| [1] |
Wild AE, Weiderpass E, Stewart BW, et al. World cancer report: Cancer research for cancer prevention [M]. Lyon: International Agency for Research on Cancer, 2020：25-26.
|
| [2] |
DeSantis CE, Ma J, Gaudet MM, et al. Breast cancer statistics, 2019 [J]. CA Cancer J Clin, 2019，69(6)：438-451.
|
| [3] |
Chen W, Zheng R, Baade PD, et al. Cancer statistics in china, 2015 [J]. CA Cancer J Clin, 2016，66(2)：115-132.
|
| [4] |
Xia C, Dong X, Li H, et al. Cancer statistics in china and united states, 2022: profiles, trends, and determinants [J]. Chin Med J (Engl), 2022，135(5)：584-590.
|
| [5] |
Ding R, Xiao Y, Mo M, et al. Breast cancer screening and early diagnosis in Chinese women [J]. Cancer Biol Med, 2022，19(4)：450-467.
|
| [6] |
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis [J]. Eur J Cancer, 2012，48(4)：441-446.
|
| [7] |
Lambin P, Leijenaar R, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine [J]. Nat Rev Clin Oncol, 2017，14(12)：749-762.
|
| [8] |
Paget S. The distribution of secondary growths in cancer of the breast. 1889 [J]. Cancer Metastasis Rev, 1989，8(2)：98-101.
|
| [9] |
Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing [J]. N Engl J Med, 2012，366(10)：883-892.
|
| [10] |
Tekpli X, Lien T, Røssevold AH, et al. An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment [J]. Nat Commun, 2019，10(1)：5499.
|
| [11] |
Bennani-Baiti B, Pinker K, Zimmermann M, et al. Non-invasive assessment of hypoxia and neovascularization with MRI for identification of aggressive breast cancer [J]. Cancers, 2020，12(8)：2024.
|
| [12] |
Braman N, Prasanna P, Whitney J, et al. Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)-positive breast cancer [J]. JAMA Netw Open, 2019，2(4)：e192561.
|
| [13] |
Zhou J, Zhang Y, Chang KT, et al. Diagnosis of benign and malignant breast lesions on dce-mri by using radiomics and deep learning with consideration of peritumor tissue [J]. J Magn Reson Imaging, 2020，51(3)：798-809.
|
| [14] |
Lee HJ, Nguyen AT, Ki SY, et al. Classification of MR-detected additional lesions in patients with breast cancer using a combination of radiomics analysis and machine learning [J]. Front Oncol, 2021，11：744 460.
|
| [15] |
肖冰冰，袁刚，郑健，等. 基于融合双模态超声瘤内瘤周影像的乳腺肿瘤分类 [J]. 生物医学工程研究，2021，40(2)：138-143.
|
| [16] |
Li C, Song L, Yin J. Intratumoral and peritumoral radiomics based on functional parametric maps from breast DCE-MRI for prediction of HER-2 and Ki-67 status [J]. J Magn Reson Imaging, 2021，54(3)：703-714.
|
| [17] |
Li C, Yin J. Radiomics nomogram based on radiomics score from multiregional diffusion-weighted MRI and clinical factors for evaluating HER-2 2+ status of breast cancer [J]. Diagnostics (Basel), 2021，11(8)：1491.
|
| [18] |
Jiang T, Song J, Wang X, et al. Intratumoral and peritumoral analysis of mammography, tomosynthesis, and multiparametric MRI for predicting Ki-67 level in breast cancer: a radiomics-based study [J]. Mol Imaging Biol, 2022，24(4)：550-559.
|
| [19] |
陆欢，葛敏，王世威，等. 动态增强MRI瘤内与瘤周影像组学特征对三阴性乳腺癌的诊断价值研究 [J]. 浙江医学，2021，43(15)：1647-1651; 1647-1651+1710.
|
| [20] |
李宝明. 基于影像组学的三阴性乳腺癌分子亚型预测[D]. 南京信息工程大学，2020.
|
| [21] |
Niu S, Jiang W, Zhao N, et al. Intra- and peritumoral radiomics on assessment of breast cancer molecular subtypes based on mammography and MRI [J]. J Cancer Res Clin Oncol, 2022，148(1)：97-106.
|
| [22] |
Qi TH, Hian OH, Kumaran AM, et al. Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer [J]. Breast Cancer Res Treat, 2022，193(1)：121-138.
|
| [23] |
周佳丽. 基于MRI影像组学分析对乳腺癌新辅助化疗病理缓解早期术前预测[D]. 浙江中医药大学，2019.
|
| [24] |
Hussain L, Huang P, Nguyen T, et al. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response [J]. Biomed Eng Online, 2021，20(1)：63.
|
| [25] |
Huang X, Mai J, Huang Y, et al. Radiomic nomogram for pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer: predictive value of staging contrast-enhanced CT [J]. Clin Breast Cancer, 2021，21(4)：e388-e401.
|
| [26] |
Braman NM, Etesami M, Prasanna P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI [J]. Breast Cancer Res, 2017，19(1)：57.
|
| [27] |
王雷. 基于影像组学的乳腺癌新辅助化疗疗效预测[D]. 南京：南京信息工程大学，2021.
|
| [28] |
Mao N, Shi Y, Lian C, et al. Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography [J]. Eur Radiol, 2022，32(5)：3207-3219.
|
| [29] |
Liu C, Ding J, Spuhler K, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI [J]. J Magn Reson Imaging, 2019, 49(1)：131-140.
|
| [30] |
Ding J, Chen S, Serrano Sosa M, et al. Optimizing the peritumoral region size in radiomics analysis for sentinel lymph node status prediction in breast cancer [J]. Acad Radiol, 2022，29 Suppl 1(Suppl 1)：S223-S228.
|
| [31] |
邓鹏飞. 基于DCE-MRI影像组学的乳腺癌区域淋巴结转移预测方法研究[D]. 西北大学，2020.
|
| [32] |
Sun Q, Lin X, Zhao Y, et al. Deep learning vs. Radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don’t forget the peritumoral region [J]. Front Oncol, 2020，10：53.
|
| [33] |
Obeid JP, Stoyanova R, Kwon D, et al. Multiparametric evaluation of preoperative MRI in early stage breast cancer: prognostic impact of peri-tumoral fat[J]. Clin Transl Oncol, 2017，19(2)：211-218.
|
| [34] |
Han X, Cao W, Wu L, et al. Radiomics assessment of the tumor immune microenvironment to predict outcomes in breast cancer [J]. Front Immunol, 2021，12：773581.
|
| [35] |
Yu F, Hang J, Deng J, et al. Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study [J]. Br J Radiol, 2021，94(1126)：20210188.
|
| [36] |
李晓虹. 基于MRI影像组学的乳腺癌远处转移风险评估模型构建与验证[D]. 南方医科大学，2021.
|
| [37] |
Xu H, Liu J, Chen Z, et al. Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer [J]. Eur Radiol, 2022，32(7)：4845-4856.
|