Distinguishing Retroperitoneal Fibrosis and Sarcoma from Other Retroperitoneal Diseases on CT Scans Via an Extended-Radiomics Approach: a Multi-Centric, International Retrospective Analysis.
Overview
- Phase
- Not Applicable
- Status
- Active, not recruiting
- Sponsor
- Heidelberg University
- Enrollment
- 600
- Locations
- 2
- Primary Endpoint
- Radiomic accuracy for retroperitoneal fibrosis
Overview
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 Design
- Study Type
- Observational
- Observational Model
- Other
- Time Perspective
- Retrospective
Eligibility Criteria
- Sex
- All
- Accepts Healthy Volunteers
- No
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.
Outcomes
Primary Outcomes
Radiomic accuracy for retroperitoneal fibrosis
Time Frame: 6 months
Accuracy of the algorithm in differentiating between retroperitoneal fibrosis and other retroperitoneal diseases
Secondary Outcomes
- Radiomic accuracy for retroperitoneal sarcomas(10 Months)
Investigators
Cui Yang
Private Lecturer Dr. med.
Heidelberg University