Abstract：Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis and treatment of breast cancer. Among its modalities, dynamic contrast-enhanced MRI (DCE-MRI), apparent diffusion coefficient (ADC) imaging, and T2-weighted imaging (T2WI) provide comprehensive imaging information that offers valuable insights for predicting the pathological characteristics of breast cancer. In this study, we integrated multi-parametric imaging features derived from DCE-MRI, ADC, T2WI, delayed-phase imaging, DCE-MRI-based tumor subregions, tumor pharmacokinetics, and quantitative background parenchymal enhancement (BPE) features to conduct predictive analyses on both multi-ethnic classification and Ki-67 expression levels. A total of 781 breast cancer cases were collected. Following preprocessing, tumor regions and fibroglandular tissue (FGT) on multi-parametric MR images were delineated. Tumor subregions were segmented using pixel-based enhancement clustering into three enhancement patterns. Radiomics features were extracted from both tumors and FGT. In addition, pharmacokinetic features of the tumors on DCE-MRI, the percentage of FGT volume within the entire breast (%BPEF/B) and the proportion of enhancing FGT volume exceeding different signal intensity thresholds within the FGT (%BPEF/F) were calculated. An unsupervised feature selection strategy was employed to eliminate irrelevant features. A genetic algorithm combined with cross-validation was used to identify the optimal feature subset. A random forest classifier was constructed for prediction, and model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and ROC curve plotting. Furthermore, a feature-level fusion approach was adopted to integrate radiomic, kinetic, and BPE features, thereby constructing a multi-parametric imaging fusion model. In single-modality models for the multi-ethnic classification task (691 Han, 70 Tibetan, and 20 Yi patients), ADC images achieved the best AUCs of 0.841±0.024, 0.819±0.031, and 0.775±0.092 for Han, Tibetan, and Yi ethnic groups, respectively. For the Ki-67 expression prediction task (103 low expression, 498 high expression cases), the Fast Flow subregion yielded the best AUC of 0.756, with a sensitivity of 0.845 and specificity of 0.684. Upon fusion of multi-parametric imaging and BPE features, the AUCs for the multi-ethnic prediction task improved to 0.909±0.027 (Han), 0.890±0.031 (Tibetan), and 0.863±0.070 (Yi), which improved by 8.1%, 8.7%, and 11.3%, respectively compared to the best single-parameter model. For Ki-67 prediction, the fusion model incorporating ADC, DCE-MRI, and BPE features yielded an improved AUC of 0.770, with sensitivity and specificity increasing to 0.866 and 0.728, respectively. In summary, the integration of multi-parametric MRI features, quantitative BPE metrics, and tumor subregion enhancement patterns provides a more comprehensive characterization of breast cancer imaging and underlying biology. This approach enhances diagnostic accuracy and offers valuable guidance for personalized treatment and prognostic assessment in breast cancer patients across different ethnic groups.
刘家保, 范明, 厉力华. 基于多参数磁共振影像的多民族病理信息预测研究[J]. 中国生物医学工程学报, 2026, 45(1): 47-60.
Liu Jiabao, Fan Ming, Li Lihua. Prediction of Multi-Ethnic Pathological Information Based on Multi-Parameter MRI. Chinese Journal of Biomedical Engineering, 2026, 45(1): 47-60.
[1] 刘小娟. 磁共振成像在乳腺癌诊断中的应用 [J]. 实用放射学杂志, 2006, 22: 483-486. [2] 张春灵, 王宁邴, 雪姚建, 等. MRI影像组学在乳腺癌诊疗中的应用进展 [J]. 影像诊断与介入放射学, 2022, 31: 377-381. [3] 张雪, 董晓平, 管雅喆, 等. 女性乳腺癌流行病学趋势及危险因素研究进展 [J]. 肿瘤防治研究, 2021, 48: 87-92. [4] Davey MG, Hynes SO, Kerin MJ, et al. Ki-67 as a prognostic biomarker in invasive breast cancer [J]. Cancers (Basel), 2021, 13(17): 2072-6694. [5] Lashen AG, Toss MS, Ghannam SF, et al. Expression, assessment and significance of Ki67 expression in breast cancer: an update [J]. J Clin Pathol, 2023, 76(6): 357-364. [6] Weigel MT, Dowsett M. Current and emerging biomarkers in breast cancer: prognosis and prediction [J]. Endocr Relat Cancer, 2010, 17(4): R245-262. [7] 杨丽丽,梁莉萍,李景英,等. 新疆维吾尔族与汉族女性多中心性乳腺癌患者临床病理分析[J]. 肿瘤防治研究, 2014, 41(4): 362-365. [8] Kong X, Liu Z, Cheng R, et al. Variation in breast cancer subtype incidence and distribution by race/ethnicity in the United States from 2010 to 2015 [J]. JAMA Netw Open, 2020, 3(10): e2020303. [9] Pinker K, Helbich HT, Morris EA. The potential of multiparametric MRI of the breast [J]. The British Journal of Radiology, 2017, 90(1069): 20160715. [10] Fan Ming, He Ting, Zhang Peng, et al. Heterogeneity of diffusion-weighted imaging in tumours and the surrounding stroma for prediction of Ki-67 proliferation status in breast cancer [J]. Scientific Reports, 2017, 7(1): 2875. [11] Xing Hua, Song Changlong, Li Wenjia. Meta analysis of lymph node metastasis of breast cancer patients: clinical value of DWI and ADC value [J]. European Journal of Radiology, 2016, 85(6): 1132-1137. [12] Coffey K, Sung J, Comstock C, et al. Utility of targeted ultrasound to predict malignancy among lesions detected on contrast-enhanced digital mammography [J]. AJR Am J Roentgenol, 2021, 217(3): 595-604. [13] 路红, 季宇, 张迎, 等. 多参数磁共振成像在乳腺癌新辅助化疗中的应用研究进展 [J]. 中国肿瘤临床, 2021, 48: 1056-1060. [14] Xiao J, Rahbar H, Hippe DS, et al. Dynamic contrast-enhanced breast MRI features correlate with invasive breast cancer angiogenesis [J]. NPJ Breast Cancer, 2021, 7(1): 42. [15] 谢春, 李作瑞, 郭佑民. 不同海拔地区浸润性乳腺癌女性患者MRI动态增强扫描血流动力学参数的差异 [J]. 广西医学, 2023, 3: 277-280. [16] 张承杰, 厉力华. 基于空间 FCM 与 MRF 方法的乳腺 MRI 序列三维病灶分割研究 [J]. 中国生物医学工程学报, 2014, 33: 202-211. [17] Abe H, Mori N, Tsuchiya K, et al. Kinetic analysis of benign and malignant breast lesions with ultrafast dynamic contrast-enhanced MRI: comparison with standard kinetic assessment [J]. American Journal of Roentgenology, 2016, 207(5): 1159-1166. [18] Xie Tianwen, Zhao Qiufeng, Fu Caixia, et al. Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis [J]. European Journal of Radiology, 2024, 176: 111501. [19] Varatharaj A, Liljeroth M, Darekar A, et al. Blood-brain barrier permeability measured using dynamic contrast-enhanced magnetic resonance imaging: a validation study [J]. The Journal of Physiology, 2019, 597(3): 699-709. [20] Qi YJ, Su GH, You C, et al. Radiomics in breast cancer: current advances and future directions [J]. Cell Rep Med, 2024, 5(9): 101719. [21] 李艳, 彭嘉雄. 多尺度 H 参数纹理特征的子带算法 [J]. 电子学报, 2002, 30: 1041-1043. [22] 马明明, 张耀峰, 王祥鹏, 等. 基于深度学习和阈值分割方法对乳腺 MRI 中背景实质强化自动分类的研究 [J]. 肿瘤影像学, 2021, 30: 332-338. [23] Chen JH, Yu HJ, Hsu C, et al. Background parenchymal enhancement of the contralateral normal breast: association with tumor response in breast cancer patients receiving neoadjuvant chemotherapy [J]. Transl Oncol, 2015, 8(3): 204-209. [24] Kerlikowske K, Chen S, Bissell MCS, et al. Population attributable risk of advanced-stage breast cancer by race and ethnicity [J]. JAMA Oncol, 2024, 10(2): 167-175. [25] Schmid VJ, Whitcher B, Padhani AR, et al. Quantitative analysis of dynamic contrast-enhanced MR images based on Bayesian P-splines [J]. IEEE Transactions on Medical Imaging, 2008, 28(6): 789-798. [26] Wu SD, Berg WA, Zuley ML, et al. Breast MRI contrast enhancement kinetics of normal parenchyma correlate with presence of breast cancer [J]. Breast Cancer Res, 2016, 18(1): 76. [27] Arefan D, Zuley ML, Berg WA, et al. Assessment of background parenchymal enhancement at dynamic contrast-enhanced MRI in predicting breast cancer recurrence risk [J]. Radiology, 2024, 310(1): e230269. [28] Calderón-Gerstein WS, Torres-Samaniego G. High altitude and cancer: an old controversy [J]. Respiratory Physiology & Neurobiology, 2021, 289: 103655. [29] 娄潇方, 范明, 许茂盛, 等. 基于多参数磁共振影像组学的乳腺癌病理信息预测模型研究 [J]. 中国生物医学工程学报, 2020, 39: 513-523. [30] Fan Ming, Yuan Wei, Zhao Wenrui, et al. Joint prediction of breast cancer histological grade and Ki-67 expression level based on DCE-MRI and DWI radiomics [J]. IEEE J Biomed Health Inform, 2020, 24(6): 1632-1642.
