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Cited by in F6Publishing
For: Zhuang X, Chen C, Liu Z, Zhang L, Zhou X, Cheng M, Ji F, Zhu T, Lei C, Zhang J, Jiang J, Tian J, Wang K. Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy. Transl Oncol 2020;13:100831. [PMID: 32759037 DOI: 10.1016/j.tranon.2020.100831] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Kong X, Zhang Q, Wu X, Zou T, Duan J, Song S, Nie J, Tao C, Tang M, Wang M, Zou J, Xie Y, Li Z, Li Z. Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer. Front Oncol 2022;12:816297. [DOI: 10.3389/fonc.2022.816297] [Reference Citation Analysis]
2 Pesapane F, Rotili A, Botta F, Raimondi S, Bianchini L, Corso F, Ferrari F, Penco S, Nicosia L, Bozzini A, Pizzamiglio M, Origgi D, Cremonesi M, Cassano E. Radiomics of MRI for the Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Single Referral Centre Analysis. Cancers (Basel) 2021;13:4271. [PMID: 34503081 DOI: 10.3390/cancers13174271] [Reference Citation Analysis]
3 Li X, Yan C, Xiao J, Xu X, Li Y, Wen X, Wei H. Factors Associated With Surgical Modality Following Neoadjuvant Chemotherapy in Patients with Breast Cancer. Clin Breast Cancer 2021:S1526-8209(21)00066-5. [PMID: 34001440 DOI: 10.1016/j.clbc.2021.03.011] [Reference Citation Analysis]
4 Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Reference Citation Analysis]
5 Gu J, Tong T, He C, Xu M, Yang X, Tian J, Jiang T, Wang K. Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study. Eur Radiol 2021. [PMID: 34654965 DOI: 10.1007/s00330-021-08293-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Huang Y, Chen W, Zhang X, He S, Shao N, Shi H, Lin Z, Wu X, Li T, Lin H, Lin Y. Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer. Front Bioeng Biotechnol 2021;9:662749. [PMID: 34295877 DOI: 10.3389/fbioe.2021.662749] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Kim SY, Cho N, Choi Y, Lee SH, Ha SM, Kim ES, Chang JM, Moon WK. Factors Affecting Pathologic Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer: Development and Validation of a Predictive Nomogram. Radiology 2021;299:290-300. [PMID: 33754824 DOI: 10.1148/radiol.2021203871] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
8 Pesapane F, Rotili A, Agazzi GM, Botta F, Raimondi S, Penco S, Dominelli V, Cremonesi M, Jereczek-Fossa BA, Carrafiello G, Cassano E. Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future. Curr Oncol 2021;28:2351-72. [PMID: 34202321 DOI: 10.3390/curroncol28040217] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]