Individualized estimation of conditional survival for patients with spinal chordoma

Unlike traditional survival analysis methods, conditional survival (CS) provides enhanced insight by offering a personalized prognosis estimation as time advances for tumor patients. This study aimed to estimate CS and devised a novel CS-nomogram for real-time prediction of 10-year CS for patients with spinal chordoma.

Patients diagnosed with spinal chordoma from 2000 to 2019, as documented in the Surveillance, Epidemiology, and End Results (SEER) database, were included in this study. CS represents the likelihood of surviving an additional y years given that the patient has already survived x years. It is computed using the equation CS(x|y) = S(x + y)/S(x), where S(x) denotes the patient's survival rate at x years. The univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO) analysis and best subset regression (BSR) methods were employed for variable selection. Based on these selected factors, the CS-based nomogram and a risk classification system were developed. Finally, several approaches were used to validate the performance of our model.

Between 2000 and 2019, the SEER database identified 730 patients with spinal chordoma, distributed into 510 in the training group and 220 in the validation group. CS analysis showed that patients experienced a gradual augmentation in their 10-year survival rates over the course of each additional year post-diagnosis. We also successfully created a CS-based nomogram model for forecasting 3-, 5-, and 10-year overall survival, along with 10-year CS. The CS-based nomogram incorporating age, tumor size, tumor extension, multiple primary tumors, and surgery demonstrated robust predictive capabilities. Moreover, a novel risk classification system was constructed to aid in tailored management strategies and personalized treatment decisions for spinal chordoma patients.

In contrast to traditional survival assessment methods, our analysis of CS yielded more dynamic and real-time outcomes for spinal chordoma patients. Via our CS-based nomogram model and risk classification system, we have provided more precise prognostic insights for these patients, aiding in treatment planning and follow-up strategy formulation in clinical settings.