Machine learning in automatic detection of chordoma signature, postoperative residuals, and prognosis of skull base chordomas

Skull base chordomas are rare,locally invasive tumors that remain diagnostic and therapeutic challenge. We developed a machine-learning (ML) radiomics model to (i) distinguish chordoma from chondrosarcoma and skull base background, (ii) differentiate true postoperative residual tumor from treatment-related changes, and (iii) predict 2-year progression-free survival (PFS). In this retrospective, dual-center study, 61 patients underwent surgery between 1998-2023.Preoperative contrast-enhanced T1-weighted MRI images were pre-processed segmented; data were augmented by 20%. ML models included nested cross-validated XGBoost and a 4-layer standard feedforward Multilayer Perceptron (MLP)(Python,Keras). The primary and secondary endpoints were diagnostic discrimination and residual-versus-treatment-related change classification; the exploratory endpoint was 2-year PFS prediction. XGBoost achieved diagnostic accuracy 0.90 (95% CI, 0.84–0.96) in distinguishing chordoma from chondrosarcoma/skull base background, and residual-versus-change accuracy 0.91 (95% CI, 0.85–0.96). PFS prediction reached accuracy 0.87 (95% CI, 0.74–0.98). MLP showed comparable performance (diagnostic validation accuracy 0.89; residual classification 0.90; PFS 0.93). To our knowledge, this is the first dual-center MRI-based ML study to jointly address preoperative histologic discrimination, postoperative residual detection, and short-term PFS prediction in a small, heterogeneous cohort. These results support future clinical translation as a noninvasive decision-support tool for preoperative assessment, postoperative surveillance, and risk stratification.