The Value of a Convolutional Neural Network-Based Renal Artery Perfusion Model in Predicting Renal Function After Partial Nephrectomy: A Prospective Study

NCT06751498 | Recruiting DMC

• 1 other identifier: 2024-SR-710
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 2

Brief Summary

The goal of this observational study is to develop a CNN-based machine module to predict postoperative fractional renal function in people who are proposed to undergo partial nephrectomy. The main question it aims to answer is: • Does this machine learning model accurately predict renal function after partial nephrectomy?

Conditions

Renal Cell Cancer

Keywords

Articicial Intelligence; convolutional neural network; renal arterial perfusion model; partial nephrectomy; fractional renal function

Outcome Measures

Primary Outcomes (2)

• GFR of ipsilateral and contralateral kidneys

  3 months after surgery

• volume of ipsilateral kidney

  3 months after surgery

Secondary Outcomes (1)

• Postoperative total renal function(eGFR)

  24 hours, 1 month, 3 months, 6 months, 1 year after surgery

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

people with localized renal tumors scheduled for partial nephrectomy

You may qualify if:

• people with stage cT1 renal tumors confirmed by preoperative CT or MR
• people who are proposed to undergoing partial nephrectomy
• localized renal tumors without lymph node and distant metastases as defined by NCCN guidelines
• ECOG score of 0 or 1
• Life expectancy greater than 10 years

You may not qualify if:

• people with surgically unresectable lesions
• people with Abnormal preoperative renal function, eGFR(estimated by CKD-EPI)\<90ml/min/1.73m2
• people who receive preoperative molecular targeted therapy, immunotherapy, chemotherapy
• people with any contraindications to surgery
• people who convert to radical nephrectomy during surgery
• people who receive molecular targeted therapy, immunotherapy or chemotherapy during the postoperative follow-up period
• people with serious systemic disease

Sponsors & Collaborators

• Shao Pengfei: lead

Study Sites (2)

The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)

Nanjing, Jiangsu, 210000, China

RECRUITING

The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)

Nanjing, Jiangsu, 210036, China

NOT YET RECRUITING

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

Central Study Contacts

Shao Pengfei, Professor

CONTACT

+8613851925825; spf032@hotmail.com

Miao Haoqi, Postgraduate

CONTACT

+8613276636957; mhq@stu.njmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: chief physician

Study Record Dates

• First Submitted: December 25, 2024
• First Posted: December 30, 2024
• Study Start: January 1, 2025
• Primary Completion (Estimated): April 1, 2027
• Study Completion (Estimated): January 1, 2028
• Last Updated: April 17, 2025

Record last verified: 2025-04

Locations

CN (2)