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Cited by in F6Publishing
For: Matos J, Paparo F, Mussetto I, Bacigalupo L, Veneziano A, Perugin Bernardi S, Biscaldi E, Melani E, Antonucci G, Cremonesi P, Lattuada M, Pilotto A, Pontali E, Rollandi GA. Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome. Eur Radiol Exp 2020;4:39. [PMID: 32592118 DOI: 10.1186/s41747-020-00167-0] [Cited by in Crossref: 23] [Cited by in F6Publishing: 15] [Article Influence: 11.5] [Reference Citation Analysis]
Number Citing Articles
1 Rasheed J, Jamil A, Hameed AA, Aftab U, Aftab J, Shah SA, Draheim D. A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. Chaos Solitons Fractals 2020;141:110337. [PMID: 33071481 DOI: 10.1016/j.chaos.2020.110337] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 7.5] [Reference Citation Analysis]
2 Gupta YS, Finkelstein M, Manna S, Toussie D, Bernheim A, Little BP, Concepcion J, Maron SZ, Jacobi A, Chung M, Kukar N, Voutsinas N, Cedillo MA, Fernandes A, Eber C, Fayad ZA, Hota P. Coronary artery calcification in COVID-19 patients: an imaging biomarker for adverse clinical outcomes. Clin Imaging 2021;77:1-8. [PMID: 33601125 DOI: 10.1016/j.clinimag.2021.02.016] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Mazzilli A, Fiorino C, Loria A, Mori M, Esposito PG, Palumbo D, de Cobelli F, del Vecchio A. An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients. Applied Sciences 2021;11:1238. [DOI: 10.3390/app11031238] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
4 Pergola V, Cabrelle G, Previtero M, Fiorencis A, Lorenzoni G, Dellino CM, Montonati C, Continisio S, Masetto E, Mele D, Perazzolo Marra M, Giraudo C, Barbiero G, De Conti G, Di Salvo G, Gregori D, Iliceto S, Motta R. Impact of the "atherosclerotic pabulum" on in-hospital mortality for SARS-CoV-2 infection. Is calcium score able to identify at-risk patients? Clin Cardiol 2022. [PMID: 35355295 DOI: 10.1002/clc.23809] [Reference Citation Analysis]
5 Safarchi A, Fatima S, Ayati Z, Vafaee F. An update on novel approaches for diagnosis and treatment of SARS-CoV-2 infection. Cell Biosci 2021;11:164. [PMID: 34420513 DOI: 10.1186/s13578-021-00674-6] [Reference Citation Analysis]
6 Matos J, Paparo F, Mori M, Veneziano A, Sartini M, Cristina ML, Rollandi GA. Contamination inside CT gantry in the SARS-CoV-2 era. Eur Radiol Exp 2020;4:55. [PMID: 33000373 DOI: 10.1186/s41747-020-00182-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
7 Mori M, Palumbo D, De Lorenzo R, Broggi S, Compagnone N, Guazzarotti G, Giorgio Esposito P, Mazzilli A, Steidler S, Pietro Vitali G, Del Vecchio A, Rovere Querini P, De Cobelli F, Fiorino C. Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry. Phys Med 2021;85:63-71. [PMID: 33971530 DOI: 10.1016/j.ejmp.2021.04.022] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
8 Dheir H, Karacan A, Sipahi S, Yaylaci S, Tocoglu A, Demirci T, Cetin ES, Guneysu F, Firat N, Varim C, Karabay O. Correlation between venous blood gas indices and radiological involvements of COVID-19 patients at first admission to emergency department. Rev Assoc Med Bras (1992) 2021;67Suppl 1:51-6. [PMID: 34406295 DOI: 10.1590/1806-9282.67.Suppl1.20200715] [Reference Citation Analysis]
9 Näppi JJ, Uemura T, Watari C, Hironaka T, Kamiya T, Yoshida H. U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19. Sci Rep 2021;11:9263. [PMID: 33927287 DOI: 10.1038/s41598-021-88591-z] [Reference Citation Analysis]
10 Yurdaisik I, Nurili F, Agirman AG, Aksoy SH. The relationship between lesion density change in chest computed tomography and clinical improvement in COVID-19 patients. Int J Clin Pract 2021;75:e14355. [PMID: 33974359 DOI: 10.1111/ijcp.14355] [Reference Citation Analysis]
11 Chardoli M, Sabbaghan Kermani S, Abdollahzade Manqoutaei S, Loesche MA, Duggan NM, Schulwolf S, Tofighi R, Yadegari S, Shokoohi H. Lung ultrasound in predicting COVID-19 clinical outcomes: A prospective observational study. J Am Coll Emerg Physicians Open 2021;2:e12575. [PMID: 34755148 DOI: 10.1002/emp2.12575] [Reference Citation Analysis]
12 Uemura T, Näppi JJ, Watari C, Hironaka T, Kamiya T, Yoshida H. Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT. Med Image Anal 2021;73:102159. [PMID: 34303892 DOI: 10.1016/j.media.2021.102159] [Reference Citation Analysis]
13 Zhang T, Li X, Ji X, Lu J, Fang X, Bian Y. Generalized additive mixed model to evaluate the association between total pulmonary infection volume and volume ratio, and clinical types, in patients with COVID-19 pneumonia: a propensity score analysis. Eur Radiol 2021. [PMID: 33855587 DOI: 10.1007/s00330-021-07860-7] [Reference Citation Analysis]
14 Arru C, Ebrahimian S, Falaschi Z, Hansen JV, Pasche A, Lyhne MD, Zimmermann M, Durlak F, Mitschke M, Carriero A, Nielsen-Kudsk JE, Kalra MK, Saba L. Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia. Clin Imaging 2021;80:58-66. [PMID: 34246044 DOI: 10.1016/j.clinimag.2021.06.036] [Reference Citation Analysis]
15 Homayounieh F, Bezerra Cavalcanti Rockenbach MA, Ebrahimian S, Doda Khera R, Bizzo BC, Buch V, Babaei R, Karimi Mobin H, Mohseni I, Mitschke M, Zimmermann M, Durlak F, Rauch F, Digumarthy SR, Kalra MK. Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome. J Digit Imaging 2021;34:320-9. [PMID: 33634416 DOI: 10.1007/s10278-021-00430-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
16 Monterde D, Carot-Sans G, Cainzos-Achirica M, Abilleira S, Coca M, Vela E, Clèries M, Valero-Bover D, Comin-Colet J, García-Eroles L, Pérez-Sust P, Arrufat M, Lejardi Y, Piera-Jiménez J. Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients. Risk Manag Healthc Policy 2021;14:4729-37. [PMID: 34849041 DOI: 10.2147/RMHP.S326132] [Reference Citation Analysis]
17 Avanzo M, Porzio M, Lorenzon L, Milan L, Sghedoni R, Russo G, Massafra R, Fanizzi A, Barucci A, Ardu V, Branchini M, Giannelli M, Gallio E, Cilla S, Tangaro S, Lombardi A, Pirrone G, De Martin E, Giuliano A, Belmonte G, Russo S, Rampado O, Mettivier G. Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Physica Medica 2021;83:221-41. [DOI: 10.1016/j.ejmp.2021.04.010] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
18 Planek MIC, Ruge M, Du Fay de Lavallaz JM, Kyung SB, Gomez JMD, Suboc TM, Williams KA, Volgman AS, Simmons JA, Rao AK. Cardiovascular findings on chest computed tomography associated with COVID-19 adverse clinical outcomes. Am Heart J Plus 2021;11:100052. [PMID: 34667971 DOI: 10.1016/j.ahjo.2021.100052] [Reference Citation Analysis]
19 Palumbo P, Palumbo MM, Bruno F, Picchi G, Iacopino A, Acanfora C, Sgalambro F, Arrigoni F, Ciccullo A, Cosimini B, Splendiani A, Barile A, Masedu F, Grimaldi A, Di Cesare E, Masciocchi C. Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease. Diagnostics (Basel) 2021;11:2125. [PMID: 34829472 DOI: 10.3390/diagnostics11112125] [Reference Citation Analysis]
20 Khamis AH, Jaber M, Azar A, AlQahtani F, Bishawi K, Shanably A. Clinical and laboratory findings of COVID-19: A systematic review and meta-analysis. J Formos Med Assoc 2021;120:1706-18. [PMID: 33376008 DOI: 10.1016/j.jfma.2020.12.003] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]