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

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.

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Key information

Conditions

Renal Cell Carcinoma (RCC) Adenocarcinoma Carcinoma Carcinoma, Renal Cell Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Kidney Diseases Kidney Neoplasms Male Urogenital Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Renal Cyst Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

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

Shanghai, Xuhui District, 200032, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

whether complications occurred

Time frame: perioperatively

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

Secondary outcomes

Patients' risk grade

Time frame: 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.

Sponsors and collaborators

Lead sponsor

Du Lingzhi

Other

Collaborators

Minhang Hospital, Fudan University
Shanghai Zhongshan Hospital
Xuhui Central Hospital, Shanghai

Registry information

Official study title

The CT-based Deep Learning Model Outperforms Traditional Anatomical Classification Models in Preoperatively Predicting Complications and Risk Grade in Partial Nephrectomy

Important dates

Study start: 2024
Primary completion: 2024
Study completion: 2025
First posted: Mar 14, 2025
Registry last updated: Mar 14, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed