Predicción personalizada de supervivencia condicional para pacientes con cáncer de pulmón no microcítico en estadio...

Conditional survival (CS) estimates offer valuable prognostic insights for both clinicians and patients who have already survived a period following diagnosis. This study aimed to evaluate how survival outcomes evolve over time in patients with early-stage nonsmall cell lung cancer (NSCLC) and constructed a personalised CS-nomogram to provide dynamic prognostic predictions.

Patients with early-stage NSCLC diagnosed between 2004 and 2015 were identified from the Surveillance Epidemiology End Results (SEER) registry. The Aalen-Johansen estimator was used to estimate cancer-specific survival (CSS). LASSO regression were used to identify key prognostic factors. Multivariable Cox regression was used to construct the CS-nomogram. The model performance was evaluated in terms of discrimination, calibration, and clinical utility.

The findings indicated that the 5-year conditional survival rate exhibited an enhancement from an initial 66.1% to 87.6% among patients who had survived for a period of five years. The CS-nomogram demonstrated strong predictive performance, with concordance indices of 0.745 (95% CI: 0.742-0.748) and 0.751 (95% CI: 0.749-0.753) on the training and validation cohorts, respectively. The calibration curves exhibited a high degree of alignment with the ideal reference line, the AUC values for predictions from 1 to 10 years demonstrated stability, and decision curve analysis revealed a high net benefit, indicative of the model's superior performance.

The CS-nomogram offered dynamic and individualised prognostic assessments to support the long-term prognosis management.