Abstract
Multiple competing normal tissue complication probability (NTCP) models have been proposed for predicting symptomatic radiation-induced lung injury in human. In this paper we tested the efficacy of four common NTCP models applied quantitatively to sub-clinical X-ray computed tomography (CT)-density changes in the lung following radiotherapy. Radiotherapy planning datasets and follow-up chest CTs were obtained in eight patients treated for targets within the lung or hilar region. Image pixel-wise radiation dose exposure versus change in observable CT Hounsfield units was recorded for early (2–5 months) and late (6–9 months) time-points. Four NTCP models, Lyman, Logistic, Weibull and Poisson, were fit to the population data. The quality of fits was assessed by five statistical criteria. All four models fit the data significantly (p < 0.05) well at early, late and cumulative time points. The Lyman model fitted best for early effects while the Weibull Model fitted best for late effects. No significant difference was found between the fits of the models and with respect to parameters D50 and γ50. The D50 estimates were more robust than γ50 to image registration error. For analyzing population-based sub-clinical CT pixel intensity-based dose response, all four models performed well.
Highlights
Multiple competing normal tissue complication probability (NTCP) models have been proposed for predicting symptomatic radiation-induced lung injury in human
The quality of fits was compared by the computing sum of squared residuals (SSR), Adjusted R2, Akaike information criterion (AIC)[28] and the Bayesian information criterion (BIC)[29]
An Analysis of Variance (ANOVA) revealed that all models individually fitted the true data significantly (p < 0.05) well for early effect, late effect and for the cumulative time points
Summary
Multiple competing normal tissue complication probability (NTCP) models have been proposed for predicting symptomatic radiation-induced lung injury in human. All four models fit the data significantly (p < 0.05) well at early, late and cumulative time points. Radiation oncology has relied on treatment protocols for various cancers and target organs. This empirical method will likely shift to biological-based treatment planning. Incorporating radiomics in the investigation and use of treatment models will allow for a more patient specific approach. This approach may lead to dose escalation or de-escalation based on cancer genomics and patient factors such as comorbidities, inflammatory markers, cytokines and other serologies. Unlike RP, RILF is a late effect evolving 6–24 months after radiotherapy and is marked by irreversible reduced lung function[7,8]. RILF is associated with pulmonary hypertension, increasing the risk of developing right heart failure[9]
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