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Distinguishing Retroperitoneal Fibrosis and Sarcoma from Other Retroperitoneal Diseases Via Radiomics

NCT ID: NCT06741423

Last Updated: 2024-12-19

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ACTIVE_NOT_RECRUITING

Total Enrollment

600 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-11-01

Study Completion Date

2025-12-31

Brief Summary

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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

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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.

Conditions

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Retroperitoneal Sarcoma Retroperitoneal Fibrosis

Keywords

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radiomics auto-segmentation

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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retroperitoneal fibrosis

All recruited patients with retroperitoneal fibrosis

Radiomics Algortihm

Intervention Type OTHER

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

Radiomics Algortihm

Intervention Type OTHER

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

Radiomics Algortihm

Intervention Type OTHER

A radiomics algorithm designed to distinguish retroperitoneal fibrosis from other retroperitoneal tumors and provide recommendations for clinical treatment decisions.

Interventions

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Radiomics Algortihm

A radiomics algorithm designed to distinguish retroperitoneal fibrosis from other retroperitoneal tumors and provide recommendations for clinical treatment decisions.

Intervention Type OTHER

Eligibility Criteria

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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.
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Heidelberg University

OTHER

Sponsor Role lead

Responsible Party

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Cui Yang

Private Lecturer Dr. med.

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Peking University International Hospital

Beijing, , China

Site Status

Universitätsklinikum Mannheim

Mannheim, Baden-Wurttemberg, Germany

Site Status

Countries

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China Germany

Other Identifiers

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2023-890-AF 11

Identifier Type: -

Identifier Source: org_study_id