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• W2024996963 abstract "Predicting the risk of radiotherapy (RT) induced cardiac toxicity plays an important role in RT field design. Commonly used predictive models assume that the dose-response behavior can be characterized by pre-defined mathematical functions (parametric models), even though these function forms are speculative and hence may not reflect clinical reality. The subject of this work is to determine if models that do not assume any such pre-defined mathematical behavior (non-parametric models) can be better predictors of left breast RT-induced cardiac toxicity. Since 1998, we have been conducting an IRB-approved prospective clinical trial in left-sided breast/chest wall cancer patients receiving tangential photon therapy (46–50 Gy at 1.8–2 Gy/fx) to assess RT-induced changes in left ventricular (LV) function. LV functional changes were scored by single photon emission computed tomography (SPECT) perfusion defects. The basis of this study is 68 patients with normal pre-RT SPECT scans. Of these, 19 developed defects at 6 months post-RT. The dose-volume histograms (DVH) of all patients were used as inputs to a non-parametric linear discriminant analysis model (LDA) and three commonly used parametric models: Lyman normal tissue complication probability (LNTCP), relative seriality (RS), and generalized equivalent uniform dose (GEUD). The pre-defined mathematical functions of the parametric models were fitted to the data using statistical methods (maximum likelihood estimation and F-test). The non-parametric LDA model selected a linear combination of volumes above certain dose levels to best separate the groups with and without defects. The models were compared to each other using receiver operating characteristics (ROC) curves, which plot sensitivity vs. 1-specificity (greater area under the ROC curve implies a more accurate model). Optimistic estimates of each model’s predictive capabilities were obtained by generating ROC curves using all patient datasets in the model generation and error estimation (train-test-all). Additionally, pessimistic estimates were obtained using a technique (leave-one-out) where each patient dataset is used as a validation set with the remainder as the model generation set. ROC areas under the curves for train-test-all (leave-one-out) were 0.80 (0.75) for NTCP, 0.80 (0.78) for RS, 0.80 (0.74) for GEUD, and 0.91 (0.87) for LDA (see figure). Among the parametric models (NTCP, RS, GEUD), a test of the superiority of one over the other was not significant (p = 0.44), which can be qualitatively seen in the similarity of their ROC curves. The non-parametric LDA model was a significantly better predictor than the parametric models in the optimistic estimate (p = 0.03), but less so in the pessimistic estimate (p = 0.11). LDA selected volumes above 30 and 58 Gy as most important in separating the groups. As a predictor of RT-induced left-ventricular perfusion defects, commonly used parametric predictive models (which assume that the dose-response behavior is characterized by pre-defined mathematical functions) appear to be inferior to the linear discriminant analysis model (which does not assume a dose-response behavior). Linear discriminant analysis essentially identified two left-ventricular volumes as most important in separating the groups: volume within the fields, and volume at high dose." @default.
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• W2024996963 date "2004-09-01" @default.
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• W2024996963 title "Comparison of biological models to predict the incidence of breast radiotherapy-induced cardiac perfusion defects" @default.
• W2024996963 doi "https://doi.org/10.1016/j.ijrobp.2004.06.068" @default.
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