|
[1]
|
Bray, F., Ferlay, J., Soerjomataram, I., et al. (2018) Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 68, 394-424. [Google Scholar] [CrossRef] [PubMed]
|
|
[2]
|
Siegel, R.L., Miller, K.D. and Jemal, A. (2019) Cancer Statistics, 2019. CA: A Cancer Journal for Clinicians, 69, 7-34. [Google Scholar] [CrossRef] [PubMed]
|
|
[3]
|
Goldhirsch, A., Winer, E.P., Coates, A.S., et al. (2013) Personalizing the Treatment of Women with Early Breast Cancer: Highlights of the St Gallen International Expert Consensus on the Pri-mary Therapy of Early Breast Cancer 2013. Annals of Oncology, 24, 2206-2223. [Google Scholar] [CrossRef] [PubMed]
|
|
[4]
|
Britt, K.L., Cuzick, J. and Phillips, K.A. (2020) Key Steps for Effective Breast Cancer Prevention. Nature Reviews Cancer, 20, 417-436. [Google Scholar] [CrossRef] [PubMed]
|
|
[5]
|
Phung, M.T., Tin Tin, S. and Elwood, J.M. (2019) Prognostic Models for Breast Cancer: A Systematic Review. BMC Cancer, 19, Article No. 230. [Google Scholar] [CrossRef] [PubMed]
|
|
[6]
|
Conti, A., Duggento, A., Indovina, I., et al. (2021) Radiomics in Breast Cancer Classification and Prediction. Seminars in Cancer Biology, 72, 238-250. [Google Scholar] [CrossRef] [PubMed]
|
|
[7]
|
Xie, T., Wang, Z., Zhao, Q., et al. (2019) Machine Learn-ing-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer. Frontiers in On-cology, 9, Article No. 505. [Google Scholar] [CrossRef] [PubMed]
|
|
[8]
|
Davey, M.G., Davey, M.S., Boland, M.R., et al. (2021) Radiomic Differentiation of Breast Cancer Molecular Subtypes Using Pre-Operative Breast Imaging—A Systematic Review and Meta-Analysis. European Journal of Radiology, 144, Article ID: 109996. [Google Scholar] [CrossRef] [PubMed]
|
|
[9]
|
Rizzo, S., Botta, F., Raimondi, S., et al. (2018) Radiomics: The Facts and the Challenges of Image Analysis. European Radiology Experimental, 2, Article No. 36. [Google Scholar] [CrossRef] [PubMed]
|
|
[10]
|
Pesapane, F., Suter, M.B., Rotili, A., et al. (2020) Will Traditional Biopsy Be Substituted by Radiomics and Liquid Biopsy for Breast Cancer Diagnosis and Characterisation? Medical On-cology, 37, Article No. 29. [Google Scholar] [CrossRef] [PubMed]
|
|
[11]
|
Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. (2012) Ra-diomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. European Journal of Cancer, 48, 441-446. [Google Scholar] [CrossRef] [PubMed]
|
|
[12]
|
Gillies, R.J., Kinahan, P.E. and Hricak, H. (2016) Radiomics: Im-ages Are More than Pictures, They Are Data. Radiology, 278, 563-577. [Google Scholar] [CrossRef] [PubMed]
|
|
[13]
|
Cui, Q., Dai, L., Li, J., et al. (2020) Accuracy of CEUS-Guided Sentinel Lymph Node Biopsy in Early-Stage Breast Cancer: A Study Review and Meta-Analysis. World Journal of Sur-gical Oncology, 18, Article No. 112. [Google Scholar] [CrossRef] [PubMed]
|
|
[14]
|
许荣, 欧阳秋芳, 林晴, 等. 超声影像组学预测雌激素及孕激素受体双阴性乳腺癌[J]. 中国医学影像技术, 2023, 39(9): 1346-1349.
|
|
[15]
|
Ferre, R., Elst, J., Senthilnathan, S., et al. (2023) Machine Learning Analysis of Breast Ultrasound to Classify Triple Negative and HER2+ Breast Cancer Sub-types. Breast Disease, 42, 59-66. [Google Scholar] [CrossRef]
|
|
[16]
|
Wu, J., Ge, L., Jin, Y., et al. (2022) Development and Validation of an Ultrasound-Based Radiomics Nomogram for Predicting the Luminal from Non-Luminal Type in Patients with Breast Carcinoma. Frontiers in Oncology, 12, Article ID: 993466. [Google Scholar] [CrossRef] [PubMed]
|
|
[17]
|
Wan, C.F., Liu, X.S., Wang, L., et al. (2018) Quantitative Con-trast-Enhanced Ultrasound Evaluation of Pathological Complete Response in Patients with Locally Advanced Breast Cancer Receiving Neoadjuvant Chemotherapy. European Journal of Radiology, 103, 118-123. [Google Scholar] [CrossRef] [PubMed]
|
|
[18]
|
Zhao, Y.X., Liu, S., Hu, Y.B., et al. (2017) Diagnostic and Prog-nostic Values of Contrast-Enhanced Ultrasound in Breast Cancer: A Retrospective Study. OncoTargets and Therapy, 10, 1123-1129. [Google Scholar] [CrossRef]
|
|
[19]
|
Gong, X., Li, Q., Gu, L., et al. (2023) Conventional Ultrasound and Contrast-Enhanced Ultrasound Radiomics in Breast Cancer and Molecular Subtype Diagnosis. Frontiers in Oncology, 13, Article ID: 1158736. [Google Scholar] [CrossRef] [PubMed]
|
|
[20]
|
Chen, S., Guan, X., Shu, Z., et al. (2019) A New Application of Multimodality Radiomics Improves Diagnostic Accuracy of Nonpalpable Breast Lesions in Patients with Microcalcifica-tions-Only in Mammography. Medical Science Monitor, 25, 9786-9793. [Google Scholar] [CrossRef]
|
|
[21]
|
万宏燕, 徐井旭, 杨瑜, 等. 基于X线摄影影像组学特征鉴别乳腺良恶性肿块的价值[J]. 医学影像学杂志, 2023, 33(5): 773-776.
|
|
[22]
|
Ge, S., Yixing, Y., Jia, D., et al. (2022) Application of Mammography-Based Radiomics Signature for Preoperative Prediction of Triple-Negative Breast Cancer. BMC Medical Imaging, 22, Article No. 166. [Google Scholar] [CrossRef] [PubMed]
|
|
[23]
|
Son, J., Lee, S.E., Kim, E.K., et al. (2020) Prediction of Breast Cancer Molecular Subtypes Using Radiomics Signatures of Synthetic Mammography from Digital Breast Tomosynthesis. Scientific Reports, 10, Article No. 21566. [Google Scholar] [CrossRef] [PubMed]
|
|
[24]
|
Ma, W., Zhao, Y., Ji, Y., et al. (2019) Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features. Academic Radiology, 26, 196-201. [Google Scholar] [CrossRef] [PubMed]
|
|
[25]
|
李佳蔚, 姜婷婷, 汤振伟, 等. 数字乳腺断层摄影的影像组学对乳腺癌分子分型预测研究[J]. 肿瘤影像学, 2023, 32(1): 12-19.
|
|
[26]
|
La Forgia, D., Fanizzi, A., Campobasso, F., et al. (2020) Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome. Diagnostics (Basel), 10, Article No. 708. [Google Scholar] [CrossRef] [PubMed]
|
|
[27]
|
Zhang, Y., Liu, F., Zhang, H., et al. (2021) Diagnostic Value of Radiomics Analysis in Contrast-Enhanced Spectral Mammography for Identifying Triple-Negative Breast Cancer. Fron-tiers in Oncology, 11, Article ID: 773196. [Google Scholar] [CrossRef] [PubMed]
|
|
[28]
|
Cardoso, F., Kyriakides, S., Ohno, S., et al. (2019) Early Breast Cancer: ESMO Clinical Practice Guidelines for Diagnosis, Treatment and Follow-Updagger. Annals of Oncology, 30, 1194-1220. [Google Scholar] [CrossRef] [PubMed]
|
|
[29]
|
张文, 何兰, 范志豪, 等. 基于术前分期CT的影像组学标签预测三阴性乳腺癌[J]. 放射学实践, 2019, 34(9): 947-951.
|
|
[30]
|
Wang, F., Wang, D., Xu, Y., et al. (2022) Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study. Frontiers in Oncology, 12, Article ID: 848726. [Google Scholar] [CrossRef] [PubMed]
|
|
[31]
|
Feng, Q., Hu, Q., Liu, Y., et al. (2020) Diagnosis of Triple Nega-tive Breast Cancer Based on Radiomics Signatures Extracted from Preoperative Contrast-Enhanced Chest Computed Tomography. BMC Cancer, 20, Article No. 579. [Google Scholar] [CrossRef] [PubMed]
|
|
[32]
|
Li, Q., Dormer, J., Daryani, P., et al. (2019) Radiomics Analysis of MRI for Predicting Molecular Subtypes of Breast Cancer in Young Women. Proceedings of SPIE—The International Society for Optical Engineering, Vol. 10950, 1095044. [Google Scholar] [CrossRef] [PubMed]
|
|
[33]
|
Wang, Q., Mao, N., Liu, M., et al. (2021) Radiomic Analysis on Magnetic Resonance Diffusion Weighted Image in Distinguishing Triple-Negative Breast Cancer from Other Subtypes: A Feasibil-ity Study. Clinical Imaging, 72, 136-141. [Google Scholar] [CrossRef] [PubMed]
|
|
[34]
|
Baysal, B., Baysal, H., Eser, M.B., et al. (2022) Radiomics Features Based on MRI-ADC Maps of Patients with Breast Cancer: Relationship with Lesion Size, Features Stability, and Model Accuracy. Medeniyet Medical Journal, 37, 277-288. [Google Scholar] [CrossRef]
|
|
[35]
|
Xu, A., Chu, X., Zhang, S., et al. (2022) Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study. Frontiers in Oncology, 12, Article ID: 799232. [Google Scholar] [CrossRef] [PubMed]
|
|
[36]
|
Huang, Y., Wei, L., Hu, Y., et al. (2021) Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer. Frontiers in Oncology, 11, Article ID: 706733. [Google Scholar] [CrossRef] [PubMed]
|
|
[37]
|
Zhang, S., Wang, X., Yang, Z., et al. (2022) Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study. Frontiers in Oncology, 12, Article ID: 905551. [Google Scholar] [CrossRef] [PubMed]
|
|
[38]
|
Kovacevic, L., Stajduhar, A., Stemberger, K., et al. (2023) Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Reso-nance Imaging Radiomics: A Retrospective Single-Center Study. Journal of Personalized Medicine, 13, Article No. 1150. [Google Scholar] [CrossRef] [PubMed]
|
|
[39]
|
Krajnc, D., Papp, L., Nakuz, T.S., et al. (2021) Breast Tumor Charac-terization Using [(18)F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics. Cancers (Basel), 13, Article No. 1249. [Google Scholar] [CrossRef] [PubMed]
|
|
[40]
|
Romeo, V., Kapetas, P., Clauser, P., et al. (2022) A Simultaneous Multiparametric (18)F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer. Cancers (Basel), 14, Article No. 3944. [Google Scholar] [CrossRef] [PubMed]
|