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

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06876584

Enrollment

1474

Registered

2025-03-14

Start date

2024-06-01

Completion date

2025-02-28

Last updated

2025-03-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Renal Cell Carcinoma (RCC), Renal Cyst

Keywords

CT-based deep learning, complication prediction, traditional classification model, partial nephrectomy

Brief 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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

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

Design outcomes

Primary

Measure	Time frame	Description
whether complications occurred	perioperatively	Retrospectively review the medical record system to determine whether patients developed postoperative complications.

Secondary

Measure	Time frame	Description
Patients' risk grade	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.

Countries

China

Outcome results