PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

Anna Rita Alitto; Roberto Gatta; Bgl Vanneste; M. Vallati; Elisa Meldolesi; Andrea Damiani; V. Lanzotti; Gian Carlo Mattiucci; Vincenzo Frascino; Carlotta Masciocchi; Francesco Catucci; A. Dekker; Alain Dekker; P. Lambin; Vincenzo Valentini; Giovanna Mantini

doi:10.2217/fon-2017-0142

PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

Anna Rita Alitto
, Roberto Gatta
, Bgl Vanneste
, M. Vallati
, Elisa Meldolesi
, Andrea Damiani
, V. Lanzotti
, Gian Carlo Mattiucci
, Vincenzo Frascino
, Carlotta Masciocchi
, Francesco Catucci
, A. Dekker
, Alain Dekker
, P. Lambin
, Vincenzo Valentini
, Giovanna Mantini

Maastricht University Medical Center
University of Huddersfield

Research output: Contribution to journal › Article

7 Citations (Scopus)

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 language	English
Pages (from-to)	2171-2181
Number of pages	11
Journal	Future Oncology
Volume	13
DOIs	https://doi.org/10.2217/fon-2017-0142
Publication status	Published - 2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cancer Research
Decision Support System
Oncology
individualized medicine
large database
machine learning
ontology
predictive model

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10.2217/fon-2017-0142

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Alitto, A. R., Gatta, R., Vanneste, B., Vallati, M., Meldolesi, E., Damiani, A., Lanzotti, V., Mattiucci, G. C., Frascino, V., Masciocchi, C., Catucci, F., Dekker, A., Dekker, A., Lambin, P., Valentini, V., & Mantini, G. (2017). PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE. Future Oncology, 13, 2171-2181. https://doi.org/10.2217/fon-2017-0142

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title = "PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE",

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.",

keywords = "Cancer Research, Decision Support System, Oncology, individualized medicine, large database, machine learning, ontology, predictive model, Cancer Research, Decision Support System, Oncology, individualized medicine, large database, machine learning, ontology, predictive model",

author = "Alitto, \{Anna Rita\} and Roberto Gatta and Bgl Vanneste and M. Vallati and Elisa Meldolesi and Andrea Damiani and V. Lanzotti and Mattiucci, \{Gian Carlo\} and Vincenzo Frascino and Carlotta Masciocchi and Francesco Catucci and A. Dekker and Alain Dekker and P. Lambin and Vincenzo Valentini and Giovanna Mantini",

year = "2017",

doi = "10.2217/fon-2017-0142",

language = "English",

volume = "13",

pages = "2171--2181",

journal = "Future Oncology",

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TY - JOUR

T1 - PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

AU - Alitto, Anna Rita

AU - Gatta, Roberto

AU - Vanneste, Bgl

AU - Vallati, M.

AU - Meldolesi, Elisa

AU - Damiani, Andrea

AU - Lanzotti, V.

AU - Mattiucci, Gian Carlo

AU - Frascino, Vincenzo

AU - Masciocchi, Carlotta

AU - Catucci, Francesco

AU - Dekker, A.

AU - Dekker, Alain

AU - Lambin, P.

AU - Valentini, Vincenzo

AU - Mantini, Giovanna

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Cancer Research

KW - Decision Support System

KW - Oncology

KW - individualized medicine

KW - large database

KW - machine learning

KW - ontology

KW - predictive model

KW - Cancer Research

KW - Decision Support System

KW - Oncology

KW - individualized medicine

KW - large database

KW - machine learning

KW - ontology

KW - predictive model

UR - http://hdl.handle.net/10807/111833

UR - http://www.futuremedicine.com/loi/fon

U2 - 10.2217/fon-2017-0142

DO - 10.2217/fon-2017-0142

M3 - Article

SN - 1479-6694

VL - 13

SP - 2171

EP - 2181

JO - Future Oncology

JF - Future Oncology

ER -

PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

Abstract

UN SDGs

Keywords

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