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Breast Cancer Research
Published in: Breast Cancer Research 1/2020

01-12-2020 | Breast Cancer | Letter

Letter to the editor: a response to Ming’s study on machine learning techniques for personalized breast cancer risk prediction

Authors: Daniele Giardiello, Antonis C. Antoniou, Luigi Mariani, Douglas F. Easton, Ewout W. Steyerberg

Published in: Breast Cancer Research | Issue 1/2020

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Excerpt

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. …
Literature
1.
Ming C, Viassolo V, Probst-Hensch N, Chappuis PO, Dinov ID, Katapodi MC. Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models. Breast Cancer Res. 2019;21(1):75.CrossRef
2.
Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12–22.CrossRef
3.
Steyerberg EW, Harrell FE Jr. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol. 2016;69:245–7.CrossRef
4.
Siontis GC, Tzoulaki I, Castaldi PJ, Ioannidis JP. External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol. 2015;68(1):25–34.CrossRef
5.
Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res. 2019;3:18.CrossRef
Metadata
Title
Letter to the editor: a response to Ming’s study on machine learning techniques for personalized breast cancer risk prediction
Authors
Daniele Giardiello
Antonis C. Antoniou
Luigi Mariani
Douglas F. Easton
Ewout W. Steyerberg
Publication date
01-12-2020
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2020
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-020-1255-4

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