The Impact of Clinical and Demographic Factors on High-Risk Patient Classification Frequencies by the EndoPredict Test: A Review and Single-Site Study

<b>Background/Objectives</b>: EndoPredict is a second-generation prognostic assay for estrogen-receptor-positive, HER2-negative breast cancer that integrates molecular and clinical parameters for risk stratification. Multiple studies have reported its clinical utility, while differences in the proportion of patients classified as high- or low-risk have been observed across cohorts. This study aimed to characterize clinical, pathological, and demographic factors associated with these differences. <b>Methods</b>: We conducted a descriptive review of 17 published studies and analyzed a single-institution cohort of 140 patients. Associations between clinicopathological variables and high-risk classification were assessed, including tumor size, lymph node status, histological grade, Ki-67 expression, and reproductive and demographic factors. Differences in inclusion criteria and cohort characteristics were also examined. <b>Results</b>: Tumor size and lymph node involvement emerged as primary determinants of high-risk classification. A high histological grade and Ki-67 levels above 25% were significantly associated with high-risk status (<i>p</i> < 0.001). Conversely, age, age at menarche, menopausal status, Body Mass Index, progesterone receptor expression, molecular subtype, and histological type showed no significant association. A higher number of pregnancies correlated with a lower frequency of high-risk classification (<i>p</i> < 0.01). Heterogeneity in risk distribution across studies was largely attributable to differences in tumor size, nodal involvement, and histological grade. Additional variability was associated with inclusion criteria, sample selection, and regional demographic characteristics. <b>Conclusions</b>: Variability in EndoPredict risk classification reflects both tumor biological features and population-specific factors. These findings emphasize the importance of interpreting genomic risk scores within their clinical and demographic context and support the comparison of risk distributions across heterogeneous patient cohorts.