Probability of axillary lymph node metastasis in breast cancer patients
Probability of axillary lymph node metastasis in breast cancer patients.

Note: This model consists of 4 different submodels. After selection of the patient-specific characteristics, the corresponding model formula is automatically selected. 
Research authors: Qiu S, Aarnink M, Maaren van MC, Dorius MD, Bhattacharya A, Veltman J, Klazen CAH, Korte JH, Estourgie SH, Ott P, Kelder W, Zeng HC, Koffijberg H, Zhang GJ, Dam van GM, Siesling S
Version: 1.43
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  • Oncology
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Probability of axillary lymph node metastasis: %

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How this model should be used:
This model calculates the probability of axillary lymph node metastasis in breast cancer patients.

Model validation and updating:
At total of 1416 breast cancer patients diagnosed at six Dutch hospitals between 2011 and 2015 were used to externally validate the original model, which was published previously.1,2 Subsequently, the original model was updated using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope.1

Model performance:
The AUC for the original model was 0.774.2 Patients were classified into four risk groups by GLM analysis, for which four updated models were created.1 The AUC for the updated models was 0.812. The calibration curves showed that the updated model predictions were better in agreement with actual observations than the original model predictions. FNRs of the updated models were lower than the preset 10% at all cut-off points when the predictive probability was less than 12.0%.

References:

  1. Qiu S, Aarnink M, van Maaren MC, et al. Validation and update of a lymph node metastasis prediction model for breast cancer. Eur J Surg Oncol. 2018;44(5):700-707.
  2. S.Q. Qiu, H.C. Zeng, F. Zhang. A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound. Sci Rep. 2016;6,:21196.

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This model is provided for educational, training and information purposes. It must not be used to support medical decision making, or to provide medical or diagnostic services. Read our full disclaimer.

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