ConclusionIn summary, the surface guided Radiotherapy (SGRT) imaging has a significant correlation with CBCT imaging in detecting setup errors in the treatment for skull base and Head and neck cancer patients.The differences of Σ and δ between the two IGRT methods were less than 1mm and 0.5 o .Hence SGRT is feasible option to position the patient to reduce the setup errors as much and to reduce the frequency of CBCT to avoid unnecessary imaging radiation to patients OC-051To determine an imaging regime for Linac based VMAT SRS.Are we going overboard with image guidance?

ESTRO 38that FSRT was used for large lesions and adverse locations, we find that FSRT can particularly be beneficial for patients with large lesions or lesions located near critical structures.Further investigation is warranted to determine the optimal dose/fractionation.

Purpose Recently an increase in radiosurgery treatments for multiple metastases has been observed. Traditionally these lesions were treated using one isocenter for each target and the patient had to be shifted during a single session for repositioning.The implementation of single-isocenter volumetric modulated arc therapy (VMAT) radiosurgery technique for multiple intracranial lesions allows a significant reduction of treatment times, as well as enhancing accuracy by reducing the potential intrafraction motion [ [1] Jay Morrison et al. Is a single isocenter sufficient for volumetric modulated arc therapy radiosurgery when multiple intracranial metastases are spatially. https://doi.org/10.1016/j.meddos.2016.06.007. Google Scholar ]. Methods For 20 patients treated with VMAT for multiple brain metastases we have analyzed the treatment process. Each case had different number and distribution of metastases. We performed a CT simulation scan with 1.25 mm thickness slice for all patients. The simulation images were registered with a MR in order to define the targets. For each case we compared the VMAT single-isocenter technique plan with the sums of the plans of every single lesion. Plans were evaluated using Dose Volume Histograms, Conformity and Uniformity Indexes. All the plans were optimized with 6 MV flattened filter free arcs. Set up checks were performed using both BrainLAB ExacTrac and Varian Cone Beam CT (CBCT). For 10 patients we also evaluated the intrafraction motion. Results No significant difference was detected between the two techniques. The single-isocenter technique comes out better for the dose distributions especially for low doses. We discovered a good agreement between ExacTrac and CBCT and a minimum intrafraction movement (<1 mm) which was dosimetrically insignificant also for peripheral lesions. We underline a very significant reduction of treatment times. Conclusions Both methods achieve good dosimetric results. Single-isocenter VMAT radiosurgery treatment for multiple metastases is a faster technique and could be performed safely using image guided radiotherapy treatment thus relieving patient discomfort.

It becomes more and more clear that people with the same cancer can have different forms of the disease, so responses to treatment and their toxicity can vary. Beside the widely accepted and old fashioned clinical guidelines, personalized medicine needs the DSS (Decision Support System) to be implemented in order to offer a valuable assistance in clinical decision making and in daily practice. DSS are represented by softwares, apps, nomograms all based on the creation of predictive models (PM) that require standardization of data collection. Ontology represents a fundamental tool to establish a common language without knowing, beforehand, which one of the described features could be the most relevant for a specific hypothesis. Through the creation of a ontology for a specific technique [Stereotactic Radiotherapy (SRT)], this multicentric study aims at collecting standardized data in order to build up a predictive model. We started focusing on brain metastases SRT. The first two centres involved selected personnel in order to create a multidisciplinary team made of physicians, physicist, nurses and therapists. They meet twice a month. The first step of the project was to identify variables, validate them and built up the ontology. The second step was to implement a system that defines variables characteristics and the relationships among them. The semantic web technology was put into practice and the team is now collecting data on a dedicated software called BOA (Beyond Ontology Awareness) with its own WEB platform. The future development is to involve other RT centres to combine multiple datasets. The drafted ontology is made up of more than 130 variables related with brain SRT, including both general “brain” nomenclature and SRT specific features. All the features were collected and organized in three levels (‘registry’, ‘procedural’ and ‘research’ level) that classify all the information in order to easily diversify queries. The BOA Web platform was shared between the first two centers involved and data collection process is ongoing by using Case Report Form (CRF). We drafted a brain SRT ontology in order to create a common language with the aim of enlarging our databases by involving different centres in collecting clinical data. The creation of a centralized multicentre large database is therefore essential for the development of ad hoc prediction tools. The aim is to implement a system that analyse large heterogeneous datasets to develop and validate multifactorial models that are able to assist clinical decision making based on specific pre-treatment characteristics in each individual patient.

The introduction of measures allowing physicians to deliver tailored treatment demonstrates medicine is moving on from the old population-based studies and confers an essential role to Decision Support Systems. The DSS are the helpful (softwares, apps, nomograms, etc) results of one or more previous predictive model (PM) creation; the PM demands a standardization of data collection. Ontology represents a fundamental tool to standardize data collection and to establish a common language without knowing, beforehand, which one of the described features could be the most relevant for a specific hypothesis. Through the creation of a specific ontology for SRT , this multicentric study aims at collecting standardized data in order to build up a predictive model. In phase I we focused on brain metastases (BM). A multidisciplinary team has been created from the first two centres involved including physicians, physicist, nurses and therapists and meets twice a month. It works on identifying variables to be inserted in the ontology, validates variables and builds up a system that defines variables' characteristics and the relationships among them. The future development of the sharing platform is to involve other RT centres to combine multiple datasets. We selected more than 130 variables related with SRT for BM starting from general "brain" nomenclature adding then SRT specific features like: isodose line prescription, conformity index, distance among treated lesions, calculation algorithms, resolution grid, multiple lesion treatment with a single isocenter, etc. All the features are collected and organized in three levels: 'registry' level, containing epidemiological information; 'procedural' level which includes elements about treatment, toxicities and outcome evaluation and 'research' level where dimensional data, such as imaging information, are collected. These levels classify all the information in order to easily diversify queries. We created a brain SRT ontology that shares features with other cancer sites and keeps SRT specific characteristics. Furthermore, we constructed a platform for multiple dataset sharing which facilitates the creation of large communal databases. Our project's innovation resides mainly in having created the ontology for a particular radiation therapy technique instead of creating a model that only concerns a specific pathology. To our knowledge no predictive model focusing on a treatment technique is available in literature. Next step of this initiative consists of patients' enrolment phase. Our future perspectives are both to include other centres in collecting data and to start building ontology for SRT in lung cancer.

Gleason Score (GS) < 7; the mean of iPSA was 18 ng/mL; the rate of clinical positive nodes was 1%.The ADT was prescribed to 69% of patients in neoadjuvant setting, 65% in concomitant setting and 34% in adjuvant setting.The mean follow-up was 81 months. Results:The prognostic factors resulted statistically significant for all groups of patients at both, univariate and multivariate analysis, were the GS and the iPSA.In intermediate and high/very-high risk patients at multivariate analysis the prognostic factors for CSOS were: GS (p= 0.001), positive lymph nodes on CT scan (p = 0.05) and rectal preparation during the treatment (p= 0.005); for the BDFS were: GS (p= 0.008), patient risk classification (p= 0.037), positive lymph nodes on CT scan (p= 0.004), iPSA (p= 0.001) and rectal/bladder preparation during the radiation treatment (p=0.001); for the CDFS were: number of positive core on biopsy (p= 0.003), GS (p= 0.0003), positive lymph nodes on CT scan (p= 0.015), iPSA (p= 0.0056) and RT dose (p = 0.001).In high/very-high risk patient group at multivariate analysis the prognostic factors for CSOS were: biopsic Gleason Score, clinical/radiological stage, RT dose; for BDFS were: biopsic Gleason Score, adjuvant ADT, clinical/radiological stage, iPSA and RT dose>77.7 Gy; for CDFS were: biopsic Gleason Score, clinical/radiological stage, iPSA and RT dose>77.7 Gy. Conclusion:Our results confirm several prognostic factors already described by literature, adding a new prognostic factor represented by the rectal/bladder preparation, generally known for its effect on toxicity but not yet on outcome.We believe that in the future a new nomogram should include also some therapeutic variables (as RT dose, RT technique and ADT), to help clinicians in decision-making.