ESTRO 2021 Abstract Book

S34

ESTRO 2021

Conclusion In the long-term, hypofractionation offers a cost efficient mechanism to treat an increasing number of patients within the existing linear accelerator capacity. As a large majority of treatment costs are fixed/stepped, however, disinvest is complex and short to medium-term imbalances between demand and capacity will result in increased treatment costs. This may act as a disincentive to delivering hypofractionated treatment when reimbursement is on a per fraction basis. OC-0059 Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Carcinoma G. Macchia 1 , G. Ferrandina 2 , S. Patarnello 3 , R. Autorino 4 , C. Masciocchi 3 , V. Pisapia 5 , C. Calvani 3 , C. Iacomini 3 , A. Cesario 6 , B. Gui 7 , V. Rufini 4 , L. Boldrini 8 , G. Scambia 2 , V. Valentini 4 1 Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Radiation Oncology Unit , Campobasso, Italy; 2 Fondazione Policlinico Universitario A. Gemelli IRCCS, Department of Woman, Child and Public Health, Rome, Italy; 3 Fondazione Policlinico Universitario A. Gemelli IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Rome, Italy; 4 Fondazione Policlinico Universitario A. Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Rome, Italy; 5 Fondazione Policlinico Universitario A. Gemelli IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia , Rome, Italy; 6 Fondazione Policlinico Universitario A. Gemelli IRCCS, Scientific Directorate, Rome, Italy; 7 Fondazione Policlinico Universitario A. Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia,, Rome, Italy; 8 Fondazione Policlinico Universitario A. Gemelli, IRCCS , Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Rome, Italy Purpose or Objective The first prototype of the “Multidisciplinary Tumor Board Smart Virtual Assistant” is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician's attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision-making and standardized language and interpretation. Materials and Methods Data from patients affected by invasive carcinoma of the cervix (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography–Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of