The CT-based Deep Learning Model Predicts Complications in Partial Nephrectomy

Completed

• Conditions: Renal Cell Carcinoma (RCC); Renal Cyst

• Registration Number: NCT06876584
• Lead Sponsor: Du Lingzhi

Brief Summary

The investigators combine radiomics and deep learning to analyze the lesions more thoroughly, aiming for a more accurate prediction of complications in partial nephrectomy, and compare this approach with traditional models.

Detailed Description

In this study, patients diagnosed with renal cell carcinoma or renal cyst who underwent partial nephrectomy across multiple centers was included. And the participants were excluded if they had (a) missing or unavailable imaging data or (b) no available enhanced CT images. The cohort was divided into training and test sets at a 7:3 ratio. After that, the radiomics features were extracted from the images, and lasso regression was used to select features. Then a deep learning model was developed to predict complications and risk grades and compared with traditional classification models (RENAL and PADUA), demonstrating superior applicability.

Study Timeline

• Study Start: 2024-06-01
• Primary Completion: 2024-12-31
• Study Completion: 2025-02-28
• Status Verified: 2025-03
• Study First Submitted: 2025-03-08
• Study First Submitted QC: 2025-03-12
• Last Update Submitted: 2025-03-12
• Study First Posted: 2025-03-14
• Last Update Posted: 2025-03-14

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1474

Inclusion Criteria

• Clinical diagnosis of renal cell carcinoma or renal cyst
• Underwent partial nephrectomy between June 2014 and July 2024

Exclusion Criteria

• Missing or unavailable imaging data
• No available enhanced CT images

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
whether complications occurred	perioperatively	Retrospectively review the medical record system to determine whether patients developed postoperative complications.

Secondary Outcome Measures

Name	Time	Method
Patients' risk grade	perioperatively	Based on the widely recognized Clavien-Dindo classification (CDC) system for surgical complications, these complications were categorized into four grades: Grade I, II, III, and IV. Risk grade was assigned accordingly: "no risk" is defined as no complications occurred, "grade low" is defined as the highest level of complication being Grade I, "grade moderate" is defined as the highest level of complication being Grade II, and "grade high" is defined as complications of Grade III or higher, which are life-threatening.

Trial Locations

Locations (1)

Name: Zhongshan Hospital Fudan University, Location: 180th Fenglin Road, Xuhui District, Shanghai, China; 🇨🇳 Shanghai, Xuhui District, China