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Preoperative Prediction of Trifecta Outcomes in Minimally Invasive Partial Nephrectomy Using a CT-Based Deep Learning Radiomics Model

Preoperative Prediction of Trifecta Outcomes in Minimally Invasive Partial Nephrectomy Using a CT-Based Deep Learning Radiomics Model

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500096859
Enrollment
Unknown
Registered
2025-02-07
Start date
2025-02-08
Completion date
Unknown
Last updated
2025-02-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Renal masses

Interventions

Observation group:None

Sponsors

The First Affiliated Hospital,Sun Yat-sen University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. CT examination revealed a single renal tumor; 2.The patient underwent RAPN or LPN treatment; 3.Preoperative creatinine level < 133 µmol/L, with no underlying kidney disease or renal insufficiency.

Exclusion criteria

Exclusion criteria: 1. Patients with incomplete clinical and imaging data; 2. Patients with duplicate kidneys, solitary kidneys, horseshoe kidneys; 3. Patients with poor-quality CT images.

Design outcomes

Primary

MeasureTime frame
Negative surgical margins;Reduction in eGFR in the early postoperative period by =3 grade complications;—

Countries

China

Contacts

Public ContactXu Chen

Department of Urology, The First Affiliated Hospital of Sun Yat-sen University

chenxu25@mail.sysu.edu.cn+86 15013175420

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026