A new standardized data collection system for brain stereotactic external radiotherapy: the PRE.M.I.S.E project.

Silvia Chiesa,Mariangela Massaccesi,Nikola Dino Capocchiano,Giuseppe Minniti,Alessandro Pacchiarotti,Gerardina Stimato,Luca Capone,Silvia Longo,Federico Bianciardi,Mario Balducci,Vincenzo Valentini,Andrea Damiani,Barbara Tolu,Barbara Nardiello,Federica Rea,Francesco Cellini,Piercarlo Gentile,Roberto Gatta

doi:10.2144/fsoa-2020-0015

Abstract

Background:In recent years, novel radiation therapy techniques have moved clinical practice toward tailored medicine. An essential role is played by the decision support system, which requires a standardization of data collection. The Aim of the Prediction Models In Stereotactic External radiotherapy (PRE.M.I.S.E.) project is the implementation of systems that analyze heterogeneous datasets. This article presents the project design, focusing on brain stereotactic radiotherapy (SRT).Materials & methods:First, raw ontology was defined by exploiting semiformal languages (block and entity relationship diagrams) and the natural language; then, it was transposed in a Case Report Form, creating a storage system.Results:More than 130 brain SRT’s variables were selected. The dedicated software Beyond Ontology Awareness (BOA-Web) was set and data collection is ongoing.Conclusion:The PRE.M.I.S.E. project provides standardized data collection for a specific radiation therapy technique, such as SRT. Future aims are: including other centers and validating an extracranial SRT ontology.

Highlights

In recent years, novel radiation therapy techniques have moved clinical practice toward tailored medicine
identify which clinical decisions are better for specific patients
An essential role is played by the decision support system

Summary

Introduction

Novel radiation therapy techniques have moved clinical practice toward tailored medicine. Conclusion: The PRE.M.I.S.E. project provides standardized data collection for a specific radiation therapy technique, such as SRT. A larger amount of different types of data, together with their increased complexity, need to be considered in the decision-making process [1,2]. Population-based observational studies are recently emerging as a complementary form of research, often named ‘rapid learning healthcare’ (RLHC), which is essential to ensure that clinical trials results can be translated into tangible benefits for the general population [3]. Standardized data collection improves the quality of this process, defining variables and the way they should be shared without ambiguity [4]

Objectives

Methods

Results

Discussion

Conclusion