  Can we predict if an early stage cancer patient is at high risk of developing
distant metastasis and what clinicopathological factors are associated with
such a risk? In this paper, we propose a ranking based censoring-aware machine
learning model for answering such questions. The proposed model is able to
generate an interpretable formula for risk stratifi-cation using a minimal
number of clinicopathological covariates through L1-regulrization. Using this
approach, we analyze the association of time to distant metastasis (TTDM) with
various clinical parameters for early stage, luminal (ER+ or HER2-) breast
cancer patients who received endocrine therapy but no chemotherapy (n = 728).
The TTDM risk stratification formula obtained using the proposed approach is
primarily based on mitotic score, histolog-ical tumor type and lymphovascular
invasion. These findings corroborate with the known role of these covariates in
increased risk for distant metastasis. Our analysis shows that the proposed
risk stratification formula can discriminate between cases with high and low
risk of distant metastasis (p-value < 0.005) and can also rank cases based on
their time to distant metastasis with a concordance-index of 0.73.
