Breast Cancer Prognosis Using a Machine Learning Approach.

artificial intelligence breast cancer prognosis decision support systems machine learning

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
07 Mar 2019
Historique:
received: 18 12 2018
revised: 26 02 2019
accepted: 04 03 2019
entrez: 15 3 2019
pubmed: 15 3 2019
medline: 15 3 2019
Statut: epublish

Résumé

Machine learning (ML) has been recently introduced to develop prognostic classification models that can be used to predict outcomes in individual cancer patients. Here, we report the significance of an ML-based decision support system (DSS), combined with random optimization (RO), to extract prognostic information from routinely collected demographic, clinical and biochemical data of breast cancer (BC) patients. A DSS model was developed in a training set (

Identifiants

pubmed: 30866535
pii: cancers11030328
doi: 10.3390/cancers11030328
pmc: PMC6468737
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : European Social Fund
ID : PNR 2015-2020 ARS01_01163 PerMedNet - CUP B66G18000220005
Organisme : European Social Fund
ID : PON I&C 2014-2020 - F/050383/01-03/X32

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Auteurs

Patrizia Ferroni (P)

BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy. patrizia.ferroni@sanraffaele.it.
Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy. patrizia.ferroni@sanraffaele.it.

Fabio M Zanzotto (FM)

Department of Enterprise Engineering, University of Rome "Tor Vergata", Viale Oxford 81, 00133 Rome, Italy. fabio.massimo.zanzotto@uniroma2.it.

Silvia Riondino (S)

BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy. silvia.riondino@sanraffaele.it.
Department of Systems Medicine, Medical Oncology, Tor Vergata Clinical Center, University of Rome "Tor Vergata", Viale Oxford 81, 00133 Rome, Italy. silvia.riondino@sanraffaele.it.

Noemi Scarpato (N)

Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy. noemi.scarpato@unisanraffaele.gov.it.

Fiorella Guadagni (F)

BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy. fiorella.guadagni@sanraffaele.it.
Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy. fiorella.guadagni@sanraffaele.it.

Mario Roselli (M)

Department of Systems Medicine, Medical Oncology, Tor Vergata Clinical Center, University of Rome "Tor Vergata", Viale Oxford 81, 00133 Rome, Italy. mario.roselli@uniroma2.it.

Classifications MeSH