Prognostic Models for Nonmetastatic Triple-Negative Breast Cancer Based on the Pretreatment Serum Tumor Markers with Machine Learning

Purpose. Triple-negative breast cancer (TNBC) is a heterogeneous and aggressive disease with poorer prognosis than other subtypes. We aimed to investigate the prognostic efficacy of multiple tumor markers and constructed a prognostic model for stage I-III TNBC patients. Patients and Methods. We included stage I-III TNBC patients whose serum tumor markers levels were measured prior to the treatment. The optimal cut-off value of each tumor marker was determined by X-tile. Then, we adopted two survival models (lasso Cox model and random survival forest model) to build the prognostic model and AUC values of the time-dependent receiver operating characteristic (ROC) were calculated. The Kaplan-Meier method was used to plot the survival curves and the log-rank test was used to test whether there was a significant difference between the predicted high-risk and low-risk groups. We used univariable and multivariable Cox analysis to identify independent prognostic factors and did subgroup analysis further for the lasso Cox model. Results. We included 258 stage I-III TNBC patients. CEA, CA125, and CA211 showed independent prognostic value for DFS when using the optimal cut-off values; their HRs and 95% CI were as follows: 1.787 (1.056–3.226), 2.684 (1.200–3.931), and 2.513 (1.567–4.877). AUC values of lasso Cox model and random survival forest model were 0.740 and 0.663 for DFS at 60 months, respectively. Both the lasso Cox model and random survival forest model demonstrated excellent prognostic value. According to tumor marker risk scores (TMRS) computed by the lasso Cox model, the high TMRS group had worse DFS (HR = 3.138, 95% CI: 1.711–5.033, p