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.
Study Type
OBSERVATIONAL
Enrollment
1,474
Name: Zhongshan Hospital Fudan University, Location: 180th Fenglin Road, Xuhui District, Shanghai, China
Shanghai, Xuhui District, China
whether complications occurred
Retrospectively review the medical record system to determine whether patients developed postoperative complications.
Time frame: perioperatively
Patients' risk grade
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.
Time frame: perioperatively
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