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

doi:10.2217/fon-2017-0142

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 journal › Article › Academic › peer-review

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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
Issue number	24
DOIs	https://doi.org/10.2217/fon-2017-0142
Publication status	Published - 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

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Alitto, A. R., Gatta, R., Vanneste, B. G. L., Vallati, M., Meldolesi, E., Damiani, A., Lanzotti, V., Mattiucci, G. C., Frascino, V., Masciocchi, C., Catucci, F., Dekker, A., Lambin, P., Valentini, V., & Mantini, G. (2017). PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE. Future Oncology, 13(24), 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 = "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",

author = "Alitto, \{A. R.\} and R. Gatta and Vanneste, \{B. G. L.\} and M. Vallati and E. Meldolesi and A. Damiani and V. Lanzotti and Mattiucci, \{G. C.\} and V. Frascino and C. Masciocchi and F. Catucci and A. Dekker and P. Lambin and V. Valentini and G. Mantini",

year = "2017",

month = oct,

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

language = "English",

volume = "13",

pages = "2171--2181",

journal = "Future Oncology",

issn = "1479-6694",

publisher = "Routledge/Taylor \& Francis Group",

number = "24",

}

TY - JOUR

T1 - PRODIGE

T2 - PRediction models in prOstate cancer for personalized meDIcine challenGE

AU - Alitto, A. R.

AU - Gatta, R.

AU - Vanneste, B. G. L.

AU - Vallati, M.

AU - Meldolesi, E.

AU - Damiani, A.

AU - Lanzotti, V.

AU - Mattiucci, G. C.

AU - Frascino, V.

AU - Masciocchi, C.

AU - Catucci, F.

AU - Dekker, A.

AU - Lambin, P.

AU - Valentini, V.

AU - Mantini, G.

PY - 2017/10

Y1 - 2017/10

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 - Decision Support System

KW - individualized medicine

KW - large database

KW - machine learning

KW - ontology

KW - predictive model

KW - STANDARDIZED DATA-COLLECTION

KW - DECISION-SUPPORT-SYSTEMS

KW - LEARNING HEALTH-CARE

KW - RECTAL-CANCER

KW - ONCOLOGY

KW - RADIOTHERAPY

KW - POPULATION

KW - RADIOMICS

KW - PROGNOSIS

KW - PROTOTYPE

U2 - 10.2217/fon-2017-0142

DO - 10.2217/fon-2017-0142

M3 - Article

C2 - 28758431

SN - 1479-6694

VL - 13

SP - 2171

EP - 2181

JO - Future Oncology

JF - Future Oncology

IS - 24

ER -

PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

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Keywords

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