Distinguishing Retroperitoneal Fibrosis and Sarcoma from Other Retroperitoneal Diseases Via Radiomics

Distinguishing Retroperitoneal Fibrosis and Sarcoma from Other Retroperitoneal Diseases on CT Scans Via an Extended-Radiomics Approach: a Multi-Centric, International Retrospective Analysis.

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Retroperitoneal Sarcoma; Retroperitoneal Fibrosis
• Intervention / Treatment: Other: Radiomics Algortihm

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 Type: Observational
• Enrollment (Estimated): 600

Contacts and Locations

Study Locations

• China
  • Beijing, China
    • Peking University International Hospital
• Germany
  • Baden Württemberg
    • Mannheim, Baden Württemberg, Germany, 68167
      • Universitätsklinikum Mannheim

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

All patients with retroperitoneal fibrosis, sarcoma or other retroperitoneal diseases who have pre-op abdominal ct scans of good quality.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
retroperitoneal fibrosis; All recruited patients with retroperitoneal fibrosis	Other: Radiomics Algortihm; A radiomics algorithm designed to distinguish retroperitoneal fibrosis from other retroperitoneal tumors and provide recommendations for clinical treatment decisions.
retroperitoneal sarcoma; All recruited patients with retroperitoneal sarcoma	Other: Radiomics Algortihm; A radiomics algorithm designed to distinguish retroperitoneal fibrosis from other retroperitoneal tumors and provide recommendations for clinical treatment decisions.
other retroperitoneal diseases; All recruited patients with other retroperitoneal malignancies	Other: Radiomics Algortihm; A radiomics algorithm designed to distinguish retroperitoneal fibrosis from other retroperitoneal tumors and provide recommendations for clinical treatment decisions.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Radiomic accuracy for retroperitoneal fibrosis	Accuracy of the algorithm in differentiating between retroperitoneal fibrosis and other retroperitoneal diseases	6 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Radiomic accuracy for retroperitoneal sarcomas	Accuracy of the algorithm in differentiating between retroperitoneal sarcoma and other retroperitoneal diseases using CT images	10 Months

Collaborators and Investigators

• Sponsor: Heidelberg University

Study record dates

Study Major Dates

• Study Start (Actual): November 1, 2023
• Primary Completion (Estimated): May 1, 2025
• Study Completion (Estimated): December 1, 2025

Study Registration Dates

• First Submitted: June 24, 2024
• First Submitted That Met QC Criteria: December 14, 2024
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: December 14, 2024
• Last Verified: December 1, 2024

More Information

Terms related to this study

• Keywords: radiomics; auto-segmentation
• Additional Relevant MeSH Terms: Pathologic Processes; Neoplasms; Neoplasms by Histologic Type; Neoplasms, Connective and Soft Tissue; Fibrosis; Sarcoma; Retroperitoneal Fibrosis
• Other Study ID Numbers: 2023-890-AF 11

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No