L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy na\"ive breast cancer patients

TL;DR

This paper introduces a L1-regularized neural ranking model for risk stratification of early stage luminal breast cancer patients, identifying key clinical factors associated with time to distant metastasis and demonstrating effective discrimination and ranking capabilities.

Contribution

It presents a novel censoring-aware machine learning approach that produces an interpretable risk formula using minimal clinical covariates, advancing risk prediction in breast cancer.

Findings

The risk formula mainly includes mitotic score, tumor type, and lymphovascular invasion.

The model discriminates high and low risk cases with p-value < 0.005.

It achieves a concordance-index of 0.73 in ranking time to metastasis.

Abstract

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.