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Cancers 2019, 11(3), 328; https://doi.org/10.3390/cancers11030328

Breast Cancer Prognosis Using a Machine Learning Approach

1
BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy
2
Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy
3
Department of Enterprise Engineering, University of Rome “Tor Vergata”, Viale Oxford 81, 00133 Rome, Italy
4
Department of Systems Medicine, Medical Oncology, Tor Vergata Clinical Center, University of Rome “Tor Vergata”, Viale Oxford 81, 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
Received: 18 December 2018 / Revised: 26 February 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Abstract

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 (n = 318), whose performance analysis in the testing set (n = 136) resulted in a C-index for progression-free survival of 0.84, with an accuracy of 86%. Furthermore, the model was capable of stratifying the testing set into two groups of patients with low- or high-risk of progression with a hazard ratio (HR) of 10.9 (p < 0.0001). Validation in multicenter prospective studies and appropriate management of privacy issues in relation to digital electronic health records (EHR) data are presently needed. Nonetheless, we may conclude that the implementation of ML algorithms and RO models into EHR data might help to achieve prognostic information, and has the potential to revolutionize the practice of personalized medicine. View Full-Text
Keywords: breast cancer prognosis; artificial intelligence; machine learning; decision support systems breast cancer prognosis; artificial intelligence; machine learning; decision support systems
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Ferroni, P.; Zanzotto, F.M.; Riondino, S.; Scarpato, N.; Guadagni, F.; Roselli, M. Breast Cancer Prognosis Using a Machine Learning Approach. Cancers 2019, 11, 328.

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