Predicting radiation-induced valvular heart damage

Abstract

Purpose: To develop a predictive multivariate normal tissue complication probability (NTCP) model for radiation-induced heart valvular damage (RVD). The influence of combined heart-lung irradiation on RVD development was included.

Material and methods: Multivariate logistic regression modeling with the least absolute shrinkage and selection operator (LASSO) was used to build an NTCP model to predict RVD based on a cohort of 90 Hodgkin lymphoma patients treated with sequential chemo-radiation therapy. In addition to heart irradiation factors, clinical variables, along with left and right lung dose-volume histogram statistics, were included in the analysis. To avoid overfitting, 10-fold cross-validation (CV) was used for LASSO logistic regression modeling, with 50 reshuffled cycles. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and Spearman's correlation coefficient (Rs).

Results: At a median follow-up time of 55 months (range 12-92 months) after the end of radiation treatment, 27 of 90 patients (30%) manifested at least one kind of RVD (mild or moderate), with a higher incidence of left-sided valve defects (64%). Fourteen prognostic factors were frequently selected (more than 100/500 model fits) by LASSO, which included mainly heart and left lung dosimetric variables along with their volume variables. The averaged cross-validated performance was AUC-CV = 0.685 and Rs = 0.293. The overall performance of a final NTCP model for RVD obtained applying LASSO logistic regression to the full dataset was satisfactory (AUC = 0.84, Rs = 0.55, p < 0.001).

Conclusion: LASSO proved to be an improved and flexible modeling method for variable selection. Applying LASSO, we showed, for the first time, the importance of jointly considering left lung irradiation and left lung volume size in the prediction of subclinical radiation-related heart disease resulting in RVD.

Figure 1

(a) List of the 26 candidate variables and (b) their selection frequencies by least absolute shrinkage and selection operator (LASSO) logistic regression models during 10-fold cross validation with 50 iterations. Variables are named by their index numbers as listed in (a). The red dashed-line indicates the frequency of occurrence of 100; 14 variables are selected more than 100 times. (c) Deviance values of the LASSO models as a function of the regularization parameter λ. The optimal penalty λ, determined by 10-fold cross-validation, is the value that minimizes the deviance curve. The plot identifies the minimum-deviance (dashed line λ₀) and the minimum deviance within one standard error (dashed line λ₁) from λ₀. (d) Trace plot showing nonzero model coefficients as a function of the regularization parameter λ. As λ increases to the left, LASSO sets various coefficients to zero, removing them from the model. When λ corresponds to the minimum-deviance (dashed line λ₀), 12 variables are selected. When λ corresponds to the minimum-deviance within one standard error (dashed line λ₁) from λ₀, 8 variables are selected.

Figure 2

Comparison of prediction performance for LASSO logistic regression method and logistic regression method: a) box plot for AUC; b) box plot for Rs; c) box plot for p values.

Figure 3

Mean predicted rates of radiation induced valvular defects in binned groups (a) and calibration plot between the observed and predicted rates (b). The patients were binned into 5 groups based on the observed toxicity, with 1 being the lowest toxicity group and 5 being with the highest, with the equal number of patients (18) in each group.