The CT-based deep learning model outperforms traditional anatomical classification models in preoperatively predicting complications and risk grade in partial nephrectomy.
A deep learning model integrating CT radiomics and clinical features was developed to predict perioperative complications and risk grade in patients undergoing partial nephrectomy, and was compared to traditional anatomical classification models.
Between June 2014 and July 2024, 1214 patients diagnosed with renal cell carcinoma or renal cysts who underwent partial nephrectomy were included. A deep learning model incorporating CT radiomics (segmented by nnU-Net and extracted by pyradiomics) and clinical features was developed. Logistic regression models using RENAL or PADUA scores were also developed for comparison. An external validation cohort (n = 260) was used to assess the model's generalizability.
In predicting complications, the deep learning model achieved an area under the curve (AUC) of 0.87 (95%CI: 0.80-0.93), outperforming the RENAL (0.68, 95%CI: 0.60-0.70) (p < 0.001) and PADUA models (0.69, 95%CI: 0.55-0.71) (p < 0.001). For risk grades, the deep learning model outperformed RENAL/PADUA models for the no-risk group (AUC = 0.83 [95%CI: 0.81-0.87] vs. 0.68 [95%CI: 0.58-0.71], p = 0.01; 0.66 [95%CI: 0.65-0.67], p < 0.001) and low-risk group (AUC = 0.79 [95%CI: 0.75-0.82] vs. 0.64 [95%CI: 0.60-0.74], p = 0.03; 0.66 [95%CI: 0.63-0.73], p = 0.04). However, no significant differences were found for moderate- and high-risk groups (p > 0.05). In the external validation cohort, the model achieved a prediction accuracy of 0.854 and an AUC of 0.83.
The CT-based deep learning model showed superior performance in predicting complications and risk grades for no-risk and low-risk patients undergoing partial nephrectomy. No significant differences were found for moderate- and high-risk groups.
Between June 2014 and July 2024, 1214 patients diagnosed with renal cell carcinoma or renal cysts who underwent partial nephrectomy were included. A deep learning model incorporating CT radiomics (segmented by nnU-Net and extracted by pyradiomics) and clinical features was developed. Logistic regression models using RENAL or PADUA scores were also developed for comparison. An external validation cohort (n = 260) was used to assess the model's generalizability.
In predicting complications, the deep learning model achieved an area under the curve (AUC) of 0.87 (95%CI: 0.80-0.93), outperforming the RENAL (0.68, 95%CI: 0.60-0.70) (p < 0.001) and PADUA models (0.69, 95%CI: 0.55-0.71) (p < 0.001). For risk grades, the deep learning model outperformed RENAL/PADUA models for the no-risk group (AUC = 0.83 [95%CI: 0.81-0.87] vs. 0.68 [95%CI: 0.58-0.71], p = 0.01; 0.66 [95%CI: 0.65-0.67], p < 0.001) and low-risk group (AUC = 0.79 [95%CI: 0.75-0.82] vs. 0.64 [95%CI: 0.60-0.74], p = 0.03; 0.66 [95%CI: 0.63-0.73], p = 0.04). However, no significant differences were found for moderate- and high-risk groups (p > 0.05). In the external validation cohort, the model achieved a prediction accuracy of 0.854 and an AUC of 0.83.
The CT-based deep learning model showed superior performance in predicting complications and risk grades for no-risk and low-risk patients undergoing partial nephrectomy. No significant differences were found for moderate- and high-risk groups.
Authors
Du Du, Cheng Cheng, Shen Shen, Cheng Cheng, Yang Yang, He He, Xu Xu, Lin Lin, Liu Liu, Hu Hu, Huang Huang, Pang Pang, Xu Xu, Guo Guo, Zhu Zhu, Wang Wang
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