Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.
Journal:
European journal of radiology
Published Date:
Apr 29, 2025
Abstract
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 RCC patients who underwent contrast-enhanced CT scans were included in the analysis. Radiomic features were extracted, and a two-step feature selection methodology was applied to identify the most relevant features for classification. The XGBoost model demonstrated high performance in both training (AUC = 0.87) and testing (AUC = 0.92) sets, with no significant difference between the two (p = 0.521). The model also exhibited high sensitivity, specificity, positive predictive value, and negative predictive value. The selected radiomic features captured both the distribution of intensity values and spatial relationships, which may provide valuable insights for personalized treatment decision-making. Our findings suggest that the XGBoost model has the potential to be integrated into clinical workflows to facilitate personalized adjuvant immunotherapy decision-making, ultimately improving patient outcomes. Further research is needed to validate the model in larger, multicentre cohorts and explore the potential of combining radiomic features with other clinical and molecular data.
Authors
Keywords
Adult
Aged
Aged, 80 and over
Boosting Machine Learning Algorithms
Carcinoma, Renal Cell
Contrast Media
Female
Humans
Kidney Neoplasms
Machine Learning
Male
Middle Aged
Neoplasm Grading
Radiographic Image Interpretation, Computer-Assisted
Radiomics
Reproducibility of Results
Sensitivity and Specificity
Tomography, X-Ray Computed
