Risk calculator for long-term survival prediction of spinal chordoma versus chondrosarcoma: a nationwide analysis.

PURPOSE

Chordomas and chondrosarcomas are rare, aggressive spinal bone tumors with distinct origins, biological behavior, and treatment challenges, primarily due to their resistance to conventional chemotherapy and radiation. This study aimed to compare clinical characteristics, treatment strategies, and long-term outcomes between spinal chordoma and chondrosarcoma, and to develop a robust machine learning-based model for individualized survival prediction.

METHODS

We conducted a retrospective analysis using the National Cancer Database (NCDB) to identify patients diagnosed with spinal chordoma or chondrosarcoma from 2004 to 2017. Demographics, tumor characteristics, comorbidity indices, treatment modalities (surgery, radiation, chemotherapy), and outcomes were extracted. Kaplan-Meier and weighted log-rank analyses assessed overall survival (OS) at predefined intervals (30-day, 90-day, 1-year, 5-year, 10-year). Twelve machine learning and deep learning models were trained to predict 10-year OS. Model performance was evaluated using AUC, Brier Score, and Concordance Index (C-index). A web-based risk calculator was developed using the best-performing ensemble model.

RESULTS

A total of 3175 patients were included (chordoma: n = 1204; chondrosarcoma: n = 1971). Chordoma patients were significantly older, travelled farther for treatment, and had smaller tumors with lower rates of metastatic disease at presentation. Chondrosarcoma patients more frequently underwent gross total resection, while chordoma patients received more radiation therapy, often with higher doses and more frequent use of proton therapy. Kaplan-Meier analysis revealed that chordoma patients had superior 10-year OS compared to chondrosarcoma patients (p < 0.0001). Among those receiving radiation, chondrosarcoma patients treated with radiation alone had the poorest survival. DeepSurv achieved the highest C-index (0.83) and lowest Brier Score (0.14), while ensemble models integrating Gradient Boosting and CatBoost also demonstrated strong performance (AUC > 0.80). Age, tumor type, and radiation therapy were identified as the most influential predictors using SHAP analysis. A publicly accessible, web-based calculator was developed for individualized survival prediction.

CONCLUSION

Spinal chordoma and chondrosarcoma differ significantly in clinical features and outcomes, with chordoma showing more favorable long-term survival. The findings highlight the importance of GTR and individualized radiation therapy in optimizing outcomes. The predictive model employing complicated machine learning models provides a valuable tool for estimating long-term survival and guiding personalized treatment strategies, though external validation is needed to strengthen its generalizability and clinical utility.