Letter to the editor: a response to Ming’s study on machine learning techniques for personalized breast cancer risk prediction
Breast Cancer Research, vol. 22, pp. 17
Abstract
A recent paper [1] compared two well-known breast cancer risk prediction models (BCRAT and BOADICEA) with eight different machine learning (ML) methods. The authors found a striking improvement in cancer prediction with ML. While their comparative assessment against more classical approaches is timely, we are skeptical about the results presented.
Authors 5
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Leiden University Medical Center · The Netherlands Cancer Institute
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands. d.giardiello@nki.nl
Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. d.giardiello@nki.nl
Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
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Affiliation as printed
Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
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Fondazione IRCCS Istituto Nazionale dei Tumori
Affiliation as printed
Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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Affiliation as printed
Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
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Leiden University Medical Center · Erasmus MC Cancer Institute
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
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