Clinical trial NCT06876584: The CT-based Deep Learning Model Predicts Complications in Partial Nephrectomy
Official title: The CT-based Deep Learning Model Outperforms Traditional Anatomical Classification Models in Preoperatively Predicting Complications and Risk Grade in Partial Nephrectomy
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Renal Cell Carcinoma (RCC)
Renal Cyst
Keywords: CT-based deep learning
complication prediction
traditional classification model
partial nephrectomy
Lead sponsor: Fudan University
Sponsor class: OTHER
Collaborators: name: Shanghai Zhongshan Hospital
name: Minhang Hospital, Fudan University
name: Xuhui Central Hospital, Shanghai
First posted: 2025-03-14
Enrollment: 1474
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study population includes patients diagnosed with renal cell carcinoma or renal cyst who underwent partial nephrectomy in the participated centers. Clinical and imaging data were retrospectively collected from medical records, including demographic characteristics (age, gender, BMI), tumor location (left or right kidney), surgical details (surgical approach, ischemia time), and perioperative complications.

Patients were included based on the availability of complete clinical, surgical, and imaging data. Exclusion criteria comprised individuals with missing or unavailable imaging data, or no available enhanced CT images. The study aims to combine CT-based radiomics features and clinical features to develop a deep learning model to predict perioperative complications of partial nephrectomy, and compare with traditional anatomical classification models.

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.

Eligibility criteria: 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

Primary outcomes:
measure: whether complications occurred
timeFrame: perioperatively
Secondary outcomes:
measure: Patients' risk grade
timeFrame: perioperatively
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Name: Zhongshan Hospital Fudan University, Location: 180th Fenglin Road, Xuhui District, Shanghai, China
state: Xuhui District
Study officials:
References: