Health Condition 1: C649- Malignant neoplasm of unspecifiedkidney, except renal pelvis
Conditions
Interventions
Sponsors
Eligibility
Inclusion criteria
Inclusion criteria: For Case Group: Archived CECT abdominal scans with radiologically confirmed renal tumors. Availability of corresponding histopathological reports confirming renal tumors. Lesions measuring greater than equal to 5 mm in diameter on axial CT images. For Control Group: Archived CECT abdominal scans showing no evidence of renal tumors or pathology. CT images of good quality with normal renal tissue available for radiomic analysis.
Exclusion criteria
Exclusion criteria: Common for Both Groups: CT images with significant motion artifacts. History of renal surgery. History of chemotherapy or radiation therapy for renal conditions.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Diagnostic performance of the RenalXNet model for detection and differential diagnosis of renal tumors using CT radiomic features measured by accuracy sensitivity specificity and area under the ROC curve with histopathology as the reference standardTimepoint: At the time of CT image acquisition and analysis and at the time of availability of histopathology results | — |
Secondary
| Measure | Time frame |
|---|---|
| Identification of significant CT radiomic features associated with benign and malignant renal tumorsTimepoint: During retrospective phase data analysis;Comparative performance of RenalXNet with traditional machine learning models for renal tumor classificationTimepoint: After model development and testing on retrospective dataset ;Prospective validation performance of RenalXNet for detection and differential diagnosis of renal tumorsTimepoint: At completion of prospective validation phase;Subgroup analysis performance of RenalXNet across renal tumor subtypesTimepoint: During final data analysis | — |
Countries
India
Contacts
Manipal College of Health Professions