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

Du Lingzhi

Status: Completed

Conditions: Renal Cell Carcinoma (RCC); Renal Cyst

Study type: Observational

Funder types: Other

Identifiers

NCT06876584

zsurologyDLMforPNcomplication

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,474 patients

Sex: All

Volunteers: No Healthy Volunteers

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

Trial design

1,474 participants in 2 patient groups

Complication 1

Description:

Patients who experienced perioperative complications during the partial nephrectomy

Complication 0

Description:

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