Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.
Authors
Affiliations (10)
Affiliations (10)
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Radiologisch-Nuklearmedizinisches Zentrum (RNZ), Martin-Richter-Straße 43, 90489 Nürnberg, Germany. Electronic address: [email protected].
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany.
- Institute of Pathology, Faculty of Medicine, Ss Cyril and Methodius University ul. 50 Divizija bb 1000 Skopje, North Macedonia.
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany.
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Humanpathologie Dr. Weiß MVZ GmbH, Am Weichselgarten 30a, 91058 Erlangen-Tennenlohe, Germany.
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen - EMD, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany.
- Clinic of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany.
- Clinic of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; Comprehensive Cancer Center Erlangen - EMD, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany.
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen - EMD, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; BZKF: Bavarian Cancer Research Center (BZKF), Erlangen, Germany.
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; University Medical Center of Johannes Gutenberg-University Mainz, Department of Diagnostic and Interventional Radiology, Langenbeckstr. 1, 55131 Mainz, Germany.
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.