During the study workup, Bayesian adaptive designs based on the continual reassessment method were compared with a more standard rule‐based design, with the aim of choosing a design that would maximise the scientific information gained from the study. The process of specifying and evaluating the design options was time consuming and required the active involvement of all members of the trial's protocol development team. However, the effort was worthwhile as the originally proposed rule‐based design has been replaced by a more efficient Bayesian adaptive design. While the outcome to be modelled, design details and evaluation criteria are trial specific, the principles behind their selection are general. This case study illustrates the steps required to establish a design in a novel context. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons LtdThe design of phase I studies is often challenging, because of limited evidence to inform study protocols. Adaptive designs are now well established in cancer but much less so in other clinical areas. A phase I study to assess the safety, pharmacokinetic profile and antiretroviral efficacy of C34‐PEG These are first in human studies, usually involving small numbers of participants, but they are crucial in the development of new treatments. A poor trial design will increase the chance of discontinuing research into a promising drug prematurely or unnecessarily investing further resource in a drug. Additionally, a well‐chosen dose to take forward is essential for maximising the probability of making the correct decision about the drug at the end of any follow‐up study. Given the stakes, the choice of design is an extremely important part of setting up a phase I study.The traditional approach for phase I trials is rule‐based, typically the ‘3+3’ design or variations et al.However, while easy to implement, the operating characteristics of rule‐based designs tend to be unattractive et al.Continual reassessment method designs were mainly developed in the field of oncology, where the aim is to find a dose that will benefit the patient without causing an unacceptable amount of toxicity, known as the maximum tolerated dose (MTD). This requires defining an acceptable level of toxicity, the target toxicity level (TTL) and a dose‐toxicity curve. These methods assume that there is a monotonic dose‐response relationship between the dose and the probability of dose‐limiting toxicity (DLT) for patients treated at that dose. Since its inception, various modifications to the CRM have been proposed to address safety concerns, for example starting at a more conservative dose level, treating more than one patient at the same level and not skipping doses et al.While the CRM has primarily been used in cancer trials, its use has also been demonstrated in early phase stroke trials 4‐Chol is a novel peptide fusion inhibitor for the treatment of HIV infection. The funding application to the British Medical Research Council (MRC) for a phase I study to assess C34 was based on a simple dose‐ascending, rule‐based design, but incorporated a request for resources to investigate alternative designs. In this paper, we describe our experience of developing an adaptive design using the CRM in this novel context.C34‐PEGBy documenting the process of the design development, we hope that others can learn from our experiences and be encouraged to explore the use of adaptive designs for their own studies. The main elements are described in the narrative of Sections 2Approximately 35 million people are infected with HIV worldwide There are now more older HIV‐positive individuals. The medical care of ageing HIV‐positive populations are complex for several reasons. Firstly, age‐related medical conditions appear to occur frequently and at a younger age in HIV‐positive subjects There are six current classes of antiretroviral drugs, four of which target viral replication through inhibition of viral enzymes  and two of which block viral cell entry. Current standard of care comprises a combination of three agents, coming from at least two different classes. Many agents are associated with long‐term toxicities including metabolic, renal and bone toxicities and as such, may be challenging to use in older subjects with age‐related medical problems.4‐Chol, has been developed which has been shown to be highly potent in vitro and is thought to possess potential as a once or twice weekly injectable agent C34 is a molecule belonging to the fusion inhibitor agents, a class of antiretroviral used exceptionally rarely because of the requirement for twice daily subcutaneous injections and frequent injection site reactions but which remain highly attractive to further exploitation in view of their very low systemic toxicity profile and limited propensity for drug–drug interactions Given the advantages of C34 outlined previously, we plan to run a dose and dose‐schedule finding phase I, first in man study to assess the safety, PK and pharmacodynamics of C34 in people with HIV infection. This will be a two‐stage, double‐blind, randomised, placebo‐controlled study. In stage 1, a single dose of C34 will be administered, while stage 2 will assess multiple‐dosing of C34. The population considered for this trial will be HIV‐1 infected male adults, in reasonably good health and not imminently requiring antiretroviral therapy. This places practical constraints on finding participants, as there is a small window for their recruitment, and with changing clinical practice, this window is becoming narrower. Further, there is a strong ethical argument for minimising recruitment, because participants receiving active treatment may develop resistance to fusion inhibitors, impacting the future use of this class of drugs for their treatment. Taking these considerations into account, along with the costs of the drug and running this type of study, a maximum of 42 participants will be enrolled across the two‐study stages. This number is considered sufficient to detect any common adverse events and to provide a useful amount of information on the PK profile.50(IC50 = 0.0025μg/mL) at the furthest time point after administration will be determined. We will refer to this dose as the ‘target dose’, and this will be the dose used in stage 2. A maximum of 28 participants will be enrolled in stage 1, with up to 20 participants receiving active drug and 8 receiving placebo. The use of placebo was to tease out drug‐related side effects, and does not impact on the statistical design, as the data for those patients is not pertinent, so we do not consider it further within this paper.In the first stage, the safety, tolerability and the PK profile of C34 will be assessed following administration of a single dose of C34. Additionally, the lowest dose which achieves concentrations 10 times above the ICThe aim of stage 2 is to assess the safety, tolerability and the PK profile of C34 following administration of multiple doses of C34. The dosing interval and dose to be administered will be determined upon review of safety and PK parameters from stage 1. A total of 14 participants will be enrolled, 11 receiving the active drug and 3 receiving placebo. Stage 2 will not be considered further in this paper, as the details of its design are dependent upon the outcome of stage 1.3In the original funding application to the MRC, a dose‐ascending, rule‐based design was proposed for stage 1, consisting of four dose groups of seven participants, five receiving active drug and two receiving placebo according to random allocation, with each dose escalation subject to a satisfactory safety review of the results from the preceding dose group. These doses were chosen to include a lowest dose thought to be sub‐therapeutic, two middle doses thought to be around the range where efficacy may be seen and lastly the maximum dose that can be practically administered.However, the application also stated that ‘prior to protocol submission to Ethics Committees further work will be performed … to simulate alternative approaches which may lead to more insightful understanding of the final data. For example, methods to characterise the dose‐response relationship will be assessed’. The intention was to use an adaptive design instead, provided this was likely to make more efficient use of the 20 subjects given active drug. Crucially, a request for statistical support to carry out simulation work to finalise the study design was included in the application.The objective of the further design work was to explore whether the relevant parts of the dose‐response relationship could be more accurately characterised using an alternative design. The four dose group design was included in the simulations for benchmarking, with the doses specified as 10, 20, 40 and 80 mg. The maximum single dose we can administer in a reasonable volume is 80 mg. We will refer to this design as the ‘5+5+5+5’ design. The sample size was assumed fixed at 20 participants on active treatment as specified in the MRC funding application, and the implications of changing this were not investigated.3.1A drawback of this design is that the doses are fixed in advance, and so no use is made of the accruing information as the trial progresses. As this is a first in man study, there is high uncertainty about the true dose‐response curve, and hence with this initial design, we may end up gathering much information on part of the curve that is not of particular interest. The trial's protocol development team, which included clinicians, statisticians, project managers and patient representatives , decided to develop an adaptive design that would enable them to maximise the relevant information gathered from a limited number of participants.4To utilise the best features of an adaptive design without compromising safety, the trial team agreed that their alternative design would comprise three study periods: (i) an initial dose escalation and safety phase; (ii) an adaptive dose phase; and (iii) an optional final cohort phase. Period 1 will be similar to the 5+5+5+5 design, using the same doses but randomising two rather than five participants to the active treatment. All the administration of placebo will occur in this first period, leaving a further 12 participants to be administered active drug. Subject to the dose escalation phase indicating that the proposed dose range is safe, in period 2, 10 of the remaining participants will be allocated one of the doses under consideration, taking account of results from all previous cohorts. In period 3, optionally the remaining two participants will be given the dose selected at the end of period 2 for the multiple‐dosing stage, to allow improved estimation of the PK profile. Whether period 3 takes place will be determined by the Trial Steering Committee, with advice from the Independent Data Monitoring Committee (IDMC).The benefits of the multiple periods are various. Not only does the first period enable the safety of the proposed drug range to be checked, it also mitigates the detrimental effects of an early unexpected outcome on the performance of the subsequent adaptive periodThe design of period 1 is rule‐based and well defined, so the focus of debate for the trial team centred on the specification of the adaptive part, period 2. As discussed in the introduction, adaptive designs for phase I clinical trials typically use some variant of the CRM, which models a dose‐response curve assumed to be monotonic. Whereas in cancer trials, the relationship between dose and toxicity is modelled; for the C34 trial, we are interested in the probability of inefficacy given dose. Hence, a CRM will be used to model a dose‐inefficacy curve rather than a dose‐toxicity curve . The TTL is typically set at levels in the range 20–30% for cancer trials. The choice of TIL is key in determining the dose selected for each cohort during the adaptive phase. One of the motivations for using an adaptive rather than standard design was to dose more participants at or close to the dose that will be taken forward to stage 2 of the trial. Hence, the requirement was to define a TIL such that the right tail of the dose‐inefficacy curve is adequately explored. It was decided to assess three options through simulation: TIL = 5%, 10% and 20% .4.3This question feeds directly into the selection of the test scenarios, which are required for assessing the performance of the proposed designs Section . Howevery. For participant i, yi is set to 1 if they experience inefficacy and 0 otherwise. This response is modelled using the Bernoulli distribution, yi ∼ Bernoulli(pi), where pi is the probability that participant i experiences inefficacy. This probability is dependent upon dose, so to complete the model specification, we need to choose a suitable family of dose‐response curves.The CRM models a binary response, which we denote by In theory, the choice of the functional form of the dose‐response curve is quite wide, but in practice, only a limited number of one‐parameter and two‐parameter models have been used: the hyperbolic tangent model, the power model, the one‐parameter logistic model and the two‐parameter logistic model. One‐parameter models require less information to estimate, but may not accurately depict the entire dose‐response curve. They are, however, adequate for estimating the part of the curve around the target dose α, set to 1, 3 or 5) and a set of power curves. The shape of the logistic models better reflected the clinicians expectations than the power curves. The chosen value of α in the logistic model determines the position of the intercept (probability of inefficacy when no drug is given). If no drug is given, then the patient must experience inefficacy. To ensure the probability of inefficacy at dose 0 mg is close to 1, the one‐parameter logistic model with α=5 was chosen, and pi is modelled using logit(pi) = 5 − βdi, where di is the dose given to participant i.Graphs showing families of curves were prepared and shown to the trial team. These included three variants of the one‐parameter logistic model ). This is an inexact science, but plugging the probabilities from Scenarios 1–3 into this formula suggests that dividing the actual doses by 10 will map them to a sensible scale, and this is the mapping that underpins the simulation work . Scenario 2 has a slightly higher target dose, which has not been selected for use in the safety phase. Scenario 3 tests the performance of the proposed model when the target dose is at the upper boundary of the available doses. Scenarios 4 and 5 are designed as ‘extreme’ scenarios, with Scenario 4 having no effective doses, and conversely for Scenario 5 all doses have inefficacy at 5% or below and so are considered effective.Scenarios 6 and 7 are the ‘unexpected’ scenarios, which are considered unlikely to occur. They are included to test the performance of the trial design if the shape of the dose‐inefficacy curve is unusual and does not approximate the shape of a logistic curve. Scenario 6 has a much shallower dose‐inefficacy curve than the other scenarios, with the same target dose as Scenario 2. Scenario 7 follows a reverse ‘J’ shape, mimicking Scenario 1 exactly for the first four doses before turning upwards. This type of shape could occur if the drug unexpectedly auto‐induces, giving rise to a situation where the probability of inefficacy initially decreases, reaching a tolerable rate before rising again. Scenario 7 is not thought at all likely to occur but is a good check of model robustness. Figure 6Simulations were run and were gradually refined as decisions on various features of the design and test scenarios agreed. For example, before the cohort size decision was taken, some early simulations compared using cohort sizes of both one and two. Participants on placebo were ignored for the purposes of these simulations. In this section, we describe the final set of simulations. At this stage, the two main outstanding questions concerned the most appropriate value of the TIL and how to select the dose to take forward to stage 2.6.15% TIL, 10% TIL and 20% TIL, respectively. For each scenario, 1000 realisations of the C34 trial were generated using each design. The 1000 realisations for one adaptive design for one scenario took approximately 10 h to complete on a laptop computer with an Intel(R) Core(TM) i7‐2620 CPU @ 2.70 GHz processor and 8 GB of RAM.The final set of simulations were run to investigate and compare the performance of the 5+5+5+5 design and three variants of the adaptive design with the TIL set to 5%, 10% and 20%, which we call Rule‐based criteria: select the lowest dose administered during periods 1 and 2, such that there are no failures at this dose or any higher doses. Model‐based criteria: select the lowest dose with probability of inefficacy below the TIL. A rule‐based approach to selecting the dose to take forward to stage 2 was implemented for all four designs. This approach closely aligns with the original thinking of the clinicians but is inconsistent with the use of a model to select doses during the adaptive phase. Hence for the adaptive designs, a model‐based approach was implemented as an alternative. The two approaches are defined as follows:In reporting the simulation results, the words ‘rule’ and ‘model’ are used to indicate which criteria was used.6.2r software, version 3.0.1 et al.The algorithm to run the simulations was implemented using the 5% TIL design are shown graphically in Figure To demonstrate how the adaptive designs work, the first three realisations of the Scenario 1 simulation using the The positions of the first eight participants are always the same, as specified for period 1; however, the dose level of subsequent participants varies dependent upon the previous results. For the first realisation (left plot) where there were two ineffective responses at both 10 and 20 mg, but two effective responses at both 40 and 80 mg, participants 9 and 10 are given a 50 mg dose. However, for the second and third realisations (centre and right plots), which differ in having one effective response at 20 mg, these participants are allocated the 40 mg dose. The dose suggested for stage 2 is the same regardless of the basis of the decision for the first two realisations, but is different for the third realisation, reflecting the requirement of the rule‐based criteria to choose only doses which have been previously allocated.5% TIL simulations for Scenario 1 as an example. The plots all show the dose‐inefficacy curve as a solid line, with dashed vertical lines indicating the 90% credible intervals for the probability of inefficacy at the possible doses. The dose‐inefficacy curve is calculated using the median of the posterior distribution of β. From the left plot, calculated before the trial begins, we see that the prior is vague as intended, with the 90% credible intervals for most doses covering the whole probability range from 0 to 1. As the trial proceeds, and data from the participants are combined with the prior, the position of the dose‐inefficacy curve changes, and the 90% credible intervals shrink (the positions after period 1 and at the end of period 2 are shown). From the posterior distribution of the probability of inefficacy of the chosen dose, based on the vague prior and data from 18 participants, there is a 90% chance that the probability of inefficacy for the 40 mg dose lies between 0% and 21% , and its most likely probability of inefficacy is 4% (based on the median of the posterior distribution).The reduction in the uncertainty about the dose‐inefficacy curve as a trial progresses is demonstrated in Figure 6.3maximising the probability of selecting the target or higher dose to take forward;minimising the probability of wrongly rejecting all doses and not proceeding to stage 2;treating enough patients at the dose selected to take forward to allow an adequate PK analysis at this dose level.The concerns of the HIV clinicians for this trial may differ from those often addressed in phase I studies in other disease areas. For instance, in cancer trials, not only is it important to correctly identify the MTD but also to minimise the number of subjects who are given a sub‐therapeutic drug dose or experience a DLT. In this trial, toxicity is not expected to be a major issue nor is under‐dosing a concern in this context. However, adverse events will be monitored and carefully reviewed by the IDMC . Indeed, any safety issues encountered are likely to be trial limiting and can override the trial design; while safety is a deal breaker here, the trial was planned on the working assumption that significant safety issues were unlikely to arise. For this reason, we formally concentrate on the PK and the priorities are the following:As a result, the set of operating characteristics of interest is somewhat different to the cancer setting.The probability of selecting each dose (or no dose) for stage 2;The number of subjects allocated each dose;The number of observed ineffective events;The number of subjects treated at doses below the target dose.Using simulation, we assess the following: For each trial design, these measures are presented as means calculated over that design's set of realisations. We focus primarily on the first of these, also paying attention to the second. Operating characteristics 3 and 4 are of little interest in the context of the C34 trial but are included for completeness as they could be important for later phase HIV trials and in other therapeutic areas. A detailed table for each scenario, providing information about all four of these operating characteristics, was produced. Table SM1:Probability of selecting the target dose or any higher dose;SM2:Probability of selecting the target dose or a dose 10 mg higher;SM3:Probability of selecting the target dose;SM4:Probability of selecting the target dose or a dose 10 mg higher or lower.However, the detailed information in these tables is difficult to absorb. This led to discussion within the trial team about the best way to summarise these operating characteristics using a clear pictorial representation. Focussing on the first operating characteristic, the clinical team were primarily interested in the probability of choosing a ‘good dose’ when there is a target dose or the probability of not selecting any dose when there is no target dose. Four summary measures were defined using different definitions of a ‘good dose’: The trial team concluded that the most helpful presentation of these summary measures was a direct comparison of the adaptive designs with the 5+5+5+5 design Figure . In thisIn choosing a design for this study, the prime consideration is selecting a dose to take forward to the multiple‐dosing stage which maximises the chance of success. As such, choosing a dose that is lower than the target dose would not be acceptable to the clinicians, and hence the first summary measure is considered the most important.dt is the target dose divided by 10, and p(dt) is the probability of selecting the target dose. For example, if the target dose is 60 mg, then this measure will be calculated asAdditionally, we calculated a weighted summary measure (WSM), based on SM1, which increasingly down‐weights the probability of choosing a dose as its distance from the target dose increases and penalises for failing to select any dose. Specifically, when there is a target dose, WSM is calculated as5% TIL and 10% TIL adaptive designs allocate more participants to the doses of interest than the 5+5+5+5.Figure 6.4Looking at the different summary measures across the seven scenarios, it is evident that no one design is the clear ‘winner’, rather the ‘best’ performing design varies according to the scenario and operating characteristics considered. However, a few overall findings emerge. Compared with the 5+5+5+5 design, the adaptive designs treat less participants at the lowest doses (10 and 20 mg) when the target dose is at a higher level. This is less ‘wasteful’ of participants because more PK information can be obtained in the part of the dose‐inefficacy curve of interest. In terms of selecting a ‘good’ dose, the 5+5+5+5 design does better than all the adaptive designs when the target dose is at the upper boundary, that is the maximum dose level, regardless of the definition of a ‘good’ dose (Scenario 3). However, when the target dose is not one of the doses administered in the 5+5+5+5 design, the adaptive designs do better unless equal credit is given to selecting the target dose and any higher dose (Scenario 2). For a non‐boundary target dose included in the 5+5+5+5 design, the differences between the two options are smaller, the better option being more sensitive to the chosen summary measure (Scenario 1).5% TIL and 10% TIL. In the more challenging Scenario 6, apart from for SM1, the adaptive designs outperform the 5+5+5+5 design. No design does well for Scenario 7, hardly surprising because it conflicts with the design assumptions.Turning attention to the less likely scenarios, all the designs correctly selected no dose for Scenario 4 a very high proportion of the time. For Scenario 5, where the target dose is at the lower boundary, using the rule‐based criteria, the 5+5+5+5 and the adaptive designs select the target dose just over 50% of the time. The adaptive designs treat a lot of participants at 20 mg, and using the model‐based criteria select the 20 mg to take forward with high probability, particularly for 5% TIL design generally outperforms its equivalent 10% TIL and 20% TIL designs, and the 10% TIL design usually does better than its equivalent 20% TIL design. The situation is rather less clear‐cut and is scenario dependent for the other summary measures.Comparing the three adaptive designs for SM1 visually , the 5% TIL and 10% TIL designs, the model‐based criteria does at least almost as well as the rule‐based criteria. For the 20% TIL, there are more instances of the rule‐based criteria doing best, particularly for Scenario 3. However, when the trial is run on real participants, both criteria can feed into the decision about the dose to be used in stage 2 of the study, as well as the detailed PK analysis.Again, no clear‐cut pattern emerges from a visual comparison of the rule‐based versus model‐based criteria : the better option depends on the scenario and summary measure considered as well as which TIL the design uses. In general, for the 5% TIL design for Scenario 1 four times. The range of the summary measures across the five runs was at most 5%, with a slightly larger range for the weighted summary measure which is on a different scale.Ideally, our analysis of each design would be based on more than 1000 realisations, but running time was a constraint. The uncertainty in the results was assessed by repeating the simulation for the 7Advantage 1:5% TIL and 10% TIL designs).More likely to select appropriate dose for schedules not covered by the 5+5+5+5 design .More subjects administered optimal dose in settings where optimal dose is covered by the 5+5+5+5 design .More subjects administered the optimal dose where the maximum dose is the optimal dose , in this situation, the study team are likely to override the adaptive design . Overriding study design would not occur with 5+5+5+5 design.Neutral:Very little difference in dose selected between designs for scenarios where the optimal dose is covered by the 5+5+5+5 design (Scenario 1).Disadvantage:Less likely to select a dose where the maximum dose is the optimal dose (Scenario 3). However, this disadvantage may be mitigated by advantages 3 and 4.The results from Section On the basis of the aforementioned clinical interpretations of the results presented in Section 8In this paper, we have outlined the steps needed to design an adaptive study in a novel context and applied them to design a study of a new antiretroviral compound. We paralleled a conventional CRM, developing analogous terminology to allow the parallels to be clearly seen. In this final section, we reflect on some of the important decisions required in this process, tease out some keys messages to different trial team members and comment on possible avenues of future research before concluding.8.1In Section 8.1.1A clear understanding of the endpoint driving the study was crucial to making progress, as it impacted the answers to all the other questions. Although ‘inefficacy’ was the intended endpoint, its precise definition was not obvious, as discussed in Section 8.1.250 at the furthest time point after administration. The clinical inclination was that this should be true for all patients, that is the probability of inefficacy should be 0%. However, working with 0% is not compatible with using a logistic model. The target dose is to be used for the follow‐up multiple‐dosing stage of this trial. For the purpose of setting up the test scenarios, we defined the target dose to be the lowest dose with probability ⩽5%. Given this is chosen from a discrete set of doses at 10 mg intervals, for the simulations, this will result in a target dose with a very low probability of inefficacy, which was considered reasonable for simulation purposes. There was discussion about how the target dose would be identified at the end of each realisation of the simulations, revolving around the pros and cons of using a rule‐based versus a model‐based criteria. Hence, the results of the simulations were reported using both criteria. Ultimately, we realised that this discussion was somewhat redundant, as both types of criteria could feed into the decision when the trial is run for real, along with more detailed information from a full PK analysis.Recall that we defined the target dose as the lowest dose which achieves concentrations 10 times above the ICThe choice of the TIL, which forms part of the specification of the CRM is a separate issue to the method of identification of the dose for the multi‐dosing stage, although it is clearly related. The key requirement in setting the TIL is to generate suitable information for making a decision about the dose for stage 2, that is to ensure that the right tail of the dose‐inefficacy curve is adequately explored. The choice impacts directly on the dose selected for the next cohort during stage 1, and because we wish to explore doses in the vicinity of the target dose, we select the dose with probability of inefficacy closest to, rather than below, the TIL. The final decision about the value of the TIL was based on simulation results which pointed to the 5% TIL, consistent with the 5% cut‐off used for selecting the target dose in the test scenarios. In fact, there was not very much difference between the 5% and 10% options, as the use of a discrete set of doses means that the chosen dose is often identical.The simulation process brought home the interdependence of these two decisions. An interesting point emerged when considering Scenario 3, where the target dose was the maximum. Here, the 5% TIL resulted in more participants being administered the top dose than the other designs under consideration. While this provides more PK information for this dose, it also increases the probability of at least one participant experiencing an inefficacy and the target dose identification criteria suggesting there is no viable dose.8.1.3Other features of the CRM design that needed to be chosen included the functional form, dose levels and cohort size. Besides these, there were a lot of decisions which were presented to the trial team but were beyond the scope of this paper. Model variants we decided not to pursue but were incorporated into interim sets of simulations, included using a two‐parameter logistic model as the functional form , different sets of dose levels (adjusted to comply with maximum reasonably deliverable dose) and a cohort size of one . The scenarios used to test the different CRM variants were also subject to continual revision, as they needed to take account of almost all the clinical inputs and practical constraints.8.2The development of the C34 trial design resulted from close collaboration between all the members of the trial team. This paper is largely written from the statisticians' perspective, however the statisticians' could not have developed the proposed design in isolation but were dependent on input from the other team members throughout the process.8.2.1From the statisticians' viewpoint, the key was properly understanding the drivers of the study. Simulation skills were required, but the research could be carried out using standard tools available in free software. To maximise progress at the trial group meetings, great care was taken in the presentation of the latest simulations to the other group members. This worked best by synthesising the material onto a set of slides, starting with a recap of what had previously been agreed to make sure all team members were up to date. Results of the latest simulations were then shown, highlighting new elements. Graphical presentations were most easily understood and benefited greatly from feedback from the non‐statisticians in the team. The final part of the statisticians' presentation was a series of questions about outstanding issues. This led to discussion about the next steps required. We took the sample size as given, and graphs such as Figure As pointed out by a reviewer, a CRM‐type design can focus participants on a single dose to the exclusion of generating safety and PK data across the range of doses under investigation. To mitigate this, we have chosen a ‘hybrid’ design, which incorporates a dose‐ascending phase to generate some data over the entire dose range followed by a CRM which will focus around the most promising dose. We believe this flexibility is key to enabling best use of the limited number of participants available for this trial.8.2.2An important message to clinicians is that they need to fully engage in the process of developing a trial design and cannot just delegate this to a statistician. The study design links to more elements of the trial protocol than just the sample size calculation. They need to be able to provide clarity on key issues such as endpoints and be prepared to challenge the statisticians. Initially, the non‐statisticians were keen to use hard rules to select the next drug dose but over time as successive sets of simulation results were explained, developed an understanding of the advantages of using probabilistic models to make the decision instead. Importantly, they accepted that the statistical models incorporated uncertainty and could not guarantee a correct answer. This led to a lot of thinking and discussion about what types of mistakes they could live with and what types they found totally intolerable. Ultimately, the fine‐tuning of the model was a result of a series of trade‐offs.8.2.3The whole process of developing the adaptive design and agreeing its final detail was time consuming, taking over a year in elapsed time. It also required a considerable time commitment from the whole trial team in terms of attending regular meetings, which were held on an approximately monthly basis. In between these meetings, simulations were run and analysed by the team's statisticians. This level of resource allocation was made possible by the MRC, who allowed the cost of the workup of the design to be written into the grant. Allowing sufficient time and including adequate financial and personnel resources at the funding and planning stage were imperative for the design development. This aspect of trial workup was carried out in parallel with the development of other aspects of the protocol, design of the data collection system and associated animal toxicology studies. However, as expertise is built, this type of trial design could be worked up in a shorter time frame if necessary, but this would require careful planning.8.3Clinically, the next steps are implementing the identified adaptive design and running the trial, which will in turn inform the design of future trials. Statistically, a useful follow‐up would be to try these steps in designing future trials, to develop guidance such as checklists. Further aspects could be explored by additional simulation, for example the implication of different dose spacings, other dose level mappings and alternative priors. The implications of having fuller PK data available in real time and how this could be incorporated into the modelling, specifically through adapting the definition of the endpoint, could be assessed. We could consider refinement of the methods of presenting simulation results to clarify their implications. Further, where there is flexibility in the choice of the sample size, modifications could be made to the simulation process.8.4While much of the detail we have described is specific to this trial, many of the general principles followed are widely applicable. In going through this process ourselves, we have identified some steps which will be helpful to teams working up other early phase clinical trials. In particular, we stress the importance of good communication between all members of a trial team, and how developing the trial design is integral to the whole trial workup and should not be carried out in isolation.The development of the chosen adaptive design involved much work and commitment, but we consider the effort justified. In this trial, an untried drug is to be given to individuals already diagnosed with a potentially life‐threatening disease, and we owe it to the participants to maximise the scientific information gained from the study. While we can rerun simulations many times if the design is not quite right, we do not want to be rerunning such a study on live patients.Supporting info itemClick here for additional data file.


X and Y dimensions of the light field measured with film differed by less than or equal to 1.4 mm from the digital collimator settings, whereas the values extracted from the EPIs differed by up to 2.5 mm. Based on these findings, EPIs were found to be a quick and reliable alternative to film for qualitative and relative analyses.A method to investigate light and radiation field congruence utilizing a commercially available amorphous silicon electronic portal imaging device (EPID) was developed. This method employed an EPID, the associated EPI software, and a diamond‐shaped template. The template was constructed from a block tray in which Sn/Pb wires, 1 mm in diameter, were embedded into a diamond shaped groove milled into the tray. The collimator jaws of the linac were aligned such that the light field fell directly on the corners of the diamond. A radiation detection algorithm within the EPI software determined the extent of the radiation field. The light and radiation field congruence was evaluated by comparing the vertexes of the diamond reference structure to the detected radiation field. In addition, the digital jaw settings were recorded and later compared to the light field detected on the films and EPIs. Three linear accelerators were tracked for a period ranging from 2–8 months. Light radiation field congruence tests with films and EPIs were comparable, yielding a difference of less than 0.6 mm, well within the allowed 2‐mm tolerance. A disparity was observed in the magnitude of the detected light field. The PACS number(s): 87.53.Xd, 87.56.Fc, 87.53.Oq, 87.52.–g, 87.53.–j Several authors have suggested the use of electronic portal imaging devices (EPIDs) to facilitate this procedure.6.8×6.8 cm2 in dimension, rotated by 45°, onto the surface of a block tray  and high‐energy (10 or 18 MV) x‐ray beams. Maintaining the jaw settings, gantry and collimator angle, a sheet of Kodak X‐Omat TL film was positioned 100 cm from the source over 5 cm of solid water backscatter material on the treatment couch. Although the diamond template was mounted in the block tray, the light field was determined by scribing the projected light field directly on the film jacket, as is done in standard practice. One centimeter of solid water build up was placed over the film and the field was irradiated with a 6X beam several minutes after the EPIs were acquired with a dose of 12 MU. A second film was acquired with a high‐energy x‐ray beam (10 or 18 MV). For each measurement, the digital collimator settings were recorded and later compared with the detected light field on the films and EPIs.An EPID‐based light and radiation field congruence procedure was examined that utilized a commercially available EPID and the accompanying software, PortalVision™ 6.1. . All tests were performed with a gantry and collimator angle of 0°. To define the light field, a diamond‐shaped structure was embedded within a block tray. The diamond template was created by milling a square X and Y. The light field size was compared to the absolute field size, as defined by the recorded digital collimator jaws.The electronic portal images were analyzed with the associated EPI software. To analyze each image, a previously acquired image was stored as a reference. A diamond structure was drawn over the shadow cast by the block tray. The reference structure was aligned such that each of its sides overlaid the center of the diamond‐shaped image. Latter EPIs were compared to the reference image by overlaying the corresponding reference structure over the center of the diamond on the newly acquired EPI see Fig. . The verThe films were analyzed via visible inspection and considered the “gold standard” to compare the EPI results. The light field was defined as the distance between the two parallel marks drawn on the film. Similar to the EPIs, the light field size was compared with the absolute field size.X and Y for low and high‐energy photon beams.This procedure was repeated on three machines to determine whether the linac‐EPID combination influenced the results. Weekly portal images were acquired on a Varian Clinac 2100C without a multileaf collimator system and a Varian Clinac 21EX equipped with a Millennium MLC‐120, for a two month period, and a Varian Clinic 2100C equipped with a Mk 2 MLC‐80 for a total of eight months. The sensitivity of the EPID to detect small changes in the radiation field was also tested by intentionally applying a 1‐mm shift in X) and in‐plane (ΔY) deviations. Based on these results, no appreciable difference was observed in light and radiation field congruence between the conventional and the EPID‐based technique discussed in this study . A systematic error of less than or equal to 0.6 mm has been calculated for the cross and in‐plane discrepancies. In addition, the low‐energy (6 MV) and high‐energy (10 or 18 MV) results compare well , suggesting that light radiation field congruence is independent of the energy of the incident photons.Three observers independently measured light and radiation field congruence with both film and EPIs for one of the three linacs under study. The results were recorded and monitored for a period ranging from 2–8 months. A total of 67 EPIs and 67 films were analyzed. At the completion of the study, the systematic and random errors for each method were calculated and compared. The deviations along each side of the projected field are summarized in Table I for each machine. In addition, results from opposing field sides were combined to yield the overall cross , again suggesting that the results are independent of the energy of the irradiating beam.A comparison of the absolute field size as defined by the digital collimator jaw settings and the light field detected on the films and EPIs is presented in Table II. Based on these results, the size of the detected light field differs depending on the QA technique utilized. The film measurements are in better agreement with the digital jaw settings, yielding a difference of X and Y was applied to the collimator jaws. After measuring and comparing the detected radiation field size, a 1‐mm difference was observed in X and Y between the two portal images.The EPID was also found to be sensitive to small changes made to the size of the radiation field. Portal images were acquired before and after an intentional 1‐mm shift in p–value>0.2 for all three machines). As the diamond template easily mounts into a block tray holder, it may be used to test light radiation field congruence at any gantry angle. Factoring in the time required to setup and process the film, and the expense of the films and processing chemicals, substituting EPIs for films will prove to be more labor and cost‐effective at facilities already equipped with EPIDs.The EPID‐based quality assurance technique discussed in this study is a relatively fast and reliable method for checking light radiation congruence. The total time to acquire and analyze a port is on the order of 1.5 min. The results presented in Table I suggest that the EPID‐based technique is comparable to film, reporting discrepancies on the order of a fraction of a millimeter (Y) for machine A. However, the EPID measurements were offset by up to an additional 1.5 mm. This is in agreement with Dunscombe et al.X and Y dimensions. This effect would not be observed when testing light and radiation field congruence because the jaws are set to cast shadows congruent with the vertexes of the diamond template on the surface of the detector and so both light and radiation fields are magnified by an equivalent factor. However, inaccurate positioning of the EPID becomes apparent with analysis of absolute field size.A difference was observed in the measured size of the light field between films and EPIs. The size of the light field measured with film was comparable to the digital collimator settings. At most, a 1.4 mm discrepancy was observed in‐plane (ΔIn this study, the use of an EPID for light and radiation field congruence tests was examined. The results indicate that the EPID‐based procedure is comparable to film, the “gold standard.” Both the conventional and EPID‐based light and radiation field congruence techniques have been shown to be sensitive to below the allowed 2 mm or 1% discrepancy on each side of the square field.et al.,A difference has been observed in the measured dimensions of the light field between film and EPIs. When comparing the field size to the digital collimator settings, the film measurements are in better agreement than the EPIs. This discrepancy may be attributed to a vertical misalignment of the detector, which can adversely affect the scaling of the acquired portal image. As suggested by Dunscombe This work was supported in part by Varian Medical Systems.


We report a new scheme of realizing terahertz fingerprint detection with ultrahigh sensitivity. Instead of using the direct absorption of terahertz through a bare sample in the regular transmission scheme, a cavity mode resonating at the characteristic frequency of the sample is used and due to the high dependence of the cavity mode transmission on the material loss, an amplified transmission decaying is observed when the sample is loaded into the cavity. Furthermore, this scheme retains the feature of substance identification. A one-dimensional photonic crystal cavity is used as the example for the detection of α-lactose and an efficient detection of 10 nm α-lactose can be achieved, which corresponds to 1/57000 of the free space wavelength at the characteristic frequency of 0.529 THz, exhibiting a sensitivity over 400 times higher than the regular transmission method. Especially, many chemicals and molecules have their characteristic absorption frequencies located in the terahertz regime resulting from their rotation, intra- and inter-molecular vibrations, suggesting that terahertz technology can work as a unique tool for chemical identification6. This technique, well-known as fingerprint detection, lays the foundation for many applications of terahertz in security checking (like drugs and explosives), biomedical diagnosis and pharmaceutic industry. However, due to the high contrast between the nanometer-scale size of most molecules and terahertz wavelengths (from tens to a few hundreds of microns), the interactions between molecules and THz radiations are extremely weak and a large volume of chemicals are usually required to have an observable absorption for identification. For example, in the mostly-used transmission scheme for THz fingerprint detection, the sample is normally made into powder and then compressed into pellets with a thickness of several millimeters7. There are many circumstances in which the sample is not abundant or it is required to use as few sample as possible, e.g. in medical diagnosis. Thus, a substantial improvement of the sensitivity in the THz fingerprint detection is still necessary to further broaden this technique in practical applications. Many approaches have been attempted in this respect, for example to use resonating antennas, metamaterials or InSb-based plasmonic gratings11 in the transmission mode to enhance the local electric field so that the absorption cross section of the chemicals can be amplified. Other examples include the exploration of new working principles like hybridization induced transparency12 or using waveguiding structures15 to increase the interaction length between molecules and terahertz radiations for a fixed amount of sample. However, with all these reported approaches, to get an observable dependence of the transmission at the characteristic frequencies on the sample amount, the required sample thickness is on the order of several micrometers11.Terahertz  radiations have many advantages over the electromagnetic waves in other frequencies due to their favorable properties like transparency in most dielectrics, low photon energy and nonionizing features, which may promise many potential applications ranging from fundamental sciences to practical applications16. Inspired by this picture, we conceived an idea of using a similar structure but with the reverse process for THz fingerprint detection with ultrahigh sensitivity. The detection scheme is schematically shown in Fig. f0 which matches one of the characteristic frequencies of the target sample. When the sample is absent, the transmission through the cavity structure will exhibit a peak at f0. If a thin target sample is present, due to the intrinsic absorption of the sample at f0, the transmission will experience a drop and the resonance frequency will remain roughly at f0 due to the little optical path length change from the thin sample. This drop, however, will be much higher than the transmission through a bare sample due to the cavity effect. When another sample with an absorption frequency away from f0 is considered, it works as a dielectric with small or negligible loss around f0. So, when it is loaded into the cavity, the resonance will be spectrally shifted but its transmission won’t be significantly affected. One can see that using this scheme the sample can be identified while its amount can be measured with high sensitivity as well. The high dependence of the cavity mode transmission on the sample amount makes it possible to realize the quantitative detection easily.In this paper, we propose a novel idea of realizing THz fingerprint detection with an ultrahigh sensitivity and the sample as thin as a few tens of nanometers can be easily and steadily captured. Basically, to realize the functionality of fingerprint detection in the regular transmission scheme using a THz spectroscopy, one needs the information for two features. One is the characteristic frequencies which are the spectral signatures of a certain sample, manifesting them as resonances in the transmission spectrum; the other is the change of the transmittance at these frequencies  which implies the amount of samples included in the measurement. It is well-known in the weak-coupling regime that when an emitter is placed into a man-made cavity structure which has a designed resonance frequency corresponding to the energy difference between the ground and excited states of the emitter, as shown in Fig. k(ω) is the imaginary part of the sample refractive index, which reaches its maximum at the sample characteristic frequencies, L is the sample thickness and λ is the free space wavelengths. Due to large values of λ in the terahertz band, L must be large to achieve an observable decrease in T at resonance. However, when the sample is located in a cavity resonating at the sample characteristic frequency, the THz radiation at resonance will bounce back and force for multiple times, interacting with the sample and resulting in an effective sample thickness Leff much larger than the original L.One can also interpretate the working principal using another way. One knows that the power transmission through a sample can be calculated using the following equation:17. As shown schematically in Fig. dc) determines the numbers and frequencies of defect modes in this 1D photonic crystal cavity17. For simplicity, we selected a length of this defect cavity (dc) being 268 μm, which combing with other parameter, results in a transmission peak around 0.529 THz. This frequency is a signature of α-lactose, which will be used as the sample representative for the fingerprint detection in this paper. The thickness of the silicon layers is t = 100 μm and air layer thickness between them w = 233 μm. The transmission characteristics of this photonic crystal cavity are numerically simulated with the finite element method (FEM). In the calculations, the incident THz plane wave is set be normal to the structure with electric field along the y direction. The calculated transmission spectrum of the bare photonic crystal cavity is shown in Fig. In the following part, we demonstrate the capability of the proposed scheme for THz fingerprint detection using a one-dimensional (1D) photonic crystal (PC) cavity structures as an example. Composed of Si stacks, these structures have been experimentally investigated by us and the measured transmission spectrums demonstrate cavity resonances with high quality factors18:11 rad s−1 and 19. When the α-lactose layer as thin as 0.05 μm is loaded on the left side of the Teflon film, one can see from the red line in Fig. When a thin layer of α-lactose is loaded on one side of Teflon film into this cavity as shown in Fig. For comparison, we also plot in Fig. To further demonstrate that our scheme keeps the feature of substance identification similar to the conventional transmission scheme, we replace the α-lactose with another sample which has a different intrinsic absorption frequency. A new value of 2π*0.65 THz which is beyond the bandgap of the cavity is used for In the above calculations, the silicon is assumed to be lossless. For practical applications, even the high-resistivity silicon may exhibit some losses in the terahertz frequencies. Since the material loss is critical in determining the property of a photonic cavity, we further consider the case when the material loss of silicon is taken into account. The calculated transmission spectrum of this bare PC cavity when the imaginary part of silicon permittivity is assumed being 0.001j is shown in Fig. Due to the formation of the standing waves in the cavity, the lateral positions of the loaded α-lactose on Teflon film will also affect the sensitivity. The highest sensitivity will be expected when the sample is loaded at the maximum electric field of the cavity, which is the physical center of the cavity for our investigated defect mode. Figure N = 4 to N = 3 and 5 respectively. The quality factors for the two cases are found to be 270 and 5076 respectively. A larger number of Si stacks will result in the defect mode with a higher quality factor because the leaking of THz radiation through the PC structure is weaker. Figure N = 3, 4, 5 are plotted in Fig. From the results in Fig. As a final remark, we know that the all the sample absorption resonances have certain bandwidths around the central frequencies. For example, the bandwidth for the α-lactose at the resonance of 0.529 THz is 25.3 GHz. For all the above calculations, we assume that the cavity defect mode has a resonance frequency matching 0.529 THz exactly. However, even when there is some mismatch between the cavity defect mode and the characteristic frequency, the proposed scheme still works. Figure Although a simple structure of 1D PC cavity is used in this paper to demonstrate the working principle of our proposed idea to realize terahertz fingerprint detection with unpresented sensitivity, we need to emphasize that the idea can be extended easily to other cavity structures working in the terahertz frequencies, like Si based waveguide ring resonators or photonic crystal cavities. Some on-chip cavity structures can even add more assets because the interaction length between the cavity mode and the sample is further increased.In a summary, we have presented in this paper a new scheme to realize the THz fingerprint detection with ultrahigh sensitivity. Using a resonance mode matching the absorption of α-lactose in a 1D PC cavity to amplify the influence of the α-lactose intrinsic absorption to the change of transmittance at the resonance peak, a transmittance drop of more than a few hundred times can be obtained compared to that through a bare α-lactose layer. With this method, an efficient detection of a thin α-lactose layer with a thickness as small as 10 nm can be easily and steadily achieved. This thickness corresponds to only 1/57000 of the free space wavelength at the characteristic frequency of 0.529 THz. A steady identification of such a thin layer of sample makes it possible to realize the recognition of a monolayer of molecules, suggesting huge potential for terahertz application in medical diagnosis. The proposed design can be easily extended to other substances by simply adjusting the cavity parameters to match the absorption frequencies of new materials. This versatility, combined with the ultra-high sensitivity, will make a significant impact on the current terahertz technology. It lays out the foundation for terahertz sensing in circumstances where high sensitivity is essential while still retaining the unique property of characteristic frequency identification. We believe that this technique really broadens the application range for the terahertz technology and paves the way for the further application of terahertz spectroscopy in biomedical area.


Rs and stagnation height hs. The side flow is formed because the material at or under the stagnation region is extruded by the tool edge to flow to the side of the tool edge. Higher stagnation height would increase the size of the side flow. The anisotropic nature of the material which partly determines the stagnation region also influences the side flow due to the different deformation mechanism under the action of the tool edge. At different cutting directions, the size of the side flow has a great difference which would finally affect the machined surface quality. The cutting directions of {100} < 011>, {110} < 001>, and {110} < 1-10 > are beneficial to obtain a better surface quality with small side flow. Besides that, the side flow could be suppressed by reducing the feed and optimizing the cutting tool geometry. Cutting tool with small edge radius, large positive rake angle, and inclination angle would decrease the side flow and consequently improve the machined surface quality.The side flow of material in nano cutting is one of the most important factors that deteriorate the machined surface quality. The effects of the crystallographic orientation, feed, and the cutting tool geometry, including tool edge radius, rake angle and inclination angle, on the side flow are investigated employing molecular dynamics simulation. The results show that the stagnation region is formed in front of tool edge and it is characterized by the stagnation radius  Ultra-precision cutting is one of the most common methods in realizing the nanometric surface roughness and sub-micrometric form accuracy. Based on the assumption that the workpiece material is removed ideally and the machined surface texture is the replication of tool nose profile, the surface roughness could attain nanometric even sub-nanometric scale, if simply decreasing the feed and increasing the tool nose radius. However, it always deviates from its ideal value due to many factors, such as, material properties of workpiece –4 and cuIn nano cutting, the ever-reduced UCT making the material removal at nanoscale which is smaller than the material grain size causing the significant influence of the size effects  and mateBesides that, when the UCT is comparable to the cutting tool edge radius, the effect of tool edge on machined surface quality could no longer be neglected. For instance, the increased tool edge radius would increase the surface roughness , and theIn this study, the effects of the crystallographic orientation and the cutting tool geometry, including tool edge radius, rake angle and inclination angle, on the side flow are investigated employing MD simulation. This study contributes to a better understanding of the surface generation for single crystal materials and polycrystalline materials in nano-cutting.rβ changes from 0 to 7.5 nm and the nose radius Rn stays constant at 15 nm. The rake angle of the cutting tool changes at the range of −30° to 30° and the clearance angle is 12.5°. In addition, the inclination angle of the cutting tool changes from −8° to 20°. The size of workpiece is 45 nm × 38 nm × 20 nm and it containing about 2,000,000 atoms. Part of workpiece is cut off by a cylinder with radius of 15 nm which is the tool nose radius Rn to simulate the former cutting trace left on the workpiece surface. Therefore, the side flow on the nano cutting or turning processes could be investigated efficiently. Atoms of workpiece are defined as three parts: boundary layer, thermostat layer and Newtonian layer. Atoms in boundary layer are fixed at space to prevent the unexpected movement under the action of cutting force. The thermostat layer adjacent to it is kept at a constant temperature of 293 K to imitate the heat dissipation in nano cutting. The rest atoms that would move under the cutting of tool are in the Newtonian layer obeying the Newton’s law.MD simulation is employed to investigate the side flow of aluminum during nano cutting. As shown in Fig. Six cutting directions, including {100} < 001>, {100} < 011>, {110} < 001>, {110} < 1-10>, {111} < 1-10 > and {111} < 11-2>, are considered in investigating the effect of crystallographic orientation on side flow in nano cutting. Depth of cut is set to 5 nm and the feed is change from 1.5 to 15 nm. Therefore, the UCT along the cutting tool edge changes with the different combination of feed and depth of cut. The cutting speed is 100 m/s at the negative x-direction. The cutting distance of the model is about 40 nm. Initial temperature of the cutting model is equal to the constant temperature in thermostat layer.E isThe interaction among the aluminum atoms is described by the embedded atom method (EAM) potential . The tooFi(ρi) is the embedding energy to embed atom i into the electron density ρi, and ϕi,j(rij) is the pair potential energy between atoms i and j. The electron density ρi can be calculated by the following formwhere fj(rij) is the electron density casing by atom j which has a distance of rij to the location of atom i.where The interaction between the carbons atoms is ignored due to the diamond is much harder than aluminum and the diamond tool is thought as rigid. The interaction between the rigid diamond tool and aluminum atoms is depicted by the Morse potential:E is the pair potential energy, D0 is the cohesion energy, α is a constant determined by material properties, r0 is the distance at equilibrium and r is the distance between two atoms.where The MD simulation is based on the LAMMPS and the microstructural evolution of the workpiece under cutting process is analyzed based on common neighbor analysis with software OVITO. The displacement vectors of the workpiece atoms are analyzed to reveal the material side flow mechanism in nano cutting.Rs. The section of the stagnation region has a triangular shape and more atoms would join in and maintain the region with the cutting process. Atoms above the stagnation region tend to form the chip and atoms below the stagnation region are pressed to form the machined surface.Figure Rs for {100} < 011 > cutting direction is larger than that of {100} < 001 > cutting direction in Fig. Rs is larger than that with 5 nm tool edge radius , is used to characterize the position of the stagnation region at the tool edge. As shown in Fig. hs at each cutting direction, as shown in Fig. At the plane C-C, as shown in Fig. Furthermore, the side flow is influenced by the anisotropic nature of single crystal materials. At different cutting directions, the material deforms in different ways under the action of the tool edge. To exhibit the material deformation and side flow in different cutting directions, the atoms displacement vector at the plane A-A see Fig.  sliced wWhen cutting with the {100} < 011 > direction whose side flow is the smallest, atoms do not flow to the side until a small amount of atoms reach the shearing plane. The flow up region in front of the cutting tool is bounded by the shearing plane which expands from the tip of the stagnation region as shown in Fig. The effect of feed on the side flow and the material removal mechanism is investigated by changing the feed from 1.5 to 15 nm. The results shown in Fig. When the feed is smaller than 8 nm, the PV value of all the cutting directions is close to each other. Minimum PV value is not found at which the PV value would increase when further decreases the feed . It is bIt is known that higher stagnation height would increase the size of side flow. The tool edge radius would influence the stagnation height and further affect the side flow. Comparing the Fig. In nano-cutting, large part of material is under the action of the tool edge whose effective rake angle is always negative regardless of the nominal rake angle is positive or not , 15, 25.In order to eliminate the influence of tool edge and only investigate the influence of rake angle on the side flow, the cutting tool with sharp edge and different rake angle, including 30°, 15°, 0°, −15°, and −30°, is introduced to the MD simulation. The results are shown in Fig. In practice, the tool edge radius could not attain 0 nm and maintain sharp at cutting process. Therefore, the inclination angle seems to be a factor to suppress the side flow in nano-cutting. Figure Rs and stagnation height hs. The side flow is formed because the material at or under the stagnation region is extruded by the tool edge to flow to the side of the tool edge. The side flow is influenced by the position of stagnation region. Higher stagnation height would increase the size of the side flow and further deteriorate the machined surface quality.The stagnation region in front of tool edge has been confirmed and is characterized by the stagnation radius The anisotropic nature of the material which partly determines the stagnation region also influence the side flow due to the different deformation mechanism under the action of the tool edge. At different cutting directions, the size of the side flow has great difference which would finally affect the machined surface quality. The cutting directions of {100} < 011>, {110} < 001>, and {110} < 1–10 > are beneficial to obtain a better surface quality.The smaller feed decreases the size of the side flow, and no minimum PV value is found at which the PV value would increase when further decreases the feed. It is because the cutting distance used in this study is relatively small.The side flow can be suppressed by optimizing the cutting tool geometry. Cutting tool with small edge radius, large positive rake angle, and inclination angle would decrease the side flow and improve the machined surface quality.When the UCT is equal to or larger than the cutting tool edge, the material is removed in shearing mechanism. But when decreases the UCT, the material removal is dominated by extrusion mechanism.The effects of the crystallographic orientation and the cutting tool geometry, including tool edge radius, rake angle and inclination angle, on the side flow are investigated employing MD simulations. The conclusions can be drawn as follows:


A dataset range of isocenter congruency verification tests have been examined from a statistical perspective for the purpose of establishing tolerance levels that are meaningful, based on the fundamental limitation of linear accelerator isocentricity and the demands of a high‐precision stereotactic radiosurgery program. Using a laser‐defined isocenter, a total of 149 individual isocenter congruency tests were examined with recorded values for ideal spatial corrections to the isocenter test tool. These spatial corrections were determined from radiation exposures recorded on an electronic portal imaging device (EPID) at various gantry, collimator, and treatment couch combinations. The limitations of establishing an ideal isocenter were quantified from each variable which contributed to uncertainty in isocenter definition. Individual contributors to uncertainty, specifically, daily positioning setup errors, gantry sag, multileaf collimator (MLC) offset, and couch walkout, were isolated from isocenter congruency measurements to determine a clinically meaningful isocenter measurement. Variations in positioning of the test tool constituted, on average, 0.38 mm magnitude of correction. Gantry sag and MLC offset contributed 0.4 and 0.16 mm, respectively. Couch walkout had an average degrading effect to isocenter of 0.72 mm. Considering the magnitude of uncertainty contributed by each uncertainty variable and the nature of their combination, an appropriate schedule action and immediate action level were determined for use in analyzing daily isocenter congruency test results in a stereotactic radiosurgery (SRS) program. The recommendations of this study for this linear accelerator include a schedule action level of 1.25 mm and an immediate action level of 1.50 mm, requiring prompt correction response from clinical medical physicists before SRS or stereotactic body radiosurgery (SBRT) is administered. These absolute values were derived from considering relative data from a specific linear accelerator and, therefore, represent a means by which a numerical quantity can be used as a test threshold with relative specificity to a particular linear accelerator.PACS number: 87.53Ly, 29.20.Ej, 87.56.Fc Stereotactic radiosurgery (SRS), by definition, is the delivery of a large therapeutic dose of radiation to an accurately localized target.The linear accelerator's isocenter is the common point shared between the mechanical axes of the gantry, treatment couch, and collimator with both the treatment beam axis and the imaging isocenter. SRS requires precise alignment of these axes such that the isocenter remains constant while any of these components change position. In 1987 Winston and Lutz described an isocentricity test to establish congruency of these axes.The Winston‐Lutz (WL) test was originally developed to provide QA for a single fraction of cranial SRS using a rigid invasive headframe attached by a neurosurgeon with the patient under local anesthesia. The localization of the target with this technique relies on aligning the lasers to the frame with the assumption that, when treating the patient, the frame has not shifted from simulation due to the pins screwed into the skull. These headframes produce imaging artifacts, and therefore imaging verification of the patient position was not possible. Now radiolucent cranial and body immobilization devices allow photon imaging to verify proper patient position. Therefore, this test has been modified from the use of film with the advent of on‐board imagers as the image acquisition device.Image‐guidance in radiotherapy added tertiary considerations to define a congruent isocenter with the possible inclusions of on‐board megavoltage (MV) and kilovoltage (kV) imaging devices and other ancillary imaging platforms, such as ExacTrac.The present study attempted to formalize the inclusion of imaging components to the Winston‐Lutz test with a statistically driven examination of data points to derive meaningful action limits to the routine performance of this quality assurance test. For the course of just over one calendar year, a total of 149 test results were analyzed, including MLC and cone‐based isocenter alignment examinations.Thresholds have been suggested to be established as being within specification of a baseline value.The Winston‐Lutz test was conducted on a total of 149 measurements obtained over 370 days. Of these datasets, 100 were complete tests which were performed for SRS or SBRT and utilized the treatment table, collimator, and gantry angles . Each enThe linear accelerator used in this study was a Novalis Tx  and BrainLAB AG . This included BrainLAB's ExacTrac system and Varian's On‐Board Imaging (OBI).1.5×1.5 cm2 field size defined by MLCs or a circular field defined by conical collimators at a variety of gantry, couch, and collimator angles with the alignment phantom positioned such that the radiopaque BB would fall within the radiation field with proper alignment. An analysis of each image was performed in DoseLab Pro  wherein the radiation field edge threshold was determined along with the edge of the center object . The centroid of each of these two objects was then determined using a proprietary algorithm reporting accuracy to within a third of a pixel.The alignment phantom was a Winston‐Lutz pointer phantom (BrainLAB) which consisted of crosshairs for laser‐guided positioning with an embedded radiopaque BB (5 mm diameter) at the crosshair‐defined center of the phantom. For MLC‐defined and cone‐based MV Winston‐Lutz images, an exposure was made utilizing a square field defined either by the collimation from a For MLC‐defined spoke shot films, electronic portal imaging device (EPID) images were taken as a series of thin slit fields formed by bringing both banks of MLCs close to each other. Exposures were then made at various collimator angles forming a star pattern. A cumulative image was combined from these fields and analyzed using DoseLab Pro. The gantry position was constant, and there was no phantom intermediate between the gantry head and imaging panel. This analysis was performed by sketching a ray line through the measured center of each slit . The smallest circle that intersected each ray at least once was taken as the quality metric for this test. Numerical assignments for baseline measurements in periodic quality assurance were derived by noting the diameter of this circle.Varian's IsoCal geometric calibration system was applied to images acquired with the MV imagers to compensate for mechanical defections and arm position errors that occurred for the linear accelerator as a function of gantry angle.The Winston‐Lutz test was performed at a frequency and/or preceding radiotherapy treatments prescribed by the protocols established by the treatment team and specific to the nature of the treatments being performed. In addition, daily testing consisted of brief, simplified alignment tests. The patient alignment tools tested depended on the nature of the imaging appropriate for those treatments and included testing of the congruency of the radiation isocenter, laser‐guided positioning system, MV and kV imaging devices, and ExacTrac imaging components by taking simultaneous images of the WL phantom at predetermined gantry positions. In both tests and patient alignment situations, precursor alignment was performed using the in‐room lasers with image‐guidance following. Congruency was verified during testing between all of the appropriate imaging platforms with action levels defined as 0.7 mm for ExacTrac with EPID and 1 mm for kV imaging with EPID. We did not observe misalignment beyond either of these action levels in any of the 149 WL tests. Therefore, we focused on the analysis of EPID‐based images, as these are used to define the laser and WL alignment phantom. We plan to proceed with the analysis of kV and ExacTrac congruency with EPID in future investigations to determine if these action levels are appropriate.t‐test was performed in this study, with a defined significance level of 0.05.A two‐sample assuming unequal variances To determine daily positioning setup error of the isocenter test tool from measurements, each data point was examined as to whether the pointer positioning could have been improved from the imaging results. Using images with complementary information, each spatial dimension was evaluated to determine whether or not the alignment of the test tool could have been improved, with the requirement that all images containing information for the dimension under analysis confirm the improvement. If the images provided an affirmative to this, a daily setup error spatial correction was deduced using the minimum spatial correction for that test as determined from all images contributing information for that dimension. This three‐dimensional spatial correction for each dimension and dataset was then applied to the data to determine the positioning error to establish a so‐called corrected data collection determined from an uncorrected one. The average spatial corrections with the daily setup bias removed from all of the datasets are presented in 0.58±0.21 mm. The sum of the datasets was compiled in a histogram displaying both the results with and without the daily positioning setup error . Followi0.38±0.25 mm.The other component of this investigation allowed for small positioning deviations in the daily positioning of the test tool. After reviewing the device positioning correction per test, the average magnitude with 1 SD of three‐dimensional shift was (average=0.58 mm) needed for each dataset. The upper and lower boundaries are defined by 2 SDs (average=0.42 mm) of the setup error for the daily positioning error unbiased data .−0.22 mm. The midpoint of these two values was 0.17±0.4 mm. The standard deviation of the gantry down data for the 149 datasets was 0.19 mm, while the standard deviation for the gantry up was 0.21 mm.The average gantry 180° shift was 0.57 mm, while the average gantry 0° shift was Two methods of measuring MLC walkout were employed in this study. For the first test, eight exposures were made with only a thin slit (5 mm) of radiation revealed by the MLCs. A line extended through each slit, and a circle was generated. The quantification of this test was in the generation of this circle which had a diameter that was able to touch all of the lines from each spoke for at least one intersection point. The magnitude was measured as 0.16 mm.(n=138) with gantry and couch positions held at values of 0° while image acquisitions were made at collimator angle of 0°, 90°, and 270°. The gantry was in an up position, and the couch was in line with the gantry stand. Holding all parameters constant, with the exception of the collimator angle for MLC‐defined fields, the difference may be attributed to the combination of both MLC offset and walkout. The second method consisted of investigating each MLC field defined dataset For quantification of the MLC offset, the collimator is considered at three positions . The degree of offset and walkout is measured relative to the collimator at 0° by noting the magnitude of correction needed at either extreme (90° and 270°). The magnitude of correction for the 0° collimator position was noted to fall between the magnitudes of correction for 90° and 270°, and the difference between the magnitudes for the extremes was the quantified measure of the effect of collimator offset and walkout.An illustration of the effect of couch walkout is shown . The magA global evaluation of each of the 149 datasets is presented in a three‐dimensional illustration where the x, y, and z positions are the spatial corrections needed to align the radiation defined isocenter from the laser defined isocenter . The par(±0.42 mm) was two standard deviations of the data after the exclusion of day‐to‐day setup variation which was found to encompass all of the effects of gantry sag, MLC offset, collimator walkout, and couch walkout. The final term (±0.25 or ±0.50 mm) was either 1 or 2 SDs, which included the day‐to‐day setup variations of the test tool for either the schedule action level or the immediate action level, respectively.This first term (0.58 mm) was the average magnitude of correction needed for the datasets considered after the exclusion of the effects of day‐to‐day setup variation. The second term In order to fully appreciate numerical results from isocenter analyses, it is important to understand and isolate the limitations of isocenter congruency based on independent confounding variables, including daily positioning errors of the test tool, gantry sag, couch walkout, and MLC offset. These effects are alike in that they all add to the uncertainty of measurements, but are not correctable beyond a fundamental limitation determined by mechanical factors of the linear accelerator's construction. This study attempted to quantify the magnitude of these effects separately and then evaluate their additive effect on measurement uncertainty. After the magnitude of the uncorrectable effects was accounted for, a global threshold was established based on clinical data which determined action limits based on numerical results measured during a routine test.The datasets that have been examined in this study are differentiated based on the type of treatment employed. Abbreviated tests are performed for SBRT that do not include any couch positions other than the home position, specifically, with the couch longitudinally in line with the linear accelerator's gantry stand. Furthermore, a cone‐based measurement set would not need to include the variation of collimator angles because of the inherent symmetry of cone‐based treatments.During normal day‐to‐day measurement of isocentricity, there exists a bias due to test tool setup error . In this study, we attempted to isolate this error. The error was determined by noting image sets that offered redundant information in each dataset and whether a spatial correction would have improved the results. The appropriate geometric direction and magnitude of the correction that could have been applied was established . It is important to note that each correction was determined for each dataset and removed to obtain daily setup unbiased data of all datasets. The corrections required to remove day‐to‐day setup variations offer an indication of the magnitude of the error that may be propagated from the variation in setup of the test device and, thus, can offer some insight to the scope of uncertainty that we can afford for our test's sensitivity.SD=0.21.Given the distribution of the data presented in Gantry sag refers to the mechanical displacement of the radiation isocenter at different gantry rotational positions.Using complementary image acquisitions at variable gantry angles, the effect from gantry sag may be quantified for inclusion in consideration of the fundamental limitations of measuring and defining the congruency between radiation‐ and laser/image‐defined isocenters for a linear accelerator. This is not to say that the magnitude of observed gantry sag (0.4 mm) changes on the order of millimeters day‐to‐day. Rather, gantry sag is a fairly constant and stable effect. The argument above quantifies the gantry sag effect to submillimeter values . Some of the noise from this data is the result of some limitations in accurately removing all setup uncertainty in the isocenter pointer phantom. This assertion is made with the assumption that the effect of gantry sag is evenly distributed between the two considered gantry angles. In reality, gantry sag is not evenly distributed among gantry angles, weighing more heavily on the gantry 0° position. This is seen in If the 0.4 mm midpoint represents a global indicator of the gantry sag magnitude, then it may be concluded that the effect of gantry sag on radiation‐ versus laser‐defined isocenters is within the statistical daily deviation of the global variations. If the average magnitude of correction needed is 0.58 mm with the removal of daily positioning errors, then the magnitude of gantry sag is less than this value. Therefore, gantry sag has a magnitude of effect within the average measured correction. Since this gantry sag magnitude is less, it may be concluded that no corrections should be necessary to counter‐effect gantry sag for day‐to‐day measurements. Alignment of the lasers should be performed such that the average deviation between isocenters is minimized. In this respect, the longitudinal shift between the anterior‐to‐posterior and posterior‐to‐anterior fields should be opposite in vector direction, and the average between the two should approach zero.This study investigated image acquisitions taken with variable MLC defined collimator angles to quantify the magnitude of the contributor to the fundamental limitation of isocenter congruency due to MLC offset and walkout. MLC offset is defined as the degree of misalignment of the MLCs with respect to the collimators.The treatment couch for a linear accelerator reflects another isocentric component and has its own associated mechanical limitations of isocenter accuracy.In an attempt to define a clinically meaningful tolerance based on quantifiable history and understanding of the fundamental limitations of the linear accelerator, it should be noted that the average total magnitude of shift required to align the laser‐defined isocenter to the radiation‐defined isocenter is 0.58 mm, following the removal of the daily setup positioning error. A maximally accepted value will then be 1.0 mm given the datasets with daily positioning error misalignment isolated from the data.  isocenter test value for a Varian Novalis Tx linear accelerator of central axis within 0.5 mm coincidence from radiation to imaging isocenter for gantry and/or collimator rotations and 0.75 mm for couch rotations). This assertion agreed very well with our observations that 98.7% of the data points fell within 2 SDs, lending credibility to this use of standard deviations as a threshold defining tool.The other component of this investigation allowed for small deviations in the daily positioning of the test tool. After reviewing the device positioning correction per test, the average magnitude of three‐dimensional shift was 0.38 mm. One SD of this data was 0.25 mm. Unlike the other variables that were considered, this effect was cumulative and must contribute to the test threshold in addition to the 1.0 mm. This may be performed using a first and second tier of 1 and 2 SDs .Both the scheduled action level (or warning level) and an immediate (or stop treatment) action level are incorporated into global threshold values. The schedule action level (the lower of the two thresholds) should exist as a “red fag” with the test operators. If this value is exceeded, treatment may proceed but with the understanding that the medical physics staff are notified of this fag. The data should be investigated by the medical physicists, with a correction to the laser‐defined isocenter if deemed appropriate. Exceeding this level requires scheduled maintenance or investigation, with the understanding that mitigation of the cause will take place. The value associated with the scheduled action level is 1.25 mm which considers gantry sag, MLC offset, collimator walkout, couch walkout, and one level of magnitude of day‐to‐day setup variation of the test phantom. The immediate action level functions as a stop treatment level. If it is exceeded, adjustments should be made immediately to reduce the isocenter congruency results to below threshold prior to further patient treatment. This value is 1.50 mm and includes gantry sag, MLC offset, collimator walkout, couch walkout, and a high level of consideration of day‐to‐day setup variation.0.58+0.42+0.25 mm and 0.58+0.42+0.50 mm, respectively. The result of establishing this threshold .If relative baselines are solely considered as threshold values, then the advantage of absolute tolerances are lost. When the baseline values are used as tolerance definitions, as long as the test results are always grossly incongruent, then they still meet the criteria of the test, regardless of a hypothetical misalignment of the machine isocenters. Granted this baseline value should be tightly established upon initialization of a program; baseline comparisons during periodic test‐to‐test review could still fail to identify a slow trend to poor congruency.For these reasons, this retrospective isocenter congruency test with emphasis on absolute tolerance determination via relative mechanisms was developed to combine the benefits of examining both absolute and baseline results and to establish a meaningful tolerance value that reflects normal day‐to‐day functioning of a linear accelerator with a value appropriate for the demands of an SRS program. The result of this examination was a two‐tier system inclusive of a scheduled action level and an immediate action level.In this study, we noted in our uncorrected data that two data points  exceeded the action level of 1.50 mm, while 42 data points exceeded the warning level of 1.25 mm. We presented the raw data that one would expect to encounter when performing this frequent test, including setup uncertainties. The definition of the warning and action levels presented in this report for this linear accelerator are based on the approximation that 68% of our data points will fall within 1 SD and 95% of our data points will fall within 2 SDs, which requires a normal data distribution. This indicated that 1 of the 20 tests (5%) would trigger an action level. We also noticed a demonstrable offset of the isocenter on average that indicated a systematic issue that likely stemmed from the predilection to define the laser‐defined isocenter to be with the gantry, couch, and collimator in a zero degrees position. The analysis of datasets of the nature performed for this study revealed the repeated application of this conjectured bias, allowing the physicists performing this test to be more mindful of balancing this offset for all available gantry angles. This systematic error and the efficacy in the potential correction of this bias will constitute the subject of further investigation.We determined that 71.8% of our data points fell within 1 SD  and 98.7% of our data points fell within 2 SDs. This 5.6% and 3.9% error may be attributed to the deviation that our actual data possessed from a true Gaussian distribution. Based on the conservative estimate that our assumption provided, it was recommended to expect action levels 1 out of 20 measurements and to prepare the treatment schedule to reflect this intermittent interruption.To establish clear tolerance threshold values for daily implementation of the isocenter congruency test, we recommend using both the daily deviation of the test results in addition to the uncertainties in the reproducibility of setup of the test tool. That is, tolerance values should reflect daily positioning errors in setting up the test device, as well as statistical deviations in the measurements made from images comprising the datasets for each test. These tolerance values should be balanced to allow for daily setup deviations while ensuring reliable detection of true misalignments between the various isocenters in a linear accelerator treatment room.A schedule action level and an immediate action level have been defined based on the examination and quantification of the fundamental limitations of the radiation‐defined isocenter congruency versus the various other defined isocenters for a standard linear accelerator . The schedule action level is 1.25 mm, while the immediate action level is 1.50 mm. These levels are established as absolute values based on machine‐specific data relative to baseline. This study proposes and demonstrates a method for assigning linear accelerator‐specific and clinically meaningful tolerance values for isocenter congruency tests that has bases in both absolute analysis for appropriate interpretation of results and relative comparison to baseline for the purpose of monitoring and responding to possible trend behaviors.We acknowledge Norton Healthcare for their continued support, as well as the Associates in Medical Physics, LLC.Supplementary MaterialClick here for additional data file.