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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Cite this

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",

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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

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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.

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KW - large database

KW - machine learning

KW - ontology

KW - predictive model

KW - STANDARDIZED DATA-COLLECTION

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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

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JO - Future Oncology

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ER -