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| Home > Publications database > Radiomics for the Prediction of Postoperative Chronic Kidney Disease in Renal Tumor Patients Undergoing Surgical Resection. |
| Home > Publications database > Radiomics for the Prediction of Postoperative Chronic Kidney Disease in Renal Tumor Patients Undergoing Surgical Resection. |
| Journal Article | DKFZ-2026-00892 |
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2026
Karger
Basel
Abstract: Chronic kidney disease (CKD) is a significant concern following renal tumor surgery, impacting long-term renal function and patient outcomes. This study investigated the potential of computed tomography (CT)-based radiomics as a quantitative imaging approach to predict postoperative CKD in kidney tumor patients.We included adult patients with renal tumor surgery treated at our center between 2012 and 2022. Preoperative retrospective CT-imaging data were analyzed, and radiomic features were extracted from tumor lesions and renal parenchyma. Machine learning models were trained to predict postoperative new-onset CKD based on clinical information and radiomics. Model performance was assessed using five-fold cross-validation on the training set (n = 65) and on a separate test set (n = 17). Model performance was primarily evaluated using the receiver operating characteristic curve, with the area under the curve (AUC) serving as the principal summary metric.The study cohort comprised n = 82 patients, of whom n = 25 (30%) developed postoperative new-onset CKD. The best models achieved a mean validation AUC of 0.74 [95% CI: 0.60-0.86] for solely radiomics, 0.83 [0.73-0.93] with clinical information only, and 0.80 [0.67-0.91] on radiomics and clinical parameters, respectively (p > 0.05). For the test dataset, AUCs were 0.62 [95% CI: 0.29-0.92], 0.77 [0.50-0.98], and 0.80 [0.52-1.00], respectively (p > 0.05).Preoperative CT-based radiomic features in combination with clinical information can serve as a noninvasive predictor of postoperative CKD in renal tumor patients undergoing surgical resection. While prospective and external validation is needed, this approach facilitated clinical decision-making and enabled personalized treatment strategies in patients with renal tumors.
Keyword(s): Chronic kidney disease ; Machine learning ; Radiomics ; Renal cancer ; Renal surgery
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