title: Standardized data collection to build prediction models in oncology: a prototype for rectal cancer.
abstract: The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare'.
authors.affiliation: Radiotherapy Department, Sacred Heart University, Rome, Italy.
authors.forename: Elisa
authors.initials: E
authors.lastname: Meldolesi
authors.affiliation: Department of Radiation Oncology (MAASTRO), GROW-School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: Johan
authors.initials: J
authors.lastname: van Soest
authors.affiliation: Radiotherapy Department, Sacred Heart University, Rome, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Damiani
authors.affiliation: Department of Radiation Oncology (MAASTRO), GROW-School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: Andre
authors.initials: A
authors.lastname: Dekker
authors.affiliation: Radiotherapy Department, Sacred Heart University, Rome, Italy.
authors.forename: Anna Rita
authors.initials: AR
authors.lastname: Alitto
authors.affiliation: Radiotherapy Department, Sacred Heart University, Rome, Italy.
authors.forename: Maura
authors.initials: M
authors.lastname: Campitelli
authors.affiliation: Radiotherapy Department, Sacred Heart University, Rome, Italy.
authors.forename: Nicola
authors.initials: N
authors.lastname: Dinapoli
authors.affiliation: Radiotherapy Department, Sacred Heart University, Rome, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Gatta
authors.affiliation: Radiotherapy Department, Sacred Heart University, Rome, Italy.
authors.forename: Maria Antonietta
authors.initials: MA
authors.lastname: Gambacorta
authors.affiliation: Radiotherapy Department, Sacred Heart University, Rome, Italy.
authors.forename: Vito
authors.initials: V
authors.lastname: Lanzotti
authors.affiliation: Department of Radiation Oncology (MAASTRO), GROW-School for Oncology & Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
authors.affiliation: Radiotherapy Department, Sacred Heart University, Rome, Italy.
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Valentini
mesh_terms: D003625:Data Collection; D057225:Data Mining; D006801:Humans; D020407:Internet; D057285:Precision Medicine; D012004:Rectal Neoplasms / Q000188:drug therapy / Q000453:epidemiology* / Q000473:pathology; D012984:Software
keywords: Big Data; data standardization; decision support system; ontology; predictive models; semantic web; umbrella protocol