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Updated prostate imaging reporting and data system (PIRADS v2) recommendations for the detection of clinically significant prostate cancer using multiparametric MRI: critical evaluation using whole-mount pathology as standard of reference
Updated prostate imaging reporting and data system (PIRADS v2) recommendations for the detection of clinically significant prostate cancer using multiparametric MRI: critical evaluation using whole-mount pathology as standard of reference
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Updated prostate imaging reporting and data system (PIRADS v2) recommendations for the detection of clinically significant prostate cancer using multiparametric MRI: critical evaluation using whole-mount pathology as standard of reference

Vargas, H. A. ; Hötker, A. M. ; Goldman, D. A. ; Moskowitz, C. S. ; Gondo, T. ; Matsumoto, K. ; Ehdaie, B. ; Woo, S. ; Fine, S. W. ; Reuter, V. E. ; Sala, E. ; Hricak, H.

European radiology, 2016-06, Vol.26 (6), p.1606-1612 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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2
Nephrogenic systemic fibrosis and gadolinium-based contrast media: updated ESUR Contrast Medium Safety Committee guidelines
Nephrogenic systemic fibrosis and gadolinium-based contrast media: updated ESUR Contrast Medium Safety Committee guidelines
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Nephrogenic systemic fibrosis and gadolinium-based contrast media: updated ESUR Contrast Medium Safety Committee guidelines

Thomsen, Henrik S. ; Morcos, Sameh K. ; Almén, Torsten ; Bellin, Marie-France ; Bertolotto, Michele ; Bongartz, Georg ; Clement, Olivier ; Leander, Peter ; Heinz-Peer, Gertraud ; Reimer, Peter ; Stacul, Fulvio ; van der Molen, Aart ; Webb, Judith AW

European radiology, 2013-02, Vol.23 (2), p.307-318 [Periódico revisado por pares]

Berlin/Heidelberg: Springer-Verlag

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3
Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer
Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer
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Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

Wang, Jing ; Wu, Chen-Jiang ; Bao, Mei-Ling ; Zhang, Jing ; Wang, Xiao-Ning ; Zhang, Yu-Dong

European radiology, 2017-10, Vol.27 (10), p.4082-4090 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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4
Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmö Breast Tomosynthesis Screening Trial, a population-based study
Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmö Breast Tomosynthesis Screening Trial, a population-based study
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Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmö Breast Tomosynthesis Screening Trial, a population-based study

Lång, Kristina ; Andersson, Ingvar ; Rosso, Aldana ; Tingberg, Anders ; Timberg, Pontus ; Zackrisson, Sophia

European radiology, 2016-01, Vol.26 (1), p.184-190 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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5
Diagnostic accuracy of prospective application of the Liver Imaging Reporting and Data System (LI-RADS) in gadoxetate-enhanced MRI
Diagnostic accuracy of prospective application of the Liver Imaging Reporting and Data System (LI-RADS) in gadoxetate-enhanced MRI
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Diagnostic accuracy of prospective application of the Liver Imaging Reporting and Data System (LI-RADS) in gadoxetate-enhanced MRI

Kim, Yeun-Yoon ; An, Chansik ; Kim, Sungwon ; Kim, Myeong-Jin

European radiology, 2018-05, Vol.28 (5), p.2038-2046 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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6
Radiographic severity index in COVID-19 pneumonia: relationship to age and sex in 783 Italian patients
Radiographic severity index in COVID-19 pneumonia: relationship to age and sex in 783 Italian patients
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Radiographic severity index in COVID-19 pneumonia: relationship to age and sex in 783 Italian patients

Borghesi, Andrea ; Zigliani, Angelo ; Masciullo, Roberto ; Golemi, Salvatore ; Maculotti, Patrizia ; Farina, Davide ; Maroldi, Roberto

Radiologia medica, 2020-05, Vol.125 (5), p.461-464 [Periódico revisado por pares]

Milan: Springer Milan

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7
COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression
COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression
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COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression

Borghesi, Andrea ; Maroldi, Roberto

Radiologia medica, 2020-05, Vol.125 (5), p.509-513 [Periódico revisado por pares]

Milan: Springer Milan

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8
Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
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Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

Akagi, Motonori ; Nakamura, Yuko ; Higaki, Toru ; Narita, Keigo ; Honda, Yukiko ; Zhou, Jian ; Yu, Zhou ; Akino, Naruomi ; Awai, Kazuo

European radiology, 2019-11, Vol.29 (11), p.6163-6171 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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9
Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network
Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network
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Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network

Ye, Hai ; Gao, Feng ; Yin, Youbing ; Guo, Danfeng ; Zhao, Pengfei ; Lu, Yi ; Wang, Xin ; Bai, Junjie ; Cao, Kunlin ; Song, Qi ; Zhang, Heye ; Chen, Wei ; Guo, Xuejun ; Xia, Jun

European radiology, 2019-11, Vol.29 (11), p.6191-6201 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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10
Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI
Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI
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Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI

Hamm, Charlie A. ; Wang, Clinton J. ; Savic, Lynn J. ; Ferrante, Marc ; Schobert, Isabel ; Schlachter, Todd ; Lin, MingDe ; Duncan, James S. ; Weinreb, Jeffrey C. ; Chapiro, Julius ; Letzen, Brian

European radiology, 2019-07, Vol.29 (7), p.3338-3347 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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