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

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.

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Key information

Conditions

Renal Tumors Adenocarcinoma Carcinoma Carcinoma, Renal Cell Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Kidney Diseases Kidney Neoplasms Male Urogenital Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

Shanghai Jiaotong University School of Medicine, Renji Hospital

Shanghai, Shanghai Municipality, 200120, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

postoperative pathological report

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

Sponsors and collaborators

Lead sponsor

RenJi Hospital

Other

Collaborators

Fudan University
Fudan University Pudong Medical Center
Shanghai Jiao Tong University Affiliated Sixth People's Hospital
The Affiliated Hospital Of Southwest Medical University

Registry information

Official study title

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

Important dates

Study start: 2024
Primary completion: 2024
Study completion: 2024
First posted: Jan 7, 2025
Registry last updated: Jan 7, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed