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Erschienen in: Breast Cancer Research and Treatment 3/2019

30.08.2019 | Epidemiology

Validation of the online prediction model CancerMath in the Dutch breast cancer population

verfasst von: Liza A. Hoveling, Marissa C. van Maaren, Tom Hueting, Luc J. A. Strobbe, Mathijs P. Hendriks, Gabe S. Sonke, Sabine Siesling

Erschienen in: Breast Cancer Research and Treatment | Ausgabe 3/2019

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Abstract

Purpose

CancerMath predicts the expected benefit of adjuvant systemic therapy on overall (OS) and breast cancer-specific survival (BCSS). Here, CancerMath was validated in Dutch breast cancer patients.

Methods

All operated women diagnosed with stage I–III primary invasive breast cancer in 2005 were identified from the Netherlands Cancer Registry. Calibration was assessed by comparing 5- and 10-year predicted and observed OS/BCSS using χ2 tests. A difference > 3% was considered as clinically relevant. Discrimination was assessed by area under the receiver operating characteristic (AUC) curves.

Results

Altogether, 8032 women were included. CancerMath underestimated 5- and 10-year OS by 2.2% and 1.9%, respectively. AUCs of 5- and 10-year OS were both 0.77. Divergence between predicted and observed OS was most pronounced in grade II, patients without positive nodes, tumours 1.01–2.00 cm, hormonal receptor positive disease and patients 60–69 years. CancerMath underestimated 5- and 10-year BCSS by 0.5% and 0.6%, respectively. AUCs were 0.78 and 0.73, respectively. No significant difference was found in any subgroup.

Conclusion

CancerMath predicts OS accurately for most patients with early breast cancer although outcomes should be interpreted with care in some subgroups. BCSS is predicted accurately in all subgroups. Therefore, CancerMath can reliably be used in (Dutch) clinical practice.
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Metadaten
Titel
Validation of the online prediction model CancerMath in the Dutch breast cancer population
verfasst von
Liza A. Hoveling
Marissa C. van Maaren
Tom Hueting
Luc J. A. Strobbe
Mathijs P. Hendriks
Gabe S. Sonke
Sabine Siesling
Publikationsdatum
30.08.2019
Verlag
Springer US
Erschienen in
Breast Cancer Research and Treatment / Ausgabe 3/2019
Print ISSN: 0167-6806
Elektronische ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-019-05399-2

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