The CT-based Deep Learning Model Outperforms Traditional Anatomical Classification Models in Preoperatively Predicting Complications and Risk Grade in Partial Nephrectomy
The CT-based Deep Learning Model Outperforms Traditional Anatomical Classification Models in Preoperatively Predicting Complications and Risk Grade in Partial Nephrectomy
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.
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