提出了一种基于DCE-MRI时间-强度曲线（TIC）剖面的体素级映射的无模型数据驱动方法，用于量化和可视化血流动力学异质性，并验证其潜在的临床应用。从2018年12月至2022年7月，回顾性纳入了259名行乳腺DCE-MRI检查的经病理证实的325个乳腺病变患者。根据手动分割的乳腺病变，将3D整个病变内每个体素的TIC基于灌注速率（非增强、缓慢、中等和快速）、洗出增强（持续、平台和下降）以及洗出稳定度（稳定和不稳定）进行分类，计算每个病变的这19个亚型的组成比例作为新的特征集（类型19）。利用三类型TIC分类、半定量参数和类型19特征构建机器学习模型，用于识别病变恶性程度并分类病理学分级、增殖状态和分子亚型。基于类型19特征的模型在区分病变恶性程度（分别为；AUC = 0.875 vs. 0.831，p = 0.01和0.875 vs. 0.804，p = 0.03），预测肿瘤增殖状态（AUC = 0.890 vs. 0.548，p = 0.006和0.890 vs. 0.596，p = 0.020）方面明显优于基于三类型TIC方法和半定量参数的模型，但在预测病理学分级方面没有明显差异（p = 0.820和0.970）。除常规方法外，提出的计算方法为量化和可视化血流动力学异质性提供了一种新颖的、无模型、数据驱动的方法。基于体素级内病变TIC剖面的映射可以实现对血流动力学异质性及其组成比例在恶性与良性乳腺病变鉴别中的直观可视化。• 进行了体素级TIC剖面的映射，并比较了各种乳腺病变之间的组成比例。• 基于体素级TIC剖面的组成比例的模型在乳腺病变的恶性程度鉴别和肿瘤增殖状态预测方面明显优于三类型TIC分类模型和半定量参数模型。• 这种新颖的数据驱动方法可以直观地可视化和量化乳腺病变的血流动力学异质性。© 2023年。作者授权欧洲放射学学会独家使用。

To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications.From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes.The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970).In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity.Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions.• Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.© 2023. The Author(s), under exclusive licence to European Society of Radiology.