ESTRO 37 Abstract book

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ESTRO 37

undertaken for the initial cohort of patients. As a community it is then important that results from this acceptance and commissioning work is shared to enable further models to be developed and updated where appropriate and to ensure that processes and tools for undertaking this work can also be further developed. SP-0558 Automated planning and prediction models for bias-free treatment technique selection A.W. Sharfo 1 , M. Dirkx 1 , R. Bijman 1 , L. Rossi 1 , T. Arts 2 , S. Breedveld 1 , M. Hoogeman 1 , B. Heijmen 1 1 Erasmus MC Cancer Institute, Radiation Oncology, Rotterdam, The Netherlands 2 University Medical Center Utecht, Radiology, Utrecht, The Netherlands Abstract text Generally, treatment planning is performed iteratively in a trial-and-error procedure, and as a result, the treatment plan quality may highly depend on the available planning time, and the experience of the treatment planner. This may result in a sub-optimal treatment, where the patient has an enhanced probability of developing radiation-associated complications. On the other hand, it is well known that the risk of radiation-induced toxicity increases when higher doses and larger volumes of sensitive structures are involved. Over the years, toxicity prediction models have been published for a number of organs-at-risk, quantifying risk with clinically relevant metrics. In modern radiotherapy, for each patient, different treatment options (modalities/techniques/fractionations) may be available. Each of which has its own pros and cons. Treatment planning may be used to assist in making choices between treatment options. However, with current trial-and-error planning, both plans may be suboptimal to an unknown extent, which may jeopardize adequate selection. Automated planning may be used to substantially enhance the accuracy and validity of treatment option selection. This is especially true if for all treatment options the plan is automatically generated with multi-criterial optimization using exactly the same optimizer, optimization scheme, planning constraints and prioritized objectives. This approach may be used for ‘bias-free’ selection of the (on average) most favorable treatment option for a patient population with a certain type of cancer, and it can also be used to select the best option for each individual patient. In this presentation, a system for ‘bias-free’ selection of treatment options using automated planning will be discussed. Examples will be presented for VMAT, proton therapy, CyberKnife, and hypofractionation. Moreover, we will discuss the impact of the accuracy of the applied prediction models on the selection. SP-0559 For the motion: Until we finally perfect x-ray vision, we need patient specific QA L. McDermott 1 1 Noordwest Alkmaar, Radiotherapy, Alkmaar, The Netherlands Abstract text Radiotherapy is using radiation that you can’t see to treat tumours that you can’t see. In an ideal world, in a variation on the Superman idea, a perfect x-ray vision tool would available that can scan a person, localise any tumour cells, assess their sensitivity and simultaneously treat disease with a precise, ablative dose of high energy charged or uncharged particles. In this scenario, there is no treatment planning or verification required, since for Debate: Is there still a place for patient specific QA?

every individual the machine can carry out the required treatment directly. But this is not the world we live in (yet). The questions is, is there still a place for patient specific QA today? Let’s focus on the word “still”. There are many impressive Radiotherapy products available today that automate currently error-prone, human driven processes. How many attendees at ESTRO (or any RT conference) will wander through the exhibition hall and think “if only we could buy and run that! We just don’t have the money, staff or skills” When every RT department in the world has the best treatment equipment available with highly skilled workers to optimally treat every kind of disease that comes their way, we won’t have to “QA” every single plan. Everything will work as expected. No humans will be involved, there won’t be room for error. Radiotherapy in 2018 is not there yet. Good Patient Specific QA is an efficient, robust, sensitive and specific check by a qualified person that the patient’s tumour, or target, receives the prescribed dose. It compares an intended dose with a result, from as early to as late in the treatment process as possible. The term generally implies it is, at least in part, performed by a human. Of course any system that wastes time and doesn’t work should be made obsolete. No one would argue that there is still a role for problem-ridden, bad Patient Specfic QA . The technology to implement a good version exists today, it is just not (yet) a priority for major vendors. We should be striving for good patient specific QA. There are many forms of non-patient specific QA in place, for the beam model, small fields, treatment planning system, mechanics of the delivery systems, image formation, image registration and positioning systems. If everything works as it should, why do you also need patient QA? There are two increases in Radiotherapy that make this necessary 1) personalisation and 2) complexity. First, an impersonal square field or two used to be sufficient to treat most tumours. In the past, the resulting dose distribution depended on the anatomy of the patient. It’s hard to get that wrong. Today the objective is to achieve a uniform, maximum dose distribution in the tumour and very little everywhere else. The result is that you have 2 identical plans about as often as you have have 2 identical patients. Imaging biomarkers in PET, MRI and now CT are being used customise plans to not only diagnosis, but also prognosis, moving away from one-size-fits-all. It is difficult for protocols to keep up with so many exceptions, which can increase the chance of mistakes. Secondly, we have increasing precision, automation, dose and accuracy in Radiotherapy. The result is complicated protocols for even simple treatments. In addition, the level of training for staff and the time allowed to carry out additional tasks does not always keep up. Consequently, both the risk of human error and the impact of accidents is also increasing. While complicated plans get all the attention, major accidents that get reported are often standard, simple treatments that went wrong. When 1 accident in a million good treatments makes headline news, doubt is cast over all Radiotherapy. Due the personalised nature of plans and increasingly complicated protocols, patient specific QA still has an important role to play. Finally patient specific QA provides reassurance that the patient was treated as intended. When a patient or a lawyer asks for evidence that the treatment went according to plan, the department should have something to show. They are unlikely to be satisfied with evidence that the previous 100 patients were checked and found to be fine. It is estimated 1 in 20 patients will suffer injuries from radiation treatment. We don’t know how many are due to mistakes unless we have plan-specific verification data. Treatment prescriptions and randomised trials also rely on information about accuracy and trend changes.