renal tumor C64
Conditions
Interventions
Group 1: urine samples acquired during clinical routine will be analysed for protein expression (Vim-3, Mxi2). These information will be combined with radiological data (CT-images) and clinical inform
Sponsors
Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Inclusion criteria
Inclusion criteria: patients age > 18yo with radiologically or urological suspected renal tumor receiving CT imaging; or those undergoing systemic therapy for renal tumors
Exclusion criteria
Exclusion criteria: patients unable for comprehend the consent for study participation
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| 1) for non-metastatic RCC: development of a deep learning algorithm combining CT-imaging-data, clinical information, and urine biomarker expression to predict renal tumor histological subtype and malignancy. 2) for metastatic RCC (mRCC): development of a deep learning algorithm combining CT-imaging-data, clinical information, and urine biomarker expression to predict renal tumor histological subtype, molecular expression patterns and response to systemic therapy. | — |
Secondary
| Measure | Time frame |
|---|---|
| inter- / intrareader reliability of radiologists; progression-free, cancer-free and overall survival | — |
Countries
Germany
Contacts
Public ContactJohannes Uhlig
Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen
Outcome results
None listed