Formulae to calculate margins for external beam radiotherapy traditionally treat the radiotherapy regimen for analysis purposes as consisting of a very large number of treatment sessions, each delivering a very small dose. It is assumed that the difference in the calculated margin for an infinitely fractionated regimen, and the margin for real world treatment scenarios with finite fractions per patient, is negligible given the usual large number of fractions used to treat most malignancies. However hypofractionated radiotherapy using five fractions or even fewer is becoming increasingly common, causing the accuracy of traditional margin formulae to break down. This work introduces an algorithm that accurately calculates margins for hypofractionated treatment regimens. A method for estimating an upper limit for the required margin is introduced, and an adjustment to the van Herk formula (van Herk et al (2000 Int. J. Radiat. Oncol. Biol. Phys. 47 1121-35)) is used to provide a lower limit. A single-term mathematical model is then used to interpolate between these limits. Results are compared to those calculated by Monte Carlo simulation, demonstrating that the proposed method yields accurate estimates of the actual margin required (consistently within ~1%) for wide ranges of values of the systematic error, random error and penumbral width, even when the number of fractions per patient is as low as 2.

Long-term survivors of childhood, adolescent and young adult (AYA) malignancies with past exposure to potentially cardiotoxic treatments are at risk of peripartum cardiac dysfunction. Incidence and risk factors for peripartum cardiac dysfunction and maternal cardiac outcomes in this population were investigated. Eligible long-term survivors were aged <30 years at cancer diagnosis, with ≥1 pregnancy occurring ≥5 years after diagnosis. “Peripartum” cardiac events were defined as occurring within pregnancy or ≤5months after delivery. Cardiac events were classified “symptomatic” or “subclinical”. “Peripartum cardiomyopathy” (PPCM) was defined as symptomatic dysfunction without prior cardiac dysfunction. Of 64 eligible women, 5 (7.8%) had peripartum cardiac events: 3 symptomatic, 2 subclinical. Of 110 live births, 2 (1.8%, 95% CI 0.2–6.4) were defined as PPCM: Significantly greater than the published general population incidence of 1:3000 (p < 0.001), representing a 55-fold (95% CI 6.6–192.0) increased risk. Risk factor analyses were hypothesis-generating, revealing younger age at cancer diagnosis and higher anthracycline dose. Postpartum, cardiac function of 4 women (80%) failed to return to baseline. In conclusion, peripartum cardiac dysfunction is an uncommon but potentially serious complication in long-term survivors of paediatric and AYA malignancies previously treated with cardiotoxic therapies. Peripartum cardiac assessment is strongly recommended for at-risk patients.

Early approaches to characterizing errors in target displacement during a fractionated course of radiotherapy assumed that the underlying fraction-to-fraction variability in target displacement, known as the 'treatment error' or 'random error', could be regarded as constant across patients. More recent approaches have modelled target displacement allowing for differences in random error between patients. However, until recently it has not been feasible to compare the goodness of fit of alternate models of random error rigorously. This is because the large volumes of real patient data necessary to distinguish between alternative models have only very recently become available. This work uses real-world displacement data collected from 365 patients undergoing radical radiotherapy for prostate cancer to compare five candidate models for target displacement. The simplest model assumes constant random errors across patients, while other models allow for random errors that vary according to one of several candidate distributions. Bayesian statistics and Markov Chain Monte Carlo simulation of the model parameters are used to compare model goodness of fit. We conclude that modelling the random error as inverse gamma distributed provides a clearly superior fit over all alternatives considered. This finding can facilitate more accurate margin recipes and correction strategies.

Randomized controlled trials can be used to generate evidence on the efficacy and safety of new treatments in eating disorders research. Many of the trials previously conducted in this area have been deemed to be of low quality, in part due to a number of practical constraints. This article provides an overview of established and more innovative clinical trial designs, accompanied by pertinent examples, to highlight how design choices can enhance flexibility and improve efficiency of both resource allocation and participant involvement. Trial designs include individually randomized, cluster randomized, and designs with randomizations at multiple time points and/or addressing several research questions (master protocol studies). Design features include the use of adaptations and considerations for pragmatic or registry‐based trials. The appropriate choice of trial design, together with rigorous trial conduct, reporting and analysis, can establish high‐quality evidence to advance knowledge in the field. It is anticipated that this article will provide a broad and contemporary introduction to trial designs and will help researchers make informed trial design choices for improved testing of new interventions in eating disorders.Public Significance: There is a paucity of high quality randomized controlled trials that have been conducted in eating disorders, highlighting the need to identify where efficiency gains in trial design may be possible to advance the eating disorder research field. We provide an overview of some key trial designs and features which may offer solutions to practical constraints and increase trial efficiency.