NCT06876584

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

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 1,474 (actual)
• Sponsor: Du Lingzhi · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

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

Timeline

• Start date: 2024-06-01
• Primary completion: 2024-12-31
• Completion: 2025-02-28
• First posted: 2025-03-14
• Last updated: 2025-03-14

Locations

1 site across 1 country: China