Key result
A multivariate logistic regression model incorporating heart and lung volume terms achieved an AUC of 0.82, significantly outperforming traditional NTCP models based solely on heart dose (AUC 0.67, p=0.03).
Observational (n=90)
No
Effect estimate: AUC 0.82 (95% CI 0.73-0.90)
Absolute Event Rate: 0.82% vs 0.67%
p-value: p=0.03
Predicting radiation-induced valvular disease requires incorporating both heart and lung volume parameters, as heart-lung interactions significantly improve model performance over heart dose-volume alone.
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Standard heart-only NTCP models inadequately predict RVD; supports heart-lung volume inclusion in modeling but should not yet change practice.
Cella et al. (2014) conducted an observational in Radiation-induced heart valvular dysfunction (n=90). Multivariate logistic regression model (including maximum heart dose, heart volume, and lung volume) vs. Heart-only LKB and RS NTCP models was evaluated on Prediction of radiation-induced valvular defects (Area Under the ROC Curve) (AUC 0.82, 95% CI 0.73-0.90, p=0.03). A multivariate logistic regression model incorporating heart and lung volume terms achieved an AUC of 0.82, significantly outperforming traditional NTCP models based solely on heart dose (AUC 0.67, p=0.03).
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