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

Abstract

Skull base chordomas are rare, locally invasive tumors that remain a 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 and 2023. Preoperative contrast-enhanced T1-weighted MRI images were pre-processed and 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 of 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 an accuracy of 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.