The presence of axillary lymph node metastases is the most important prognostic factor in breast cancer and can
influence the selection of adjuvant therapy, both chemotherapy and radiotherapy. In this work we present a set
of kinetic statistics derived from DCE-MRI for predicting axillary node status. Breast DCE-MRI images from
69 women with known nodal status were analyzed retrospectively under HIPAA and IRB approval. Axillary
lymph nodes were positive in 12 patients while 57 patients had no axillary lymph node involvement. Kinetic
curves for each pixel were computed and a pixel-wise map of time-to-peak (TTP) was obtained. Pixels were first
partitioned according to the similarity of their kinetic behavior, based on TTP values. For every kinetic curve,
the following pixel-wise features were computed: peak enhancement (PE), wash-in-slope (WIS), wash-out-slope
(WOS). Partition-wise statistics for every feature map were calculated, resulting in a total of 21 kinetic statistic
features. ANOVA analysis was done to select features that differ significantly between node positive and node
negative women. Using the computed kinetic statistic features a leave-one-out SVM classifier was learned that
performs with AUC=0.77 under the ROC curve, outperforming the conventional kinetic measures, including
maximum peak enhancement (MPE) and signal enhancement ratio (SER), (AUCs of 0.61 and 0.57 respectively).
These findings suggest that our DCE-MRI kinetic statistic features can be used to improve the prediction of
axillary node status in breast cancer patients. Such features could ultimately be used as imaging biomarkers to
guide personalized treatment choices for women diagnosed with breast cancer.