Development of AI Model for Renal Tumor Diagnosis Using CT and Lab Tests

An Artificial Intelligence Model for Accurate Diagnosis of Renal Tumors Based on Multi-phase Contrast-enhanced CT and Laboratory Tests: A Model Development and Multi-center Evaluation Study

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06761742

Enrollment

1922

Registered

2025-01-07

Start date

2024-01-01

Completion date

2024-12-01

Last updated

2025-01-07

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

Conditions

Renal Tumors

Brief summary

This multi-center retrospective study aims to develop a multimodal artificial intelligence diagnostic model using preoperative contrast-enhanced CT images and routine laboratory parameters from patients with renal tumors. The model is designed to assist clinicians in accurately predicting the pathological subtypes of renal tumors preoperatively, enabling detailed diagnoses and advancing precision medicine.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

Inclusion criteria

* Underwent renal tumor resection with a complete postoperative pathological report, and the pathological diagnosis is one of the following types: clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, renal angiomyolipoma, or renal oncocytoma. * Complete and available four-phase contrast-enhanced CT scans prior to surgery. * Complete and available routine laboratory test results prior to surgery.

Exclusion criteria

* Incomplete CT data or poor image quality that affects diagnostic analysis. * A time interval of more than three months between imaging or laboratory testing and pathological diagnosis. * Patients diagnosed with fat-rich renal angiomyolipoma (AML). * Pathological diagnosis indicating the coexistence of two or more pathological types of renal tumors.

Design outcomes

Primary

Measure	Time frame
postoperative pathological report	From the time of surgery to the release of the postoperative pathological report (typically within 2 weeks post-surgery).

Countries

China

Outcome results