Abstract
PurposeThe purpose of this study is to compare different normal tissue complication probability (NTCP) models for predicting heart valve dysfunction (RVD) following thoracic irradiation.MethodsAll patients from our institutional Hodgkin lymphoma survivors database with analyzable datasets were included (n = 90). All patients were treated with three-dimensional conformal radiotherapy with a median total dose of 32 Gy. The cardiac toxicity profile was available for each patient. Heart and lung dose-volume histograms (DVHs) were extracted and both organs were considered for Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) NTCP model fitting using maximum likelihood estimation. Bootstrap refitting was used to test the robustness of the model fit. Model performance was estimated using the area under the receiver operating characteristic curve (AUC).ResultsUsing only heart-DVHs, parameter estimates were, for the LKB model: D50 = 32.8 Gy, n = 0.16 and m = 0.67; and for the RS model: D50 = 32.4 Gy, s = 0.99 and γ = 0.42. AUC values were 0.67 for LKB and 0.66 for RS, respectively. Similar performance was obtained for models using only lung-DVHs (LKB: D50 = 33.2 Gy, n = 0.01, m = 0.19, AUC = 0.68; RS: D50 = 24.4 Gy, s = 0.99, γ = 2.12, AUC = 0.66). Bootstrap result showed that the parameter fits for lung-LKB were extremely robust. A combined heart-lung LKB model was also tested and showed a minor improvement (AUC = 0.70). However, the best performance was obtained using the previously determined multivariate regression model including maximum heart dose with increasing risk for larger heart and smaller lung volumes (AUC = 0.82).ConclusionsThe risk of radiation induced valvular disease cannot be modeled using NTCP models only based on heart dose-volume distribution. A predictive model with an improved performance can be obtained but requires the inclusion of heart and lung volume terms, indicating that heart-lung interactions are apparently important for this endpoint.
Highlights
Technological advances in radiation therapy have increased user control over organ-at-risk dose distributions
The aim of the present study is to test the predictive power of traditional LKB and the Relative Seriality (RS) normal tissue complication probability (NTCP) models for the induction of asymptomatic radio-induced valvular defects (RVD) using a dataset of Hodgkin’s lymphoma (HL) patients, and to compare this to an updated multivariate logistic regression model fit to the current, larger dataset
In a previous analysis of RVD [16] on a subset (56 patients) of the present HL survivors dataset, we developed a 3-variable logistic regression model consisting of the maximum heart dose (HDmax), heart volume (HVol), and lungs volume (LVol) given by
Summary
Technological advances in radiation therapy have increased user control over organ-at-risk dose distributions. In a modern radiotherapy setting, radiobiological models potentially play an essential role and normal tissue complication probability (NTCP) modeling may help to identify the optimal plan that minimizes side effects for individual patients. The toxicity endpoint that have been modeled include radiation-associated cardiac disease [1]. Late cardiac toxicity is one of the most feared side effects of therapeutic thoracic radiation therapy. Modeling radiation-induced heart disease is hampered both by the relatively low incidence of the complication and the lack of long term results from 3D-based thoracic RT [2,3,4]
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