- / The treatment techniques applied in the chest area (breast and lung cancer) in radiotherapy, increase the lung toxicity leading to the development of pulmonary complications. The aim of this study is to compare the predictive strength of different radiobiological models in the evaluation of radiation pneumonitis, correlate the absorbed dose with the severity of the clinical outcome and examine biological factors that may affect or induce complications after irradiation. Furthermore, due to the fact that the value of each parameter is accompanied by its confidence interval, every model is represented by a group of dose-response curves that create a range in which each radiobiological model can vary. The range of each model is very important when selecting the values of parameters used, due to the existence of coincendence areas between the models. The study was based on 179 breast cancer patients undergoing radiotherapy. Dose volume histograms and the clnical treatment outcome for every patient were available. 24 patients scored radiation pneumonitis grade 2 and 65 showed milder symptoms of grade 1. Normal tissue complication probabilities were computed for every model for each patient. Moreover, statistical analysis was applied to investigate whether the absorbed dose is the only factor that influeces the development of radiation pneumonitis in breast cancer radiotherapy (x2 test) and the ability of the radiobiological models used to discriminate cases that developed radiation pneumonitis from those that did not (ROC curves). The Relative Seriality model described with greater accuracy the clinical outcome in contrast to the LKB and Parallel model. The statistical results showed that radiation pneumonitis in the case of breast cancer radiotherapy does not depend only on the absorbed dose but on other radiobiological factors that induce the development of complication, such as the intrinsic radiosensitivity of each patient. ROC curves pointed out the weakness of the models to discriminate cases of complication from cases of non-complication. Finally, this study accented the importance to use parameter values extracted from patients groups with similar clinical characteristics as the ones examined in order to avoid the coincidence areas between the models.