ESTRO 37 Abstract book

S290

ESTRO 37

SP-0554 New irradiation options P. Hoskin 1 1 Mount Vernon Hospital, Cancer Centre, Northwood Middlesex, United Kingdom Abstract text Radiotherapy continues to have a major role in the treatment of high risk prostate cancer. Established techniques use either brachytherapy or external beam photon therapy alone or in combination. Innovations in dose, fractionation and technique continue to redefine state of the art radiation delivery in this setting which currently would mandate the use of static or rotational IMRT and daily IGRT. The advent of the MR linac will provide further insights into inter- and intrafraction verification requirements A dose response for biochemical control of prostate cancer has been long recognised and there is now considerable level 1 evidence that hypofractionated schedules over 4 to 5 weeks are equivalent to longer conventional fractionation. Dose escalation remains a goal and this may be optimally achieved by combining external beam and brachytherapy. Extreme hypofractionation using fraction sizes of >5Gy demands a high level of technical accuracy both in volume definition and delivery. The emergence of stereotactic techniques for prostate cancer has enabled external beam therapy to achieve this. Phase III trials comparing this to conventional external beam schedules are now completed and the results eagerly awaited. In high risk disease the role of pelvic node irradiation remains uncertain. Whilst practised in many centres for Gleason score 8-10 cancers it is recognised that to date Level 1 evidence for this approach is lacking and further trials are endeavouring to address this important question. Modern imaging has enabled greater understanding of the distribution of cancer within the prostate gland with the concept of a radiological index or dominant lesion. Focal boosts have been evaluated both in external beam and brachytherapy providing another avenue for dose escalation within acceptable toxicity profiles.

of fulfilling criterion three. Finally, and most demanding, the models should have 4) clinical utility. While criterion four is outside of the scope of my talk, it is important that the modeler has clinical utility top-of-mind throughout the entire process. Arguably, it is less wastefull to fail an attempt to build a model with potential clinical utility than suceeding in externally validating a model which does not stand a chance to pass the clinical utility bar. The interested participant may benefit from readin Wyatt and Altman’s commentary BMJ 1995;311:1539 for this talk and session in general. Most points made are still very relevant even after more than 20 years from this insightful commentary. SP-0557 Acceptance and commissioning of outcome prediction models L.C. Holloway 1 1 Ingham Institute and Liverpool and Macarthur Cancer Therapy Centres, Radiation Oncology, Sydney, Australia Abstract text With increasing availability and access to radiotherapy datasets as well as increasing computing power, novel learning methods and the scope of data recorded in clinics, there has also been an increase in radiotherapy outcome prediction models. These models are being developed for both cancer outcomes and toxicity outcomes and are covering many disease sites. Availability of these models has the potential to provide additional information to clinicians and patients, particularly where the current standard of randomised clinical trial evidence may not be available for this particular patient with their individual disease characteristics and co-morbidities. With publication of these outcome models and potentially availability of these models in software tools comes the question of how we determine if a particular model is acceptable and valid for use within our clinics. The first question to be asked is the validity of the proposed model beyond the data that may have been used for training the model. The ‘Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD)’ statement (Collins et al BMC Medicine 13:1, 2015) presents clear guidelines in this regard, splitting models into types 1 and 2, where only a single dataset has been used, type 3 where the model has been developed on one dataset and validated on an independent dataset and type 4 where the model has been independently validated on a separate dataset without reference to the model development dataset. At a minimum type 3 models are necessary but ideally this would be type 4. Any particular software implementation of this model should also be carefully tested to ensure that this is performing as expected for a known dataset, where possible openly available data previously used with the model can be used to achieve this. The second question to be asked is the appropriateness of the given model for the data within a given clinic. In the same fashion that we commission physics dosimetry tools ( e.g. dose calculation models used within a treatment planning system) we should also be ensuring that any outcome prediction model is commissioned for the datasets available in our individual clinics. This requires carefully scrutiny of the data will be used in model to ensure this data is measured in the same manner as that which the model was developed on and any differences accounted for. This includes factors such as imaging parameters. A retrospective cohort of patient data, importantly in the same format as the data which will be used for future patients, should be used to test the model. Ideally this would be undertaken with statistical rigour such that differences between the model outcomes and local practice would be observed. If this is not possible then active review of the model should be

SP-0555 The surgeon point of view A.Briganti Fondazione Centro San Raffaele, Milano, Italy

Abstract not received

Symposium: Outcome prediction models for RT indications - development, validation, acceptance, commissioning and application

SP-0556 Outcome prediction models – training and validation I.R. Vogelius 1 1 Vogelius Ivan Richter, Academic Physics, Frederiksberg, Denmark Abstract text Outcome models should ideally fulfill the follwing four criteria to be applied in the clinic: 1) face validity, ie. the models should be credible and meaningful to the treating physician. 2) internal validity, ie. the model should provide a good fit to the data on which it is built. We will spend some time on discussing how to perform and present model fits in a clear and concise manner such that the fulfillment of this point is adequately conveyed to the critical reader of your work. Criterion no 3) is external valudity, ie. the model ability to be applied in datasets outside of the training cohort. We will go through successful and not-so-successfull examples from literature and discuss how to impove your chances