The Value of a Convolutional Neural Network-Based Renal Artery Perfusion Model in Predicting Renal Function After Partial Nephrectomy: A Prospective Study
NCT ID: NCT06751498
Last Updated: 2025-04-17
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
300 participants
OBSERVATIONAL
2025-01-01
2028-01-01
Brief Summary
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• Does this machine learning model accurately predict renal function after partial nephrectomy?
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
* 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
Exclusion Criteria
* 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
18 Years
80 Years
ALL
No
Sponsors
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Shao Pengfei
OTHER
Responsible Party
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Shao Pengfei
chief physician
Locations
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The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Nanjing, Jiangsu, China
The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Nanjing, Jiangsu, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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2024-SR-710
Identifier Type: -
Identifier Source: org_study_id
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