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.

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.

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

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.

Clinical data and extracted radiomics feature data, excluding patient information.

Within six months after publication in the journal.

The data supporting this study are available from the enrolled institutions, but restrictions apply to their availability due to privacy reasons. Data can be accessed upon reasonable request from the corresponding author.