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Purpose: To generate synthetic relative proton stopping power (sRPSP) images from magnetic resonance imaging (MRI) sequence(s) and develop an online quality assurance (QA) tool for sRPSP to facilitate safe integration of magnetic resonance (MR)‐only proton planning into clinical practice. Materials and methods: Planning computed tomography (CT) and MR images of 195 pediatric brain tumor patients were utilized (training: 150, testing: 45). Seventeen consistent‐cycle generative adversarial network (ccGAN) models were trained separately using paired CT‐converted RPSP and MRI datasets to transform a subject's MRI into sRPSP. T1‐weighted (T1W), T2‐weighted (T2W), and FLAIR MRI were permutated to form 17 combinations, with or without preprocessing, for determining the optimal training sequence(s). For evaluation, sRPSP images were converted to synthetic CT (sCT) and compared to the real CT in terms of mean absolute error (MAE) in Hounsfield units (HU). For QA, sCT was deformed and compared to a reference template built from training dataset to produce a flag map, highlighting pixels that deviate by >100 HU and fall outside the mean ± standard deviation reference intensity. The gamma intensity analysis (10%/3 mm) of the deformed sCT against the QA template on the intensity difference was investigated as a surrogate of sCT accuracy. Results: The sRPSP images generated from a single T1W or T2W sequence outperformed that generated from multi‐MRI sequences in terms of MAE (all p < 0.05). Preprocessing with N4 bias and histogram matching reduced MAE of T2W MRI‐based sCT (54 ± 21 HU vs. 42 ± 13 HU, p = 0.002). The gamma intensity analysis of sCT against the QA template was highly correlated with the MAE of sCT against the real CT in the testing cohort (r = ‐0.89 for T1W sCT; r = ‐0.93 for T2W sCT). Conclusion: Accurate sRPSP images can be generated from T1W/T2W MRI for proton planning. A QA tool highlights regions of inaccuracy, flagging problematic cases unsuitable for clinical use. [ABSTRACT FROM AUTHOR]

Although regression relationships commonly are treated as linear, this often is not the case. An adaptive approach is described for identifying nonlinear relationships based on power transforms of predictor (or independent) variables and for assessing whether or not relationships are distinctly nonlinear. It is also possible to model adaptively both means and variances of continuous outcome (or dependent) variables and to adaptively power transform positive-valued continuous outcomes, along with their predictors. Example analyses are provided of data from parents in a nursing study on emotional-health-related quality of life for childhood brain tumor survivors as a function of the effort to manage the survivors' condition. These analyses demonstrate that relationships, including moderation relationships, can be distinctly nonlinear, that conclusions about means can be affected by accounting for non-constant variances, and that outcome transformation along with predictor transformation can provide distinct improvements and can resolve skewness problems.© 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Objective: Our aim was to expand research on predictors of health-related quality of life (HRQOL) for adolescent and young adult survivors of childhood brain tumors who are not living independently by evaluating the mediating role of family functioning in the association of disease severity/treatment late effects with survivor self-report and caregiver-proxy report of physical and emotional HRQOL. Methods: Mothers (N= 186) and their survivors living at home (N= 126) completed self-report and caregiver-proxy report of physical and emotional HRQOL. Mothers completed family functioning measures of general family functioning, caregiving demands, and caregiver distress. Medical file review and caregiver report were used to evaluate disease severity/treatment late effects. Results: Using structural equation models, family functioning was adjusted for sociodemographic factors. Disease severity/treatment late effects had significant direct effects on self-report and caregiver-proxy report of physical and emotional HRQOL. Family functioning had a significant direct effect on caregiver-proxy report of physical and emotional HRQOL, but these findings were not confirmed for self-report HRQOL. Model-fit indices suggested good fit of the models, but the mediation effect of family functioning was not supported. Conclusions: Disease severity/treatment late effects explained self-report and caregiver-proxy report of physical and emotional HRQOL for these adolescent and young adult survivors of childhood brain tumors. Family functioning was implicated as an important factor for caregiver-proxy report only. To enhance physical and emotional HRQOL, findings underscore the importance of coordinated, multidisciplinary follow-up care for the survivors who are not living independently and their families to address treatment late effects and support family management. [ABSTRACT FROM AUTHOR]