The Magee 3 Equation Predicts Favorable Pathologic Response to Neoadjuvant Endocrine Therapy in Breast Cancer Patients

Simple Summary: Neoadjuvant therapy is a crucial part of breast cancer treatment, but identifying the patients who will benefit the most from it remains challenging. This summary discusses a study of breast cancer treatment, specifically focusing on neoadjuvant endocrine therapy (NET) for postmenopausal, hormone-receptor positive, HER2-negative breast cancer patients. The study aimed to determine whether Magee equations (MEs) could predict how well patients respond to NET. Among the 75 female participants, ME3 emerged as a promising predictor with an AUC of 0.734. This means that ME3 could effectively identify patients likely to respond well to NET. In the analysis, factors such as clinical staging and molecular subtype also showed significant associations with treatment response. Specifically, patients with ME3 values less than 20 were more likely to have a positive response to treatment. The study suggests that using ME3 as a predictive tool could help identify breast cancer patients who are likely to respond positively to NET. This approach offers a cost-effective alternative to the use of Oncotype DX, a commonly used but expensive tool for treatment decision-making. However, larger prospective studies are needed to further validate these findings, which could ultimately improve the selection of the most suitable candidates for NET. Background: Breast cancer (BC) remains a significant health care challenge, and treatment approaches continue to evolve. Among these, neoadjuvant endocrine therapy (NET) has gained prominence, particularly for postmenopausal, hormone-receptor positive, HER2-negative (HR+/HER2-) BC patients. Despite this, a significant gap exists in identifying patients who stand to benefit from NET. The objective of this study was to assess whether Magee equations (MEs) could serve as predictors of response to NET. Methods: This retrospective study included adult patients with invasive BC who underwent NET followed by curative surgery. Assessment of sociodemographic, clinical, and tumor-related variables was conducted. The ME1, ME2, ME3, and ME mean were analyzed to explore their predictive role for NET response. Receiver operating characteristic (ROC) curves were employed, along with the determination of optimal cutoff points. Logistic regression models were utilized to identify the most significant predictors of pathological response. Results: Among the 75 female participants, the mean age was 69.4 years, with the majority being postmenopausal (n = 72, 96%) and having an ECOG-PS of 0/1 (n = 63, 84%). Most patients were classified as luminal A (n = 41, 54.7%). ME3 emerged as a promising predictor, boasting an AUC of 0.734, with sensitivity of 90.62% and specificity of 57.50% when the threshold was <= 19.97. In univariate analysis, clinical staging (p = 0.002), molecular subtype (p = 0.001), and ME3 (continuous = 0.001, original 3-tier: p = 0.013, new 2-tier: <0.001) categories exhibited significant associations with pathological response. In the multivariate model, clinical staging and new 2-tier ME3 (<20 vs. >= 20) were included as significant variables. Conclusions: Patients with ME3 < 20 have a higher likelihood of presenting a pathological response, offering a cost-effective alternative tool to Oncotype DX. Larger future studies with a prospective design are awaited to confirm our findings.