[1]
|
Hamy, A.S., Belin, L., Bonsang-Kitzis, H., et al. (2016) Pathological Complete Response and Prognosis after Neoadju-vant Chemotherapy for HER2-Positive Breast Cancers before and after Trastuzumab Era: Results from a Real-Life Co-hort. British Journal of Cancer, 114, 44-52. https://doi.org/10.1038/bjc.2015.426
|
[2]
|
Kuerer, H.M., Newman, L.A., Smith, T.L., et al. (1999) Clinical Course of Breast Cancer Patients with Complete Pathologic Primary Tumor and Axillary Lymph Node Response to Doxorubicin-Based Neoadjuvant Chemotherapy. Journal of Clinical Oncology, 17, 460. https://doi.org/10.1200/JCO.1999.17.2.460
|
[3]
|
von Minckwitz, G., Untch, M., Blohmer, J.U., et al. (2012) Definition and Impact of Pathologic Complete Response on Prognosis after Neoadjuvant Chemotherapy in Various In-trinsic Breast Cancer Subtypes. Journal of Clinical Oncology, 30, 1796-1804. https://doi.org/10.1200/JCO.2011.38.8595
|
[4]
|
Baumgartner, A., Tausch, C., Hosch, S., et al. (2018) Ultra-sound-Based Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer Patients. Breast, 39, 19-23. https://doi.org/10.1016/j.breast.2018.02.028
|
[5]
|
Ochi, T., Tsunoda, H., Matsuda, N., et al. (2021) Accura-cy of Morphologic Change Measurements by Ultrasound in Predicting Pathological Response to Neoadjuvant Chemo-therapy in Triple-Negative and HER2-Positive Breast Cancer. Breast Cancer, 28, 838-847. https://doi.org/10.1007/s12282-021-01220-5
|
[6]
|
D’Angelo, A., Rinaldi, P., Belli, P., et al. (2019) Usefulness of Automated Breast Volume Scanner (ABVS) for Monitoring Tumor Response to Neoadjuvant Treatment in Breast Cancer Patients: Preliminary Results. European Review for Medical and Pharmacological Sciences, 23, 225-231. https://doi.org/10.26355/eurrev_201901_16768
|
[7]
|
Um, E., Kang, J.W., Lee, S., et al. (2018) Comparing Accu-racy of Mammography and Magnetic Resonance Imaging for Residual Calcified Lesions in Breast Cancer Patients Un-dergoing Neoadjuvant Systemic Therapy. Clinical Breast Cancer, 18, e1087-e1091. https://doi.org/10.1016/j.clbc.2018.03.011
|
[8]
|
de Bazelaire, C., Calmon, R., Chapellier, M., Pluvinage, A., Frija, J. and de Kerviler, E. (2010) Imagerie TDM et IRM de l’angiogenèse tumorale [CT and MRI Imaging in Tumoral Angio-genesis]. Bulletin du Cancer, 97, 79-90.
https://doi.org/10.1684/bdc.2010.0961
|
[9]
|
Leng, X., Huang, G., Zhang, L., Ding, J. and Ma, F. (2020) Changes in Tumor Stem Cell Markers and Epithelial- Mesenchymal Transition Markers in Nonluminal Breast Cancer after Neo-adjuvant Chemotherapy and Their Correlation with Contrast-Enhanced Ultrasound. BioMed Research International, 2020, Article ID: 3869538.
https://doi.org/10.1155/2020/3869538
|
[10]
|
Chang, J.M., Moon, W.K., Cho, N., et al. (2011) Clinical Application of Shear Wave Elastography (SWE) in the Diagnosis of Benign and Malignant Breast Diseases. Breast Cancer Research and Treatment, 129, 89-97.
https://doi.org/10.1007/s10549-011-1627-7
|
[11]
|
Bai, M., Du, L., Gu, J., Li, F. and Jia, X. (2012) Virtual Touch Tissue Quantification Using Acoustic Radiation Force Impulse Technology: Initial Clinical Experience with Solid Breast Masses. Journal of Ultrasound in Medicine, 31, 289-294. https://doi.org/10.7863/jum.2012.31.2.289
|
[12]
|
Denis, M., Bayat, M., Mehrmohammadi, M., et al. (2015) Update on Breast Cancer Detection Using Comb-Push Ultrasound Shear Elastography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 62, 1644-1650.
https://doi.org/10.1109/TUFFC.2015.007043
|
[13]
|
Evans, A., Whelehan, P., Thomson, K., et al. (2010) Quantita-tive Shear Wave Ultrasound Elastography: Initial Experience in Solid Breast Masses. Breast Cancer Research, 12, Arti-cle No. R104. https://doi.org/10.1186/bcr2787
|
[14]
|
Golatta, M., Schweitzer-Martin, M., Harcos, A., et al. (2014) Evaluation of Virtual Touch Tissue Imaging Quantification, a New Shear Wave Velocity Imaging Method, for Breast Le-sion Assessment by Ultrasound. BioMed Research International, 2014, Article ID: 960262. https://doi.org/10.1155/2014/960262
|
[15]
|
Gu, J., Polley, E.C., Denis, M., et al. (2021) Early Assessment of Shear Wave Elastography Parameters Foresees the Response to Neoadjuvant Chemotherapy in Patients with Invasive Breast Cancer. Breast Cancer Research, 23, Article No. 52. https://doi.org/10.1186/s13058-021-01429-4
|
[16]
|
Jiang, M., Li, C.L., Luo, X.M., et al. (2021) Ultrasound-Based Deep Learning Radiomics in the Assessment of Pathological Com-plete Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer. European Journal of Cancer, 147, 95-105. https://doi.org/10.1016/j.ejca.2021.01.028
|
[17]
|
Mazari, F.A.K., Sharma, N., Dodwell, D. and Horgan, K. (2018) Human Epidermal Growth Factor 2-Positive Breast Cancer with Mammographic Microcalcification: Relationship to Pathologic Complete Response after Neoadjuvant Chemotherapy. Radiology, 288, 366-374. https://doi.org/10.1148/radiol.2018170960
|
[18]
|
Xing, D., Mao, N., Dong, J., Ma, H., Chen, Q. and Lyu, Y. (2021) Quantitative Analysis of Contrast Enhanced Spectral Mammography Grey Value for Early Prediction of Pathological Response of Breast Cancer to Neoadjuvant Chemotherapy. Scientific Reports, Article No. 5892. https://doi.org/10.1038/s41598-021-85353-9
|
[19]
|
Li, L., Roth, R., Germaine, P., et al. (2017) Contrast-Enhanced Spectral Mammography (CESM) versus Breast Magnetic Resonance Imaging (MRI): A Retrospective Comparison in 66 Breast Lesions. Diagnostic and Interventional Imaging, 98, 113-123. https://doi.org/10.1016/j.diii.2016.08.013
|
[20]
|
Lewin, J. (2018) Comparison of Contrast-Enhanced Mammography and Contrast-Enhanced Breast MR Imaging. Magnetic Resonance Imaging Clinics of North America, 26, 259-263. https://doi.org/10.1016/j.mric.2017.12.005
|
[21]
|
Abramson, R.G., Li, X., Hoyt, T.L., et al. (2013) Early Assess-ment of Breast Cancer Response to Neoadjuvant Chemotherapy by Semi-Quantitative Analysis of High-Temporal Reso-lution DCE-MRI: Preliminary Results. Magnetic Resonance Imaging, 31, 1457-1464. https://doi.org/10.1016/j.mri.2013.07.002
|
[22]
|
Arlinghaus, L.R., Li, X., Levy, M., et al. (2010) Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy. Journal of Oncology, 2010, Article ID: 919620.
https://doi.org/10.1155/2010/919620
|
[23]
|
Li, X., Abramson, R.G., Arlinghaus, L.R., et al. (2015) Multiparametric Magnetic Resonance Imaging for Predicting Pathological Response after the First Cycle of Neoadjuvant Chemotherapy in Breast Cancer. Investigative Radiology, 50, 195-204. https://doi.org/10.1097/RLI.0000000000000100
|
[24]
|
Wu, L.A., Chang, R.F., Huang, C.S., et al. (2015) Evaluation of the Treatment Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer Using Combined Magnetic Resonance Vascular Maps and Apparent Diffusion Coeffi-cient. Journal of Magnetic Resonance Imaging, 42, 1407-1420. https://doi.org/10.1002/jmri.24915
|
[25]
|
Minarikova, L., Bogner, W., Pinker, K., et al. (2017) Investigating the Prediction Value of Multiparametric Magnetic Resonance Im-aging at 3T in Response to Neoadjuvant Chemotherapy in Breast Cancer. European Radiology, 27, 1901-1911.
https://doi.org/10.1007/s00330-016-4565-2
|
[26]
|
Wei, J., Wang, C., Xie, X. and Jiang, D. (2019) Meta-Analysis of Quantitative Dynamic Contrast-Enhanced MRI for the Assessment of Neoadjuvant Chemotherapy in Breast Cancer. The American Surgeon, 85, 645-653.
https://doi.org/10.1177/000313481908500630
|
[27]
|
Chandramohan, A., Siddiqi, U.M., Mittal, R., et al. (2020) Diffusion Weighted Imaging Improves Diagnostic Ability of MRI for Determining Complete Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer. European Journal of Radiology Open, 7, Article ID: 100223. https://doi.org/10.1016/j.ejro.2020.100223
|
[28]
|
Shangguan, A.J., Sun, C., Wang, B., et al. (2019) DWI and DCE-MRI Approaches for Differentiating Reversibly Electroporated Penumbra from Irreversibly Electroporated Ablation Zones in a Rabbit Liver Model. American Journal of Cancer Research, 9, 1982-1994.
|
[29]
|
Zhang, J., Huang, Y., Chen, J., Wang, X. and Ma, H. (2021) Potential of Combination of DCE-MRI and DWI with Serum CA125 and CA199 in Evaluating Effectiveness of Neoadjuvant Chemotherapy in Breast Cancer. World Journal of Surgical Oncology, 19, Arti-cle No. 284. https://doi.org/10.1186/s12957-021-02398-w
|
[30]
|
Tahmassebi, A., Wengert, G.J., Helbich, T.H., et al. (2019) Impact of Machine Learning with Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients. Investigative Radiology, 54, 110-117.
https://doi.org/10.1097/RLI.0000000000000518
|
[31]
|
Jafri, N.F., Newitt, D.C., Kornak, J., Esserman, L.J., Joe, B.N. and Hylton, N.M. (2014) Optimized Breast MRI Functional Tumor Volume as a Biomarker of Recurrence-Free Survival Following Neoadjuvant Chemotherapy. Journal of Magnetic Resonance Imaging, 40, 476-482. https://doi.org/10.1002/jmri.24351
|
[32]
|
Hylton, N.M., Gatsonis, C.A., Rosen, M.A., et al. (2016) Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-Free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL. Radiology, 279, 44-55. https://doi.org/10.1148/radiol.2015150013
|
[33]
|
Musall, B.C., Abdelhafez, A.H., Adrada, B.E., et al. (2021) Func-tional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer. Journal of Magnetic Resonance Imaging, 54, 251-260. https://doi.org/10.1002/jmri.27557
|
[34]
|
Li, W., Newitt, D.C., Wilmes, L.J., et al. (2019) Additive Value of Diffu-sion-Weighted MRI in the I-SPY 2 TRIAL. Journal of Magnetic Resonance Imaging, 50, 1742-1753. https://doi.org/10.1002/jmri.26770
|
[35]
|
Furman-Haran, E., Nissan, N., Ricart-Selma, V., Martinez-Rubio, C., De-gani, H. and Camps-Herrero, J. (2018) Quantitative Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy by Diffusion Tensor Imaging: Initial Results. Journal of Magnetic Resonance Imaging g, 47, 1080-1090. https://doi.org/10.1002/jmri.25855
|
[36]
|
Hussain, L., Huang, P., Nguyen, T., et al. (2021) Machine Learning Clas-sification of Texture Features of MRI Breast Tumor and Peri-Tumor of Combined Pre- and Early Treatment Predicts Pathologic Complete Response. BioMedical Engineering OnLine, 20, Article No. 63. https://doi.org/10.1186/s12938-021-00899-z
|
[37]
|
Hanahan, D. (2022) Hallmarks of Cancer: New Dimensions. Cancer Discovery, 12, 31-46.
https://doi.org/10.1158/2159-8290.CD-21-1059
|
[38]
|
Groheux, D., Biard, L., Giacchetti, S., et al. (2016) 18F-FDG PET/CT for the Early Evaluation of Response to Neoadjuvant Treatment in Triple-Negative Breast Cancer: Influence of the Chemotherapy Regimen. Journal of Nuclear Medicine, 57, 536-543. https://doi.org/10.2967/jnumed.115.163907
|
[39]
|
Groheux, D., Sanna, A., Majdoub, M., et al. (2015) Baseline Tumor 18F-FDG Uptake and Modifications after 2 Cycles of Neoadjuvant Chemotherapy Are Prognostic of Outcome in ER+/HER2- Breast Cancer. Journal of Nuclear Medicine June, 56, 824-831. https://doi.org/10.2967/jnumed.115.154138
|
[40]
|
Groheux, D., Mankoff, D., Espié, M. and Hindié, E. (2016) 18F-FDG PET/CT in the Early Prediction of Pathological Response in Aggressive Subtypes of Breast Cancer: Review of the Literature and Recommendations for Use in Clinical Trials. European Journal of Nuclear Medicine and Molecular Imaging, 43, 983-993.
https://doi.org/10.1007/s00259-015-3295-z
|
[41]
|
Li, P., Wang, X., Xu, C., et al. (2020) 18F-FDG PET/CT Radio-mic Predictors of Pathologic Complete Response (pCR) to Neoadjuvant Chemotherapy in Breast Cancer Patients. Euro-pean Journal of Nuclear Medicine and Molecular Imaging, 47, 1116-1126. https://doi.org/10.1007/s00259-020-04684-3
|
[42]
|
Chen, L., Yang, Q., Bao, J., Liu, D., Huang, X. and Wang, J. (2017) Direct Comparison of PET/CT and MRI to Predict the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer: A Meta-Analysis. Scientific Reports, 7, Article No. 8479. https://doi.org/10.1038/s41598-017-08852-8
|
[43]
|
Sasada, S., Kimura, Y., Emi, A., et al. (2020) Tumor-Infiltrating Lymphocyte Score Based on FDG PET/CT for Predicting the Effect of Neoadjuvant Chemotherapy in Breast Cancer. An-ticancer Research, 40, 3395-3400.
https://doi.org/10.21873/anticanres.14323
|