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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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'.
Original languageEnglish
Pages (from-to)119-136
Number of pages18
JournalFuture Oncology
Volume12
Issue number1
DOIs
Publication statusPublished - 2016

Keywords

  • Big Data
  • data standardization
  • decision support system
  • ontology
  • predictive models
  • semantic web
  • umbrella protocol
  • RANDOMIZED CLINICAL-TRIALS
  • LEARNING HEALTH-CARE
  • LUNG-CANCER
  • RADIATION-THERAPY
  • COLON-CANCER
  • SURVIVAL PREDICTION
  • BAYESIAN NETWORK
  • POOLED ANALYSIS
  • RADIOTHERAPY
  • MEDICINE

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