For the prediction and evaluation of neoadjuvant chemotherapy response in breast cancer patient: Development of a nomogram

Background: The study aims to evaluate the impact of a series of significant clinical and pathological parameters in predicting the response to neoadjuvant chemotherapy in breast cancer patients and to anticipate the response to treatment. Material and Methods: Patients who were admitted to the General Surgery Clinic of Bolu Abant İzzet Baysal University Faculty of Medicine Hospital between 2015 and 2024, received neoadjuvant chemotherapy, and whose complete data were accessible from the hospital's computer database were included. Patients who did not receive neoadjuvant chemotherapy were excluded from the study, and their files were reviewed retrospectively. Data collected included age at diagnosis, family history, smoking status, physical examination findings, histopathological results (diagnosis, size, stage, receptor status, grade, Ki-67, lymphovascular invasion status, etc.), the regimen of neoadjuvant chemotherapy received, total duration of neoadjuvant chemotherapy treatment, time between preoperative biopsy diagnosis and neoadjuvant chemotherapy, and preoperative laboratory parameters (HGB, PLT, LYM, MONO, NEU, CRP, ALB, LDH, TOTAL PROTEIN), and radiological data. Patients were grouped as those who showed a complete response to neoadjuvant chemotherapy and those who did not. All obtained data were statistically compared, and a nomogram was created to predict the complete response. Results: The study analyzed the demographic and clinical characteristics of 50 patients diagnosed with locally advanced breast cancer. The proportion of patients with positive estrogen receptors was 70%, while the proportion of those with positive progesterone receptors was 52%. The average tumor size was 32.12 ± 17.67 mm. 28% of the patients showed a complete response to neoadjuvant chemotherapy. Patients with estrogen receptor negativity (p = 0.002) and progesterone receptor negativity (p = 0.008) had a higher rate of complete response. The presence of lymphovascular invasion (p = 0.017) and smaller tumor size (p = 0.010) were also found to be statistically significant factors for complete response. Elevated total protein levels were identified as a significant factor increasing the likelihood of a complete response (p = 0.022). The overall accuracy rate of the model was determined to be 85.4%, indicating that the logistic regression model exhibited high performance in predicting the response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Conclusion: Our nomogram model stands out with high accuracy rates and the use of comprehensive markers in predicting the response to neoadjuvant chemotherapy in breast cancer patients. When compared with previous and current models in the literature, our model aligns with findings from both older and newer models and can be readily used as a valuable tool in clinical practice. Current models achieve higher accuracy rates by incorporating additional data such as genetic, imaging, and gene expression profiles. In the future, we believe that incorporating such data into our model will yield even better results. Keywords: laboratory parameters, breast cancer, nomogram, neoadjuvant chemotherapy