PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

A. R. Alitto, R. Gatta, B. G. L. Vanneste, M. Vallati, E. Meldolesi, A. Damiani, V. Lanzotti, G. C. Mattiucci, V. Frascino, C. Masciocchi*, F. Catucci, A. Dekker, P. Lambin, V. Valentini, G. Mantini

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

129 Downloads (Pure)

Abstract

Aim: Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer. Materials & methods: The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded from cancerdata.org. Results: A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables. Conclusion: The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.

Original languageEnglish
Pages (from-to)2171-2181
Number of pages11
JournalFuture Oncology
Volume13
Issue number24
DOIs
Publication statusPublished - Oct 2017

Keywords

  • Decision Support System
  • individualized medicine
  • large database
  • machine learning
  • ontology
  • predictive model
  • STANDARDIZED DATA-COLLECTION
  • DECISION-SUPPORT-SYSTEMS
  • LEARNING HEALTH-CARE
  • RECTAL-CANCER
  • ONCOLOGY
  • RADIOTHERAPY
  • POPULATION
  • RADIOMICS
  • PROGNOSIS
  • PROTOTYPE

Fingerprint

Dive into the research topics of 'PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE'. Together they form a unique fingerprint.

Cite this