Clinical trial · Observational
AI for Renal Tumors Using Non-Contrast CT
An Artificial Intelligence Model for Screening and Diagnosis of Renal Tumors Based on Non-Contrast CT
NCT07304492CI-TRIAL-00099673not yet recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The goal of this observational study is to learn whether the artificial intelligence method can automatically identify and diagnose renal lesions using non-contrast CT or opportunistic screening.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Renal Cyst | — | UNRESOLVED | — |
| Renal Neoplasms | Kidney Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Building an intelligent diagnostic system for renal diseases based on CT scans.
- timeFrame
- 1 year
- description
- To construct an intelligent system for the detection of renal mass lesions and their differentiation into cysts, benign, and malignant neoplasms.
Secondary outcomes (1)
- measure
- Further develop artificial intelligence model to effectively diagnose pathological types of common renal tumors.
- timeFrame
- 1 year
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: 1. Patients who underwent an abdominal CT examination. 2. Patients with renal lesions were managed according to standard clinical pathways, which included follow-up, biopsy, or surgery. 3. Malignant lesions were pathologically confirmed; benign lesions were confirmed by either pathological diagnosis or imaging follow-up. 4. No prior treatment had been received for the renal disease. Exclusion Criteria: 1. Patients refuse to undergo recommended follow-up, biopsy, or surgery, which precluded definitive diagnosis of the renal lesion. 2. Absence of complete pathological confirmation for lesions suspected to be malignant. 3. Patients have received any form of prior treatment for the renal lesion. 4. Poor image quality that hampered diagnostic evaluation.
References
Publications (0)
Data not yet available
No reference posted for this study.