Introduction

Neoadjuvant chemotherapy (NAC) is a fundamental component of treatment for early-stage breast cancer. Achieving a pathological complete response (pCR) after NAC has been consistently linked with superior long-term clinical outcomes, as evidenced by numerous studies [1,2,3]. In addition to serving as a treatment endpoint, early assessment during NAC is crucial for guiding clinical decisions and tailoring treatment strategies [4]. NAC generally involves several cycles, leading to various patterns of tumor shrinkage. Among these, concentric shrinkage (CS) is associated with a better prognosis and, in addition to pCR, may serve as an independent predictive factor for patient outcomes [5]. Nonetheless, the relationship between specific response patterns and prognosis, particularly among patients who do not achieve a pCR, is still not well understood. Elucidating the heterogeneity of these responses is essential for evaluating their prognostic value and improving treatment approaches for breast cancer patients.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a highly specific and sensitive [30]. Our model capitalizes on these early treatment stages to provide critical prognostic information. This early prediction model holds promise for optimizing therapeutic regimens and advancing patient care by enabling clinicians to make more informed and timely decisions about treatment efficacy.

In our analysis, we delineated gene signaling pathways correlated with distinct patterns of treatment response. Genes such as BCL2 and ATF6B are involved in these pathways. Previous studies have demonstrated that downregulation of BCL2 correlates with improved disease-free survival (DFS) in breast cancer patients [31]. Additionally, the upregulation of ATF6B expression has been linked to favorable DFS outcomes [32]. Additionally, elevated PLA2G16 gene expression has been correlated with improved DFS, consistent with findings in the literature [33]. Our investigation not only confirmed a gradation of expression for these genes across patients who achieved pCR, nonpCR with CS, and nonpCR without CS but also aligned with a corresponding gradation in imaging feature changes observed in these groups. This correspondence underlines the credibility of radiomic signatures, such as sphericity, which is indicative of a tumor’s roundness and was found to be associated with a pCR and a CS pattern.

Our genomic analyses confirmed that survival differences among the distinct response pattern groups, as predicted by our model, are underpinned by biological variations. Notably, enrichment of the IL-17 signaling pathway aligns with its known contribution to the invasive progression of breast cancer [34, 35]. This observation is corroborated by evidence showing that IL-1β-induced IL-17 production by γδ T cells drives G-CSF-dependent neutrophil expansion and polarization in breast tumor models, processes critical for disease progression [36]. The complexity of ER signaling has been emphasized [37], with disruptions in ER cofactors and nongenomic mechanisms implicated in the metastasis of ER-positive breast cancer cells [28]. This discovery highlights the critical role of the estrogen pathway in the treatment of hormone-sensitive cancers and in the development of novel drug therapies [38].

Consistent with previous research findings, the extracellular matrix-receptor interaction pathway was notably prominent. This pathway included differentially expressed genes such as those in the THBS family, along with collagen and fibronectin genes, all of which are crucial in breast cancer pathogenesis [39]. These insights provide a valuable understanding of the molecular framework that dictates tumor behavior and the effectiveness of therapeutic interventions.

Our study has several limitations. First, the inclusion of diverse histopathological cancer subtypes, although representative of the clinical spectrum, adds variability to the chemotherapy protocols used. This variability may affect the pCR rate following NAC, potentially introducing selection bias. Second, the use of imaging data from multiple sources with differing imaging protocols may lead to biases in the development of the model, possibly affecting its predictive accuracy and applicability in various clinical settings. To mitigate these issues, future studies should focus on standardizing imaging protocols and taking histopathological variability into account. Such measures would enhance the validation process of the model, ensuring its dependability and usefulness across a wider range of clinical situations.

In summary, our investigation highlights the potential of noninvasive imaging as a prognostic tool for predicting responses to NAC and tumor shrinkage patterns. The study indicated that CS patterns correlate with better survival, particularly in patients who are less likely to reach a pCR. Additionally, gene expression analyses revealed distinct oncogenic pathways associated with various response patterns. These findings support the utility of imaging biomarkers in predicting therapeutic outcomes, emphasizing the role of radiomics in refining early prognosis and enabling personalized therapy.