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

NCT06876584 | Completed DMC

• 1 other identifier: zsurologyDLMforPNcomplication
• Study Type: observational
• Target: 1,474
• Locations: 1 country
• Sites: 1

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.

Conditions

Renal Cell Carcinoma (RCC); Renal Cyst

Keywords

CT-based deep learning; complication prediction; traditional classification model; partial nephrectomy

Outcome Measures

Primary Outcomes (1)

• whether complications occurred

  Retrospectively review the medical record system to determine whether patients developed postoperative complications.

  perioperatively

Secondary Outcomes (1)

• Patients' risk grade

  perioperatively

Study Arms (2)

Complication 1

Patients who experienced perioperative complications during the partial nephrectomy

Complication 0

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

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population includes patients diagnosed with renal cell carcinoma or renal cyst who underwent partial nephrectomy in the participated centers. Clinical and imaging data were retrospectively collected from medical records, including demographic characteristics (age, gender, BMI), tumor location (left or right kidney), surgical details (surgical approach, ischemia time), and perioperative complications. Patients were included based on the availability of complete clinical, surgical, and imaging data. Exclusion criteria comprised individuals with missing or unavailable imaging data, or no available enhanced CT images. The study aims to combine CT-based radiomics features and clinical features to develop a deep learning model to predict perioperative complications of partial nephrectomy, and compare with traditional anatomical classification models.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Du Lingzhi: lead
• Shanghai Zhongshan Hospital: collaborator
• Minhang Hospital, Fudan University: collaborator
• Xuhui Central Hospital, Shanghai: collaborator

Study Sites (1)

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

Shanghai, Xuhui District, 200032, China

Location

MeSH Terms

Conditions

Carcinoma, Renal Cell

Condition Hierarchy (Ancestors)

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

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 8, 2025
• First Posted: March 14, 2025
• Study Start: June 1, 2024
• Primary Completion: December 31, 2024
• Study Completion: February 28, 2025
• Last Updated: March 14, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Within six months after publication in the journal.
• Access Criteria: The data supporting this study are available from the enrolled institutions, but restrictions apply to their availability due to privacy reasons. Data can be accessed upon reasonable request from the corresponding author.

Locations

CN (1)