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

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
55 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)665-681
Number of pages17
JournalBreast cancer research and treatment
Issue number3
Early online date30 Aug 2019
Publication statusPublished - 1 Dec 2019


  • 22/4 OA procedure


Dive into the research topics of 'Validation of the online prediction model CancerMath in the Dutch breast cancer population'. Together they form a unique fingerprint.

Cite this