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1
Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis
Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis
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Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis

European radiology, 2020-12, Vol.30 (12), p.6808-6817 [Peer Reviewed Journal]

European Society of Radiology 2020 ;European Society of Radiology 2020. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-020-07033-y ;PMID: 32623505

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2
CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)
CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)
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CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)

European radiology, 2020-08, Vol.30 (8), p.4407-4416 [Peer Reviewed Journal]

European Society of Radiology 2020 ;European Society of Radiology 2020. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-020-06817-6 ;PMID: 32215691

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3
Complication rates of CT-guided transthoracic lung biopsy: meta-analysis
Complication rates of CT-guided transthoracic lung biopsy: meta-analysis
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Complication rates of CT-guided transthoracic lung biopsy: meta-analysis

European radiology, 2017-01, Vol.27 (1), p.138-148 [Peer Reviewed Journal]

The Author(s) 2016 ;European Radiology is a copyright of Springer, 2017. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-016-4357-8 ;PMID: 27108299

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4
Coronavirus disease 2019: initial chest CT findings
Coronavirus disease 2019: initial chest CT findings
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Coronavirus disease 2019: initial chest CT findings

European radiology, 2020-08, Vol.30 (8), p.4398-4406 [Peer Reviewed Journal]

European Society of Radiology 2020 ;European Society of Radiology 2020. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-020-06816-7 ;PMID: 32211963

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5
Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores
Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores
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Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores

European radiology, 2015-10, Vol.25 (10), p.2840-2850 [Peer Reviewed Journal]

European Society of Radiology 2015 ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-015-3701-8 ;PMID: 25991476

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6
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

European radiology, 2016-06, Vol.26 (6), p.1606-1612 [Peer Reviewed Journal]

European Society of Radiology 2015 ;European Society of Radiology 2016 ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-015-4015-6 ;PMID: 26396111

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7
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

European radiology, 2019-11, Vol.29 (11), p.6163-6171 [Peer Reviewed Journal]

European Society of Radiology 2019. corrected publication 2019 ;European Radiology is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-019-06170-3 ;PMID: 30976831

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8
Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study
Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study
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Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study

European radiology, 2022-03, Vol.32 (3), p.2099-2109 [Peer Reviewed Journal]

European Society of Radiology 2021 ;2021. European Society of Radiology. ;European Society of Radiology 2021. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-021-08293-y ;PMID: 34654965

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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

European radiology, 2019-11, Vol.29 (11), p.6191-6201 [Peer Reviewed Journal]

The Author(s) 2019 ;European Radiology is a copyright of Springer, (2019). All Rights Reserved. © 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-019-06163-2 ;PMID: 31041565

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10
Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning
Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning
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Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning

European radiology, 2022-01, Vol.32 (1), p.702-713 [Peer Reviewed Journal]

European Society of Radiology 2021 ;2021. European Society of Radiology. ;European Society of Radiology 2021. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-021-08126-y ;PMID: 34255160

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11
Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform
Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform
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Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform

European radiology, 2020-11, Vol.30 (11), p.6241-6250 [Peer Reviewed Journal]

The Author(s) 2020 ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-020-06957-9 ;PMID: 32483644

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12
Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT
Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT
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Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT

European radiology, 2019-07, Vol.29 (7), p.3595-3605 [Peer Reviewed Journal]

European Society of Radiology 2019 ;European Radiology is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-018-5985-y ;PMID: 30770969

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13
Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)
Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)
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Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)

European radiology, 2022-11, Vol.32 (11), p.7998-8007 [Peer Reviewed Journal]

The Author(s) 2022. corrected publication 2022 ;2022. The Author(s). ;The Author(s) 2022, corrected publication 2022 ;ISSN: 1432-1084 ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-022-08784-6 ;PMID: 35420305

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14
CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city
CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city
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CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city

European radiology, 2020-08, Vol.30 (8), p.4417-4426 [Peer Reviewed Journal]

European Society of Radiology 2020 ;European Society of Radiology 2020. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-020-06854-1 ;PMID: 32279115

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15
Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
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Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

European radiology, 2019-03, Vol.29 (3), p.1211-1220 [Peer Reviewed Journal]

European Society of Radiology 2018 ;European Radiology is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-018-5683-9 ;PMID: 30128616

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16
Association of “initial CT” findings with mortality in older patients with coronavirus disease 2019 (COVID-19)
Association of “initial CT” findings with mortality in older patients with coronavirus disease 2019 (COVID-19)
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Association of “initial CT” findings with mortality in older patients with coronavirus disease 2019 (COVID-19)

European radiology, 2020-11, Vol.30 (11), p.6186-6193 [Peer Reviewed Journal]

European Society of Radiology 2020 ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-020-06969-5 ;PMID: 32524220

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17
Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging
Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging
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Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging

European radiology, 2020-01, Vol.30 (1), p.413-424 [Peer Reviewed Journal]

The Author(s) 2019 ;European Radiology is a copyright of Springer, (2019). All Rights Reserved. © 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-019-06318-1 ;PMID: 31332558

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18
Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging
Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging
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Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging

European radiology, 2019-08, Vol.29 (8), p.4068-4076 [Peer Reviewed Journal]

European Society of Radiology 2018 ;European Radiology is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-018-5830-3 ;PMID: 30443758

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19
Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma
Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma
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Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma

European radiology, 2018-04, Vol.28 (4), p.1625-1633 [Peer Reviewed Journal]

European Society of Radiology 2017 ;European Radiology is a copyright of Springer, (2017). All Rights Reserved. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-017-5118-z ;PMID: 29134348

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20
Patients undergoing recurrent CT scans: assessing the magnitude
Patients undergoing recurrent CT scans: assessing the magnitude
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Patients undergoing recurrent CT scans: assessing the magnitude

European radiology, 2020-04, Vol.30 (4), p.1828-1836 [Peer Reviewed Journal]

European Society of Radiology 2019 ;European Radiology is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-019-06523-y ;PMID: 31792585

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