 2025-12

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 600

Inclusion Criteria

• Patients of any age or gender.
• CT scans confirming the presence of a retroperitoneal mass.
• Confirmed diagnosis of retroperitoneal fibrosis, sarcoma or other malignancies (i.e. lymphoma, germ cell tumors, metastasis, infections, ganglioneuromas) through pathology reports or clinical follow-up.

Exclusion Criteria

• Poor quality CT scans where the region of interest is not clearly visible.
• Previous treatments or surgeries that might alter the radiomic features of the tumors.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Radiomic accuracy for retroperitoneal fibrosis	6 months	Accuracy of the algorithm in differentiating between retroperitoneal fibrosis and other retroperitoneal diseases

Secondary Outcome Measures

Name	Time	Method
Radiomic accuracy for retroperitoneal sarcomas	10 Months	Accuracy of the algorithm in differentiating between retroperitoneal sarcoma and other retroperitoneal diseases using CT images

Trial Locations

Locations (2)

Peking University International Hospital; 🇨🇳 Beijing, China

Universitätsklinikum Mannheim; 🇩🇪 Mannheim, Baden Württemberg, Germany