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EISSN 1305-3612

IMPACT FACTOR: 3.346 5 YEAR IF: 3.132 SCOPUS CITESCORE: 3.9

Diagnostic and Interventional Radiology

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Future scenarios and opportunities for interventional radiology in the post COVID-19 era

Maria Abadal, Jose; Gonzalez-Nieto, Jimena; Lopez-Zarraga, Fernando; et al.

A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019‎

Ito, Rintaro; Iwano, Shingo; Naganawa, Shinji

Technical background of a novel detector-based approach energy computed tomography‎

Hokamp, Nils Grosse; Maintz, David; Shapira, Nadav; et al.

Radiomics-based nomogram using CT imaging for noninvasive preoperative prediction of early recurrence in patients with hepatocellular carcinoma‎

Zhu, Hong-Bo; Zheng, Ze-Yu; Zhao, Heng; et al.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors‎

Kavur, A. Emre; Gezer, Naciye Sinem; Baris, Mustafa; et al.

Ethical considerations for artificial intelligence: an overview of the current radiology landscape

Tugba Akinci D'Antonoli

HIGHLY CITED ARTICLES

Radiological approach to COVID-19 pneumonia with an emphasis on chest CT

By: Guneyli, S; Atceken, Z; Dogan, H; et al.

Interventional radiology and COVID-19: evidence-based measures to limit transmission

By: Chandy, PE; Nasir, MU; Srinivasan, S; et al.

Technical background of a novel detector-based approach energy computed tomography

By: Hokamp, NG; Maintz, D; Shapira, N; et al.

How imaging should properly be used in COVID-19 outbreak: an Italian experience

By: Sverzellati, N; Milone, F and Balbi, M

Management strategy of novel coronavirus (COVID-19) pneumonia in the radiology department: a Chinese experience

By: An, P; Ye, YJ; Chen, YT; et al.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors

By: Kavur, AE; Gezer, NS; Baris, M; et al.

A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019

By: Ito, R; Iwano, S and Naganawa, S

COVID-19 S: A new proposal for diagnosis and structured reporting of COVID-19 on computed tomography imaging

By: Gezer, NS; Ergan, B; Baris, MM; et al.

CT findings of COVID-19 in follow-up: comparison between progression and recovery

By: Guan, CS; Wei, LG; Xie, RM; et al.

Rare CT feature in a COVID-19 patient: cavitation

By: Xu, ZF; Pan, AZ and Zhou, HL

IMAGES FROM THE LAST ISSUE

COVID-19 ARTICLES

COVID-19 radiology CT personnel management

By: Özdemir et al.

Future of IR in the post COVID-19 era

By: Abadal et al.

CT findings to predict hospitalization and intensive care need for COVID-19

By: Tekcan Sanli et al.

Application of percutaneous transluminal sharp recanalization in TIPS

By: Li et al.

Comparison of laboratory-confirmed and clinically diagnosed COVID-19 patients

By: Kuzan et al.

Low-dose chest CT to diagnose COVID-19

By: Aslan et al.

Quantitative chest CT in COVID-19

By: Ufuk et al.

Vascular changes in COVID-19 pneumonia

By: Tekcan Şanlı and Yıldırım.

Use of deep learning in chest X-ray images of COVID-19 patients

By: Blain et al.

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Artificial intelligence at the intersection of pathology and radiology in prostate cancer

Stephanie A. Harmon, Sena Tuncer , Thomas Sanford , Peter L. Choyke , Barış Türkbey

(Diagn Interv Radiol 2019; 25: 183-188) DOI: 10.5152/dir.2019.19125

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Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images

Maxime Blain, Michael T. Kassin, Nicole Varble , Xiaosong Wang , Ziyue Xu , Daguang Xu , Gianpaolo Carrafiello , Valentina Vespro , Elvira Stellato , Anna Maria Ierardi , Letizia Di Meglio , Robert D. Suh , Stephanie A. Walker , Sheng Xu , Thomas H. Sanford , Evrim B. Turkbey , Stephanie Harmon , Baris Turkbey , Bradford J. Wood

(Diagn Interv Radiol 2021; 27: 20-27) DOI: 10.5152/dir.2020.20205

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