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

NCT ID: NCT06876584

Last Updated: 2025-03-14

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 1474 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-06-01
• Study Completion Date: 2025-02-28

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.

Conditions

Renal Cell Carcinoma (RCC); Renal Cyst

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Complication 1

Patients who experienced perioperative complications during the partial nephrectomy

No interventions assigned to this group

Complication 0

Patients who didn't experience perioperative complications during the partial nephrectomy

No interventions assigned to this group

Eligibility Criteria

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

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Shanghai Zhongshan Hospital

OTHER

Sponsor Role: collaborator

Minhang Hospital, Fudan University

UNKNOWN

Sponsor Role: collaborator

Xuhui Central Hospital, Shanghai

OTHER

Sponsor Role: collaborator

Du Lingzhi

OTHER

Sponsor Role: lead

Responsible Party

Du Lingzhi

Principal Investigator

Responsibility Role: SPONSOR_INVESTIGATOR

Locations

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

Shanghai, Xuhui District, China

Countries

China

Other Identifiers

zsurologyDLMforPNcomplication

Identifier Type: -

Identifier Source: org_study_id