• The tumor is divided into three subregions: persistent, washout, and plateau.

Breast cancer exhibits molecular heterogeneity (Barzaman et al., 2020; Yeo and Guan, 2017; Liu et al., 2022), encompassing distinct subtypes such as Luminal A, Luminal B, HER2-enriched, and Basal-like. Each molecular subtype manifests unique pathological characteristics, dictating tailored treatment strategies and prognoses (Cheang et al., 2009; Prat et al., 2015; McCart Reed et al., 2021; Parker et al., 2009; Bitencourt et al., 2020). Luminal-type breast cancer demonstrates sensitivity to endocrine therapy but resistance to chemotherapy. HER2-positive breast cancer exhibits high invasiveness and recurrence rates, yet displays a favorable response to chemotherapy. Basal-like breast cancer presents the highest propensity for recurrence and metastasis, resulting in a poorer prognosis. Hence, accurate prediction of the molecular subtypes and HER2 status of breast cancer holds paramount importance.

Radiomics refers to the high-throughput extraction of quantitative imaging features that can reveal disease characteristics invisible to human visual assessment. By converting medical images into mineable data using advanced computational analysis, radiomics allows detection of subtle patterns that may correlate with underlying pathophysiology (Gillies et al., 2016). DCE-MRI serves as a non-invasive imaging technique that offers clear delineation of the intricate shape of breast tumors, unveils dynamic enhancements within the tumor, and elucidates the characteristics of tumor angiogenesis and heterogeneity (Yu et al., 2020). Numerous studies have demonstrated the diagnostic efficacy of the radiomics features within whole tumors derived from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and DCE-MRI or their quantitative images in predicting molecular types (Xie et al., 2019; Leithner et al., 2020; Agner et al., 2014; Chaudhury et al., 2015; Mazurowski et al., 2014). The interior of breast cancer exhibits heterogeneity and often showcases a combination of distinct kinetic dynamics that reflect the diverse biological behaviors of tumors. However, previous studies often overlook the intra-tumoral heterogeneity, leading to an inaccurate reflection of the true degree of heterogeneity within the tumor and potential deviations in patient follow-up evaluation.

Computer-aided diagnosis (CAD) is an automated software designed to analyze DCE-MRI images obtained by high spatial resolution scans, thereby reducing interpretation time (Lehman et al., 2006; Williams et al., 2007; Meeuwis et al., 2010). CAD automatically segments the tumor into three distinct color-coded subregions, corresponding to three kinetic modes: Persistent, Washout, and Plateau, while concurrently generating quantitative parameters of kinetic characteristics. Among these, the washout region is thought to primarily represent angiogenesis and contrast agent kinetic destruction in the tumor. Jin You Kim et al. observed a correlation between kinetic heterogeneity determined by CAD and disease-free survival as well as distant metastasis in breast cancer patients (Kim et al., 2020; Kim et al., 2017). However, it remains unclear whether intra-tumoral kinetic heterogeneity derived from CAD can be utilized for molecular subtype classification and whether it offers superior effectiveness compared to whole-tumor region analysis.

Therefore, this study aims to utilize the CAD method and extract radiomics features of the kinetic heterogeneity regions of breast cancer. These features will be employed to construct a radiomics model to explore its performance in predicting breast cancer molecular subtypes and HER2 status. Furthermore, these models will be compared with those constructed using features from the entire tumor.

This study introduces a novel approach utilizing kinetic heterogeneity analysis based on DCE-MRI to segment intra-tumoral subregions into three distinct categories: persistent enhancement, washout, and plateau. Subsequently, radiomics models were developed leveraging features extracted from both the washout region and the whole tumor region. These models aim to predict molecular subtypes and HER2 status in breast cancer and assess their clinical efficacy. Our findings indicate that the diagnostic efficacy of the radiomics model based on the washout region within the tumor surpasses that of the whole-tumor model, particularly in predicting Luminal subtypes. Notably, the diagnostic performance is highest with an AUC of 0.924 for the training set and 0.853 for the test set.

Previous studies have demonstrated the clinical significance of radiomics analysis in distinguishing benign and malignant breast lesions, as well as in differentiating various histopathological types, grades, and stages of breast tumors (Braman et al., 2017; Li et al., 2016; Zhou J. et al., 2020; Mao et al., 2020; Ma et al., 2018; Liu et al., 2019). However, most of these studies focused on constructing radiomics models using the entire tumor area. In contrast, our approach involves analyzing the internal tumor heterogeneity and segmenting tumors based on kinetic heterogeneity. Segmenting and modeling tumors into sub-regions is crucial for accurately capturing internal heterogeneity. Breast cancer masses exhibit high heterogeneity, with various features mixed within the tumor. Focusing on the entire tumor region dilutes this heterogeneity and may weaken model performance. In contrast, the washout region, which best reflects tumor malignancy, allows for a more precise analysis of tumor heterogeneity.

The washout pattern is strongly associated with tumor angiogenesis and vascular permeability (Kuhl et al., 1999). Rapid contrast agent washout reflects immature, leaky tumor vasculature with high endothelial proliferation, characteristic of more aggressive tumors (Knopp et al., 1999). From a microenvironment perspective, washout regions correlate with areas of hypoxia and extracellular matrix remodeling, which promote epithelial-mesenchymal transition and metastatic potential (Li and Padhani, 2012; Pickles et al., 2005). At the molecular level, washout regions show elevated expression of vascular endothelial growth factor (VEGF) and other pro-angiogenic factors that drive the HER2 and basal-like phenotypes.

Recently, alternative data-driven methods have been employed to delineate tumor subregions, such as pharmacokinetic models and habitat analysis models (Liu et al., 2020; Kim et al., 2016; Xu et al., 2024). Studies have demonstrated that the Tofts model can assess pharmacokinetic parameters and analyze the vascular permeability of tumors (Zhou X. et al., 2020; Mouawad et al., 2020; Ioannidis et al., 2019). While pharmacokinetic models like the Tofts model provide quantitative parameters of vascular permeability, they require high temporal resolution (5–10 s) acquisitions, which are not routinely available in clinical practice (Sourbron and Buckley, 2013). Our approach using standard high spatial resolution DCE-MRI makes the technique more clinically feasible. Zhou X et al. diagnosed breast cancer and predicted molecular subtypes using pharmacokinetic dynamically-enhanced (PK-DCE) MRI in the Tofts model (Zhou X. et al., 2020). They found PK-DCE MRI to be superior in breast cancer diagnosis but less effective in predicting molecular subtypes, achieving AUCs of 0.71 ∼ 0.77 for predicting the Luminal subtype and 0.61 ∼ 0.68 for predicting the HER2-enriched subtype. In comparison, our model achieved higher AUCs of 0.924 for Luminal and 0.879 for HER2-enriched subtypes.

In recent years, with the rapid advancement of artificial intelligence, researchers have explored methods such as habitat analysis and unsupervised learning to be used in breast cancer (Sun et al., 2022; Twellmann et al., 2008; Cho et al., 2022). Jia Wu et al. performed molecular subtype prediction by clustering analysis of quantitative image features (Wu et al., 2017). Ming Fan et al. attempted tumor classification based on values of Time to Peak, Peak Enhancement Ratio, and Kinetic Pattern Clustering (Fan et al., 2018). They also applied unsupervised clustering analysis to decompose time series curves at the pixel level into specific regions for plasma input, fast-flow dynamics, and slow-flow dynamics (Fan et al., 2019). Their results consistently showed that intra-tumor radiomics analysis improves the predictive performance compared to the whole tumor approaches, with AUCs ranging from 0.74 to 0.88. Zhang et al. similarly reported significant diagnostic enhancements, particularly for Luminal subtype analysis (Zhang et al., 2022), aligning closely with our findings. These studies suggest that the model based on subregional radiomics features for predicting the Luminal subtype may serve as a more valuable imaging marker.

Although unsupervised clustering methods can identify tumor subregions, the biological interpretation of these clusters may be ambiguous (Wu et al., 2017; Leithner et al., 2018). Our method’s clear delineation into three physiologically meaningful regions (persistent, washout, plateau) provides more clinically intuitive results. The CAD software used in our study offers automated quantitative analysis with minimal user intervention, enhancing reproducibility in clinical settings compared to more complex AI methods that may require specialized expertise.

Our research has several limitations. Firstly, it is a retrospective study conducted at two local hospitals, which may introduce selection bias and geographical limitations in the study population. To enhance generalizability and interpretability before clinical application, data should be collected from a prospective multicenter standardized database. Secondly, the study did not account for the effects of the menstrual cycle and hormone levels, nor did it consider the tumor microenvironment. Thirdly, our study excluded TNBC cases due to limited sample size, which represents a significant limitation given the clinical importance of this aggressive subtype. TNBC typically demonstrates more rapid and pronounced washout kinetics than other subtypes (Youk et al., 2012), suggesting our model might perform differently for these cases. Future studies should specifically evaluate whether washout region features have particular value in characterizing TNBC, which could help address the current lack of targeted therapies for this subtype (Bianchini et al., 2016). Additionally, we aim to integrate multi-parametric and multi-modal imaging with genomics and genomics analysis to develop higher-dimensional, multi-scale, and more efficient diagnostic models.

In conclusion, this study explores a novel non-invasive tumor segmentation method using high spatial resolution DCE-MRI images. It analyzes tumor kinetic heterogeneity by segmenting the sub-tumor region into three distinct areas. Features from the washout region within the tumor were extracted to construct a radiomics model for predicting molecular subtypes. The findings indicate that radiomics features of the washout region derived from kinetic heterogeneity analysis may serve as predictive markers for molecular subtypes, particularly in predicting Luminal subtypes. Visualizing tumor heterogeneity in this way could potentially enhance treatment precision and personalization for breast cancer patients.