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For: Bitencourt A, Daimiel Naranjo I, Lo Gullo R, Rossi Saccarelli C, Pinker K. AI-enhanced breast imaging: Where are we and where are we heading? Eur J Radiol 2021;142:109882. [PMID: 34392105 DOI: 10.1016/j.ejrad.2021.109882] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
Number Citing Articles
1 Tan XJ, Cheor WL, Lim LL, Ab Rahman KS, Bakrin IH. Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022;12. [PMID: 36553119 DOI: 10.3390/diagnostics12123111] [Reference Citation Analysis]
2 Karthiga R, Narasimhan K, Amirtharajan R. Diagnosis of breast cancer for modern mammography using artificial intelligence. Mathematics and Computers in Simulation 2022;202:316-30. [DOI: 10.1016/j.matcom.2022.05.038] [Reference Citation Analysis]
3 Akudjedu TN, Torre S, Khine R, Katsifarakis D, Newman D, Malamateniou C. Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey. Journal of Medical Imaging and Radiation Sciences 2022. [DOI: 10.1016/j.jmir.2022.11.016] [Reference Citation Analysis]
4 Kurz FT, Schlemmer HP. Imaging in translational cancer research. Cancer Biol Med 2022;19:1565-85. [PMID: 36476372 DOI: 10.20892/j.issn.2095-3941.2022.0677] [Reference Citation Analysis]
5 Pandiyan S, Wang L. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput Biol Med 2022;150:106140. [PMID: 36179510 DOI: 10.1016/j.compbiomed.2022.106140] [Reference Citation Analysis]
6 Hooley RJ, Durand MA. Equalizing the Performance of Radiologists and Physician Extenders in Screening Mammography. Radiology 2022;:221960. [PMID: 36098645 DOI: 10.1148/radiol.221960] [Reference Citation Analysis]
7 Mahant SS, Varma AR. Artificial Intelligence in Breast Ultrasound: The Emerging Future of Modern Medicine. Cureus 2022. [DOI: 10.7759/cureus.28945] [Reference Citation Analysis]
8 Yue W, Zhang H, Zhou J, Li G, Tang Z, Sun Z, Cai J, Tian N, Gao S, Dong J, Liu Y, Bai X, Sheng F. Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging. Front Oncol 2022;12:984626. [DOI: 10.3389/fonc.2022.984626] [Reference Citation Analysis]
9 Jimenez JE, Abdelhafez A, Mittendorf EA, Elshafeey N, Yung JP, Litton JK, Adrada BE, Candelaria RP, White J, Thompson AM, Huo L, Wei P, Tripathy D, Valero V, Yam C, Hazle JD, Moulder SL, Yang WT, Rauch GM. A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer. European Journal of Radiology 2022;149:110220. [DOI: 10.1016/j.ejrad.2022.110220] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
10 Bitencourt A, Iima M, Langs G, Pinker K. Editorial: Impact of Breast MRI on Breast Cancer Treatment and Prognosis. Front Oncol 2022;12:825101. [DOI: 10.3389/fonc.2022.825101] [Reference Citation Analysis]
11 Interlenghi M, Salvatore C, Magni V, Caldara G, Schiavon E, Cozzi A, Schiaffino S, Carbonaro LA, Castiglioni I, Sardanelli F. A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses. Diagnostics 2022;12:187. [DOI: 10.3390/diagnostics12010187] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Sidebottom R, Lyburn I, Vinnicombe S. Artificial intelligence (AI) in Mammography. Digital Mammography 2022. [DOI: 10.1007/978-3-031-10898-3_19] [Reference Citation Analysis]
13 Cè M, Caloro E, Pellegrino ME, Basile M, Sorce A, Fazzini D, Oliva G, Cellina M. Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review. Explor Target Antitumor Ther 2022;3:795-816. [PMID: 36654817 DOI: 10.37349/etat.2022.00113] [Reference Citation Analysis]
14 Interlenghi M, Salvatore C, Magni V, Caldara G, Schiavon E, Cozzi A, Schiaffino S, Carbonaro LA, Castiglioni I, Sardanelli F. A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses.. [DOI: 10.1101/2021.12.16.21267907] [Reference Citation Analysis]
15 Davey MG, Davey MS, Boland MR, Ryan ÉJ, Lowery AJ, Kerin MJ. Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis. Eur J Radiol 2021;144:109996. [PMID: 34624649 DOI: 10.1016/j.ejrad.2021.109996] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]