Impact of contrast enhancement phase on CT-based radiomics analysis for predicting post-surgical recurrence in renal cell carcinoma.
Authors
Affiliations (7)
Affiliations (7)
- Department of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA. [email protected].
- Department of Urology, University of Rennes, Rennes, France. [email protected].
- Image and Signal Processing Laboratory, Inserm U1099, University of Rennes, Rennes, France. [email protected].
- Department of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA.
- Department of Urology, University of Rennes, Rennes, France.
- Image and Signal Processing Laboratory, Inserm U1099, University of Rennes, Rennes, France.
- Department of Radiation Oncology, CLCC Eugene Marquis, Rennes, France.
Abstract
To investigate the effect of CT enhancement phase on radiomics features for predicting post-surgical recurrence of clear cell renal cell carcinoma (ccRCC). This retrospective study included 144 patients who underwent radical or partial nephrectomy for ccRCC. Preoperative multiphase abdominal CT scans (non-contrast, corticomedullary, and nephrographic phases) were obtained for each patient. Automated segmentation of renal masses was performed using the nnU-Net framework. Radiomics signatures (RS) were developed for each phase using ensembles of machine learning-based models (Random Survival Forests [RSF], Survival Support Vector Machines [S-SVM], and Extreme Gradient Boosting [XGBoost]) with and without feature selection. Feature selection was performed using Affinity Propagation Clustering. The primary endpoint was disease-free survival, assessed by concordance index (C-index). The study included 144 patients. Radical and partial nephrectomies were performed in 81% and 19% of patients, respectively, with 81% of tumors classified as high grade. Disease recurrence occurred in 74 patients (51%). A total of 1,316 radiomics features were extracted per phase per patient. Without feature selection, C-index values for RSF, S-SVM, XGBoost, and Penalized Cox models ranged from 0.43 to 0.61 across phases. With Affinity Propagation feature selection, C-index values improved to 0.51-0.74, with the corticomedullary phase achieving the highest performance (C-index up to 0.74). The results of our study indicate that radiomics analysis of corticomedullary phase contrast-enhanced CT images may provide valuable predictive insight into recurrence risk for non-metastatic ccRCC following surgical resection. However, the lack of external validation is a limitation, and further studies are needed to confirm these findings in independent cohorts.