Breast cancer is a biologically heterogeneous disease comprising distinct molecular subtypes—luminal A, luminal B, human epidermal growth factor receptor 2 (HER2) -enriched, and triple-negative (TN)—defined by immunohistochemical (IHC) markers including estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67. These markers are essential for diagnosis, treatment selection, and prognostic assessment. Therefore, noninvasive methods that can accurately characterize these subtypes and markers are crucial for optimizing personalized therapy.

Breast magnetic resonance imaging (MRI) is recommended by the American College of Radiology (ACR) as an important modality for breast cancer screening, diagnosis, and treatment monitoring (1). Diffusion-weighted imaging (DWI) is widely used for its sensitivity to tissue microstructure by detecting water molecule motion. Advanced diffusion models, such as diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM), provide additional microstructural and perfusion insights by varying gradient directions or b-values. However, these models assume a fixed diffusion time, and their quantitative parameters often yield overlapping values (e.g., ADC value) among molecular subtypes (2), limiting their ability to reflect specific microstructural features of tumor pathology.

Time-dependent diffusion MRI (T_d-DWI) overcomes this limitation by varying diffusion times or oscillation frequencies to probe water diffusion across temporal scales. Short diffusion times reflect restricted movement within intracellular or subcellular environments, while longer times are more sensitive to extracellular spaces and overall tissue architecture (3). Consequently, T_d-DWI can reveal structural features such as cell size, membrane permeability and extracellular matrix complexity (4), and has demonstrated high sensitivity to tumor microscopic pathological features at cellular and even subcellular scales in previous in vitro cellular and animal studies (5–7). Although applied in head and neck tumors (8), breast cancer (9) and prostate cancer (4), T_d-DWI’s clinical application in human tumors remains preliminary. Su et al. recently demonstrated its feasibility in differentiating benign from malignant breast lesions (10); however, its value in distinguishing molecular subtypes and IHC profiles is still underexplored.

A major motivation for this study arise from the recent refinement of the HER2 classification, with the emergence of HER2-low—defined as IHC 1+ or IHC 2+ without fluorescence in situ hybridization (FISH) amplification—as a distinct third category (11). HER2-low tumors, accounting for approximately 55% of breast cancers, are emerging as a therapeutically relevant subgroup, responding to novel HER2-targeted agents such as antibody-drug conjugates (11–13). These tumors also exhibit distinct biological and clinicopathologic characteristics compared to HER2-zero and HER2-positive cancers (13, 14). Accurate identification of HER2-low lesions is therefore essential but remains challenging. Conventional pathology-based classification relies on limited biopsy samples and is susceptible to variability in staining quality, reader interpretation, and tissue sampling—issues that may lead to misclassification, particularly among borderline HER2-low cases. These constraints highlight an unmet clinical for complementary, noninvasive imaging biomarkers capable of capturing whole-tumor biology and improving HER2 stratification (15). Besides, as global incidence of breast cancer continues to rise and pathology resources vary across medical settings, accessible imaging methods that enhance subtype detection could help support more equitable treatment decisions (16). At the same time, emerging HER2-targeted therapies place increasing demands on accurate subtype characterization, underscoring the technological importance of developing diffusion-based imaging tools that reflect tumor microstructure in vivo.

Therefore, this study aimed to investigate the potential of T_d-DWI–derived microstructural parameters in distinguishing molecular subtypes of breast cancer and in assessing their correlation with key immunohistochemical markers, with particular emphasis on the updated three-tier HER2 classification.

This study demonstrates that T_d-dMRI metrics—particularly ADC values obtained across different diffusion times and oscillation frequencies—along with microstructural parameters such as cellularity, cell diameter, and D_in, possess significant discriminatory potential for non-invasively characterizing breast cancer molecular subtypes and receptor status, including the recently emphasized HER2-low category. Notably, ADC_30ms achieved the highest diagnostic performance for distinguishing HER2-zero from HER2-low tumors (AUC = 0.898), while D_in, cellularity, and cell diameter were particularly effective in differentiating HER2-low from HER2-positive tumors, highlighting the strength of T_d-dMRI in addressing this emerging three-tier classification of HER2 expression. Additionally, ADC values and D_in showed consistent performance in differentiating ER, PR, and TN breast cancer status. Furthermore, no statistically significant differences in any T_d-dMRI metrics were found between high and low Ki-67 expression groups.

Compared to prior studies utilizing mono-exponential models or conventional DWI-derived ADC values—which have reported limited specificity in distinguishing molecular subtypes (24, 25)—our findings underscore the added value of T_d-dMRI. By varying diffusion times and oscillation frequencies, T_d-dMRI enables more nuanced probing of water diffusion, potentially enhancing sensitivity to microstructural differences. Furthermore, evidence from prior in vitro and animal studies indicated that T_d-dMRI can capture quantitative characterization of microstructure at length scales that are clinically relevant (e.g, 10 – 20 μm corresponding to many cell sizes), such as cell size, membrane permeability, and extracellular matrix organization (3, 7, 26). These foundational findings supported the biological plausibility of applying T_d-dMRI to quantify tumor microstructure in vivo. Consistent with previous reports that conventional ADC values tend to be lower in ER+ tumors and higher in TN and HER2-enriched subtypes (25, 27), our results not only indicated these trends but also revealed improved diagnostic granularity using T_d-dMRI. Unlike Liu et al. and Chang et al. studies (28–30), which reported limited ability of conventional ADC values to distinguish PR status, our study demonstrated that T_d-dMRI parameters, particularly D_in, can effectively differentiate PR+ from PR− breast cancers, potentially providing a novel noninvasive tool for predicting PR status. Notably, we suggested that ADC metrics obtained from specific diffusion conditions (e.g., ADC_30ms and ADC_50ms) outperformed conventional parameters in differentiating HER2-low from HER2-zero tumors, a recent subtype distinction not addressed in earlier DWI studies (11, 13). In addition, we indicated the microstructural parameters, such as cellularity and cell diameter, could also effectively distinguish HER2-low from HER2-positive tumors, further supporting HER2-low as a biologically and therapeutically distinct subtype (14). The findings suggested that T_d-dMRI might have the potential to capture microstructural differences that could not be detected by conventional diffusion imaging (4, 10).This study observed significant differences in multiple diffusion and microstructural features derived from T_d-dMRI among different breast cancer subtypes, which might be reasonably explained by underlying pathophysiological mechanisms. Among the four molecular subtypes, Luminal A tumors exhibited significantly higher ADC values compared to the relatively more aggressive Luminal B subtype. This difference might be due to the generally favorable prognosis and lower proliferative activity of Luminal A tumors, whereas Luminal B cancers typically displayed higher cellular density and reduced extracellular space, restricting water diffusion and consequently lowering ADC values. Similar results were observed in studies by Montemezzi et al. and Martincich et al. (31, 32). In addition, our analysis of clinical characteristics also revealed that Luminal A tumors had a significantly lower rate of lymph node metastasis than Luminal B and were more likely to present with an earlier T stage compared with HER2-eriched and TN subtypes. These trends aligned with the generally less aggressive biological behavior reported for Luminal A breast cancers and provided supportive context for the imaging-based associations observed in this study. Conversely, HER2-enriched breast cancers demonstrated higher ADC values (ADC_50ms, ADC_30ms, ADC_25Hz, ADC_50Hz), larger cell diameters, and lower cellularity compared to Luminal B, which were consistent with prior researches by Kim et al. and Wang et al. (9, 33). This pattern was likely attributed to the rapid proliferation and high metabolic activity characteristic of HER2-enriched tumors, which were more susceptible to intratumoral necrosis and cystic degeneration. Although ROI delineation in this study had avoided visibly identifiable cystic or necrotic regions, small areas of necrosis or cystic degeneration that were not easily detectable might still be present. These changes resulted in relatively less compact cellular architecture, thereby facilitating increased water diffusivity.

Our study also revealed that ER− and PR− tumors, including TN breast cancers, demonstrated significantly higher ADC values, larger cell diameters, and increased D_in, along with reduced cellularity. These findings might be attributed to the following fundamental differences in tumor biology and pathophysiological processes associated with hormone receptor status. Firstly, ER+ and PR+ tumors, typically well-differentiated and associated with the luminal subtypes, exhibited slower proliferation and responded to hormonal regulation of cell growth and apoptosis (34, 35). These features resulted in tightly packed, smaller cells with limited extracellular space, leading to physically restricted water diffusion and lower ADC and D_in values. In contrast, ER− and PR− tumors, especially TN subtypes, were high-grade, poorly differentiated, and highly proliferative, with disorganized architecture, larger pleomorphic cells. The absence of hormone receptor signaling led to unregulated growth that often outpaced the development of supportive stroma and vasculature, contributing to intratumoral hypoxia. This hypoxic environment promoted necrosis and cystic degeneration, which expanded extracellular space and reduced viable cell density, resulting in decreased cellularity and increased water diffusivity (35), reflected by elevated ADC and D_in values. Secondly, the elevated D_in values observed in ER− and PR− tumors might also be attributed to subcellular alterations characteristic of aggressive phenotypes. These included compromised membrane integrity, decreased cytoplasmic viscosity, and dysregulated ion channels with elevated intracellular calcium—all contributing to enhanced intracellular water mobility (36, 37). Additionally, enhanced aerobic glycolysis (Warburg effect) led to lactic acid accumulation and intracellular acidosis, which could alter water–membrane interactions and modulate membrane transporter activity (38, 39), further increasing water diffusivity. Thirdly, ER+ and PR+ tumors were frequently associated with a fibrotic or desmoplastic stromal reaction that further limited water diffusion. In contrast, ER− and PR− tumors, particularly TN cancers, typically presented with prominent lymphocytic infiltration and reduced stromal fibrosis (40–42). This might contribute to the looser tissue structure, larger intercellular spaces, and increased diffusivity observed in our studies.

In this study, T_d-dMRI–derived parameters did not differ significantly between high and low Ki-67 expression groups, which appeared inconsistent with some prior reports suggesting a correlation between diffusion metrics and proliferative activity (28). This discrepancy may stem from a mismatch between the biological role of Ki-67, a nuclear marker of proliferative activity, and the microstructural features probed by T_d-dMRI, such as cell size, extracellular architecture, and membrane properties. Furthermore, the relatively small sample size of each Ki-67 subgroup may have limited the statistical power to detect subtle microstructural differences, even if they were present.Another important consideration was the potential influence of tumor stage on the observed associations between T_d-dMRI parameters and molecular subtypes. Because Luminal A tumors in our study were predominantly T1 stage, while HER2-enriched and TN subtypes more frequently presented at later stages, part of the observed T_d-dMRI differences may be influenced by variations in tumor burden or microenvironmental remodeling associated with progression. Although our exploratory analysis did not identify significant differences in key T_d-dMRI parameters between early-stage (T1) and more advanced (≥ T2) lesions, we acknowledged that the influence of tumor stage cannot be fully excluded due to the limited sample size. Future studies with more balanced stage distributions and dedicated stage-adjusted analyses would be essential to validate subtype-specific imaging signatures.

A major highlight of this study was the findings that T_d-dMRI showed potential for distinguishing HER2-low tumors from both HER2-zero and HER2-positive breast cancers. In particular, T_d-dMRI-derived ADC metrics—most notably ADC_30ms and ADC_50ms—demonstrated promising discriminatory ability for separating HER2-zero from HER2-low tumors, with ADC_30ms yielding the highest AUC (0.898) in our cohort. In our study, HER2-zero tumors demonstrated significantly higher ADC values across multiple diffuse ion time scales (ADC_50ms, ADC_30ms, ADC_25Hz, ADC_50Hz) compared to HER2-low lesions. This likely resulted from differences in tissue architecture, despite similar cellularity and cell diameter between the two groups. HER2-low tumors might possess a denser extracellular matrix rich in fibrotic components and more compact cellular arrangements, leading to restricted intercellular water diffusion (43). Additionally, HER2-low tumors—though traditionally considered HER2-negative—might retain partial HER2-driven proliferative signaling, which could alter intracellular architecture, such as cytoskeletal organization or organelle density, thereby further reducing diffusivity (44). In contrast, HER2-zero tumors exhibited looser cell packing and a less fibrotic stroma, facilitating greater water mobility and resulting in higher ADC values. Interestingly, HER2-positive tumors exhibited higher ADC (ADC_50ms, ADC_30ms, ADC_25Hz, ADC_50Hz) values than HER2-low tumors despite their known aggressiveness and larger cell diameters. This seemingly paradoxical observation might be partially explained by pathophysiological features associated with HER2 overexpression. HER2-positive tumors have been reported to show upregulated vascular endothelial growth factor (VEGF)-mediated angiogenesis, elevated microvascular density and permeability, and a higher likelihood of intratumoral necrosis or cystic degeneration, which could contribute to the diffusion characteristics observed in this study (45, 46). Elevated perfusion and interstitial fluid may increase extracellular volume, partially counteracting the restrictive effects of high cellularity and thereby elevating ADC values. Moreover, aggressive HER2-positive tumors tend to outgrew their blood supply, resulting in central necrosis and cystic degeneration. Loss of viable cells and membrane breakdown in these regions further increases water mobility, contributing to higher ADC values. Collectively, these findings suggested that T_d-dMRI metrics can capture clinically relevant microstructural differences among HER2 expression subgroups and may assist in more refined imaging-based subtyping and therapeutic stratification, especially as HER2-low tumors become candidates for novel antibody-drug conjugates (e.g., trastuzumab deruxtecan).

This study had several limitations. First, it was conducted at a single institution with a relatively limited sample size, particularly in luminal A and HER2-zero breast cancers, and further validation in larger, multi-center cohorts should be warranted. Second, due to the resolution and signal-to-noise constraints inherent to T_d-dMRI and considering that larger tumors better reflect underlying biological characteristics and intratumoral heterogeneity, this study included only lesions greater than 1 cm in diameter to enhance measurement accuracy. Third, although T_d-dMRI parameters were interpreted in the context of known breast cancer microstructural features, their biological underpinnings remain inferential rather than directly validated. Future studies should correlate T_d-dMRI metrics with more comprehensive histological analyses (e.g., quantitative assessment of cellularity, stromal fraction from whole-slide digital pathology) to establish a more robust biological basis for these imaging biomarkers. Finally, this study focused on pre-treatment imaging data. Longitudinal assessment of T_d-DWI changes during or after therapy may offer additional value in predicting treatment response or prognosis and should be explored in future studies.

In conclusion, we demonstrated the diagnostic potential of T_d-dMRI metrics—including ADC values derived under varying diffusion times and oscillation frequencies, as well as microstructural parameters such as D_in, cellularity, and cell diameter— in differentiating breast cancer molecular subtypes and capturing underlying tumor biology and microstructural heterogeneity associated with hormone receptor and HER2 status. T_d-dMRI showed promise as a noninvasive tool for subtype characterization and might offer auxiliary value in stratifying patients, guiding treatment decisions, and predicting tumor behavior in breast cancer. However, further validation in larger, multicenter, and longitudinal studies was warranted.