Abstract book - ESTRO meets Asia

S70 ESTRO meets Asia 2018

lymphoma implemented at our institute, both improving neurobehavioral outcomes and maintaining oncological outcomes are accomplished. PO-173 PREdiction Models in Stereotactic External Radiotherapy for brain lesions: PRE.M.I.S.E. project F. Cellini 4 , J. Lenkowicz 1 , M. Massaccesi 4 , G. Minniti 2 , A. Pacchiarotti 4 , V. Lanzotti 5 , L. Boldrini 1 , M. Balducci 4 , A. Damiani 4 , V. Valentini 1 , P. Gentile 2 1 Fondazione Policlinico A. Gemelli IRCCS - Università Cat tolica Sacro Cuore, Dipartimento Scienze Radiologiche- Radioterapiche ed E matologiche- Istituto di Radiologia., rome, Italy 2 UPMC San Pietro FBF, Advanced Radiotherapy Center, Roma, Italy 3 Fondazione Policlinico Universitario A. Gemelli IRCCS, UO Fisica sanitaria, Rome, Italy 4 Fondazione Policlinico Universitario A. Gemelli IRCCS, Dipartimento Scienze Radiologiche- Radioterapiche ed E matologiche, Rome, Italy 5 KBO.COMsrl- UCSC, spinoff, Rome, Italy Purpose or Objective Recent advances in modern radiation therapy techniques, including stereotactic radiotherapy (SRT), have created new challenges leading the clinical practice towards a personalized medicine. Beside the widely accepted and old-fashioned clinical guidelines, personalized medicine could be improved by the use of 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 the ontology, this multicentric study aims at implementing systems that analyze large heterogeneous datasets in order to build up a PM which is specific for SRT technique. Material and Methods First a multidisciplinary team made of physicians, physicist, nurses and therapists was created by the two centres initially involved in the project. The first step was to identify variables, validate them and built up the ontology. Creating an ontology for a specific technique let us figure out the complexity and the extent of the number of variables related to such a sophisticated technique. Stereotactic radiosurgery main characteristics are multiple focused radiation beams intersecting over a target, delivery of a highly conformal high-dose, sparing of the surrounding tissues and steep dose gradient. Usually delivered in a single fraction, it can sometimes be delivered over multiple once-daily fractions, usually to a maximum of five. We started from brain ontology adding SRT specific variables. The second step was to implement a system that defines variables’ characteristics and relationships among them so the semantic web technology was put into practice. Results The team created brain SRT ontologies that are organized in three levels (registry, procedural and research level). We collected more than 130 variables and all the information were classified in order to easily diversify queries. The ontology allows creating Case Report Form (CRF) that lets us collecting data through the BOA (Beyond Ontology Awareness) Web platform, shared between the first two centers involved. Conclusion We drafted brain SRT ontology in order to create a common language with the aim of enlarging our databases S. Longo 1 , S. Chiesa 1 , F. Bianciardi 2 , B. Tolu 2 , B. Nardiello 2 , F. Rea 2 , G. Stimato 3 , L. Capone 2 ,

by involving other centres in data collection. 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 analyze large heterogeneous datasets specific for SRT. This will lead to the development and validation of multifactorial models that are able to assist clinical decision making. Next step is selecting variables that could fit for other anatomical sites and combine them with the ones related to specific sites in order to implement an SBRT ontology. D. Cusumano 1 , I. Palumbo 2 , R. Barone 1 , A. Rongoni 2 , R. Tarducci 2 , R. Russo 3 , P. Floridi 2 , D. Nicola 3 , S. Longo 1 , L. Boldrini 1 , M. Balducci 1 , V. Valentini 1 , C. Aristei 2 1 Fondazione Policlinico A. Gemelli IRCSS - Università Cat tolica del Sacro Cuore, Dipartimento Scienze Radiologiche- Radioterapiche ed E matologiche- Istituto di Radiologia, Roma, Italy 2 Università di Perugia- Ospedale S. Maria della Misericor dia, Sezione di Radioterapia Oncologica- Dipartimento di Chir urgia e Scienze Biomedicali, Perugia, Italy 3 Fondazione Policlinico A. Gemelli IRCSS, Dipartimento Scienze Radiologiche- Radioterapiche ed E matologiche- Istituto di Radiologia, Roma, Italy Purpose or Objective Glioblastoma’s heterogeneous features were the subject of different studies conducted in order to correlate tumor images and clinical outcomes and thus develop predictive models and individualized patient management. A multi- centric study, the GLI.F.A. (Glioblastoma advanced Imaging Features Analysis) Project, was performed for a comprehensive analysis of GBM heterogeneity to increase the power of decision support models Material and Methods In this first phase of the study we analyzed adult patients affected by GBM, that undergo to surgery and standard chemo-radiotherapy according to EORTC 26981-22981- NCIC trial. GTV was contoured in the contrast-enhanced T1 and T2-FLAIR weighted images. We created a brain ontology and the software for sharing and combining patients’ datasets with the aim of standardizing data collection. Therefore, we used the MODDICOM software for the extraction of preoperative MRI features. Wilcoxon Mann Whitney test, Log-rank test for Kaplan-Meier curves were utilized to evaluate the significance of the radiomic features on the T2-Flair and T1 images, using he median value of the radiomic features to categorize the continue variables. We considered as main outcomes the overall survival (OS), the progression fee survival (PFS) and the response to radio-chemotherapy (RTCT) Results We collected data of 27 patients, treated from July 2014 to February 2018, with a median age of 61 years (range 45-75). At the time of the analysis 20 patients were still alive. Using the implemented software 94 features were analyzed on 1.5Tesla (T) (15 cases) and on 3T (12 cases) MRI images. Considering the whole sample, significant features, divided by MRI sequence and outcomes, are reported in Tab1. The first order features, describing the statistical characteristics of images, resulted significant for OS on the T2-FLAIR weighted MRI and for response to RTCT on the T1 weighted MRI; the second order features, describing the spatial correlation between images voxels, showed the most significant result for OS and PFS on contrast-enhanced T1. Another analysis were conducted according to T magnet MRI. The most significant results were observed on 1.5 T MRI images for OS and PFS. The second order features resulted significant for OS, three on the post contrast T1weighted MRI and one on T2-FLAIR PO-174 GLIFA Project: Glioblastoma radiomics Features Analysis. A prospective multi-centric study S. Chiesa 1 , F. Beghella Bartoli 1 , M. Lupattelli 2 ,