Abstract
BACKGROUND: Chordomas are rare, aggressive tumors of notochordal origin, commonly affecting the spine and skull base. Skull base chordomas (SBCs) comprise approximately 39% of cases, with an incidence of less than 1 per million annually in the United States. Prognosis remains poor due to resistance to chemotherapy, often requiring extensive surgical resection and adjuvant radiotherapy. Current classification methods based on chromosomal deletions are invasive and costly, presenting a need for alternative diagnostic tools. Radiomics allows for noninvasive SBC diagnosis and treatment planning. METHODS: We developed and validated radiomic-based models using MRI data to predict overall survival (OS) and progression-free survival following surgery (PFSS) in SBC patients. Machine-learning classifiers, including eXtreme Gradient Boosting (XGBoost), were employed along with feature selection techniques. Unsupervised clustering identified radiomic-based subgroups, which were correlated with chromosomal deletions and clinical outcomes. RESULTS: Our XGBoost model demonstrated superior predictive performance, achieving an area under the curve (AUC) of 83.33% for OS and 80.36% for PFSS, outperforming other classifiers. Radiomic clustering revealed two SBC groups with differing survival and molecular characteristics, strongly correlating with chromosomal deletion profiles. These findings indicate that radiomics can noninvasively characterize SBC phenotypes and stratify patients by prognosis. CONCLUSIONS: Radiomics shows promise as a reliable, noninvasive tool for the prognostication and classification of SBCs, minimizing the need for invasive genetic testing and supporting personalized treatment strategies.