Normal tissue complication models for clinically relevant acute esophagitis (>= grade 2) in patients treated with dose differentiated accelerated radiotherapy (DART-bid)

Background: One of the primary dose-limiting toxicities during thoracic irradiation is acute esophagitis (AE). The aim of this study is to investigate dosimetric and clinical predictors for AE grade >= 2 in patients treated with accelerated radiotherapy for locally advanced non-small cell lung cancer (NSCLC). Patients and methods: 66 NSCLC patients were included in the present analysis: 4 stage II, 44 stage IIIA and 18 stage IIIB. All patients received induction chemotherapy followed by dose differentiated accelerated radiotherapy (DART-bid). Depending on size (mean of three perpendicular diameters) tumors were binned in four dose groups: 6 cm 90 Gy. Patients were treated in 3D target splitting technique. In order to estimate the normal tissue complication probability (NTCP),two Lyman models and the cutoff-logistic regression model were fitted to the data with AE >= grade 2 as statistical endpoint. Inter-model comparison was performed with the corrected Akaike information criterion (AIC(c)),which calculates the model's quality of fit (likelihood value) in relation to its complexity (i.e. number of variables in the model) corrected by the number of patients in the dataset. Toxicity was documented prospectively according to RTOG. Results: The median follow up was 686 days (range 84-2921 days), 23/66 patients (35 %) experienced AE >= grade 2. The actuarial local control rates were 72.6 % and 59.4 % at 2 and 3 years, regional control was 91 % at both time points. The Lyman-MED model (D50 = 32.8 Gy, m = 0.48) and the cutoff dose model (D-c = 38 Gy) provide the most efficient fit to the current dataset. On multivariate analysis V38 (volume of the esophagus that receives 38 Gy or above, 95 %-CI 28.2-57.3) was the most significant predictor of AE >= grade 2 (HR = 1.05, CI 1.01-1.09, p = 0.007). Conclusion: Following high-dose accelerated radiotherapy the rate of AE >= grade 2 is slightly lower than reported for concomitant radio-chemotherapy with the additional benefit of markedly increased loco-regional tumor control. In the current patient cohort the most significant predictor of AE was found to be V38. A second clinically useful parameter in treatment planning may be MED (mean esophageal dose)

Normofractionated and moderately hypofractionated proton therapy: Comparison of acute toxicity and early quality of life outcomes

Aim Data on the safety of moderately hypofractionated proton beam therapy (PBT) are limited. The aim of this study is to compare the acute toxicity and early quality of life (QoL) outcomes of normofractionated (nPBT) and hypofractionated PBT (hPBT). Results Overall, the highest toxicity grades of G0, G1, G2, and G3 were observed in 7 (5%), 40 (28.8%), 78 (56.1%), and 15 (10.8%) patients, respectively. According to organ and site, no statistically significant differences were detected in the majority of toxicity comparisons (66.7%). For A&P, hPBT showed a more favorable toxicity profile as compared to nPBT with a higher frequency of G0 and G1 and a lower frequency of G2 and G3 events (p = 0.04), more patients with improvement (95.7% vs 70%, p = 0.023), and full resolution of toxicities (87% vs 50%, p = 0.008). Skin toxicity was unanimously milder for hPBT compared to nPBT in A&P and ST locations (p = 0.018 and p = 0.025, respectively). No significant differences in QoL were observed in 97% of comparisons for QLQ-C30 scale except for loss of appetite in H&N patients (+33.3 for nPBT and 0 for hPBT, p = 0.02) and role functioning for A&P patients (0 for nPBT vs +16.7 hPBT, p = 0.003). For QLQ-HN35, 97.9% of comparisons did not reveal significant differences, with pain as the only scale varying between the groups (-8.33 vs -25, p = 0.016). Conclusion Hypofractionated proton therapy offers non-inferior early safety and QoL as compared to normofractionated irradiation and warrants further clinical investigation

Motorcycle riders’ perceptions, attitudes and strategies: Findings from a focus group study

The popularity of motorcycle riding and the results from accident analyses constitute it as a major area of concern in road safety. Despite the importance of the human factor in motorcycle crashes, the need for a better understanding of the riding activity is not yet satisfied by academic research. Focus group discussions have been carried out with riders so as to obtain insights into the nature of riding, the risk factors that underlie this activity, as well as strategic and tactical issues. Results concern key areas of interest in motorcycle riding behaviour: riders' individual behaviour, interactions among riders or with other road users, environment-related hazards and improvement suggestions for riding safety. The hazards originating from the environment and other road users that have been identified by the riders should be considered in further quantitative research and the implementation of corresponding countermeasures needs to be promoted. More communication is needed among road user groups and stakeholders, taking into account the needs of riders. On the other hand, the results reveal that riders might be reluctant to acknowledge the necessary contribution to the improvement of riding safety by changes in individual riding behaviour. Self-reflection should be encouraged, considering the role hedonistic objectives may play in this context. The outcome of this study permits giving preliminary recommendations on potentially beneficial education and training measures, and identifying specific topics that should be further investigated by quantitative research, such as naturalistic riding studies

Forecasting under model uncertainty : non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models