Neoadjuvant therapy (NAT) is increasingly employed as the primary treatment for patients with locally advanced breast cancer [1]. However, the efficacy of NAT varies substantially among individuals. Observations indicate that while 19% to 30% of patients may achieve a pathological complete response (pCR), 5% to 20% may experience disease progression [2]. Attaining pCR not only allows for less invasive surgery but also generally signifies a more favorable prognosis [3]. Nevertheless, despite pCR's clinical importance, standardized methods and imaging biomarkers for its precise prediction in current clinical practice are acutely absent. These are crucial for tailoring patient treatment regimens and improving quality of life.

The heterogeneity of breast tumors significantly contributes to the variability in NAT responses, accentuating the necessity for individualized treatment strategies to meet each patient's unique requirements [4,5]. Even among patients with comparable clinical profiles—such as clinical stage and molecular subtype—this heterogeneity necessitates more personalized predictive tools for treatment planning. In this milieu, the value of advanced imaging modalities, including functional MR imaging (e.g., diffusion-weighted imaging [DWI], dynamic contrast-enhanced [DCE] MRI), is clear. These modalities provide insights into tumor biology; the apparent diffusion coefficient (ADC) derived from DWI provides quantitative metrics that indicate the heterogeneity of cellular composition within tumors [6], while parameters from DCE-MRI like the time-signal intensity curve (TIC), maximum slope of increase (MSI), and signal enhancement ratio (SER) furnish dynamic assessments of tumor perfusion and vascular integrity [7,8]. Furthermore, alterations in background parenchymal enhancement (BPE) in response to NAT have demonstrated promise as a pCR indicator in contralateral normal breast tissue, suggesting broader applications of MRI in gauging therapeutic effectiveness [9].

Nomogram, as a sophisticated statistical tool, is generated via R software from a model exhibiting robust diagnostic capabilities and includes various lengthened lines. Each line symbolizes a distinct model parameter, with its length reflecting the parameter's significance to the model. A longer line denotes a greater impact. This tool quantifies predictive probabilities using a scale at the bottom, thereby facilitating an intuitive and visualized decision-making tool for clinical application.

Previous studies focusing on achieving pCR in breast cancer via NAT have largely centered on either clinical characteristics or tumor MRI features separately [10,11]. With the advent of artificial intelligence (AI) in medical monitoring, recent research has shifted to a comprehensive evaluation of imaging radiomic features and machine learning algorithms [[12], [13], [14], [15]]. However, BPE of the breast has been notably overlooked. Our study distinguishes itself by adopting a novel strategy that eschews complex research software and instead utilizes meaningful and straightforward parameters extracted directly from MRI scans, combined with BPE and clinical characteristics. This integration forms the basis of a user-friendly predictive nomogram, designed for intuitive implementation within clinical workflows and prospectively externally validated. By streamlining the decision-making process, this tool holds promise for improving patient outcomes. Notably, its practicality extends to diverse clinical settings, making it particularly well-suited for broad, multicenter research and seamless application by healthcare professionals.