Standardized data collection to build prediction models in oncology: A prototype for rectal cancer

Elisa Meldolesi, Johan Van Soest, Andrea Damiani, Andre Dekker, Anna Rita Alitto, Maura Campitelli, Nicola Dinapoli, Roberto Gatta, Maria Antonietta Gambacorta*, Vito Lanzotti, Philippe Lambin, Vincenzo Valentini

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivista

27 Citazioni (Scopus)


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'.
Lingua originaleEnglish
pagine (da-a)119-136
Numero di pagine18
RivistaFuture Oncology
Stato di pubblicazionePubblicato - 2016


  • Big Data
  • Cancer Research
  • Data Collection
  • Data Mining
  • Humans
  • Internet
  • Oncology
  • Precision Medicine
  • Rectal Neoplasms
  • Software
  • data standardization
  • decision support system
  • ontology
  • predictive models
  • semantic web
  • umbrella protocol


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